please work

Dockerfile

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+# Base image with Python and necessary tools
+FROM python:3.12
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the application files to the container
+COPY . /app
+
+                                                                                        
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port for the web server
+EXPOSE 7860
+
+# Command to run the application
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

animate.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b85742cd6d924ac74dcc8be48215c75fba8536335e4946af53336ff689199917
+size 1580600

app.py

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+from flask import Flask, request, jsonify
+import base64
+import io
+from PIL import Image
+from flask_cors import CORS
+from transformers import pipeline
+
+# Initialize Flask app
+app = Flask(__name__)
+CORS(app)
+
+# Load the Hugging Face pipeline
+pipe = pipeline("image-classification", model="trpakov/vit-face-expression")
+
+# Emotion to emoji mapping
+emotion_to_emoji = {
+    "angry": "\ud83d\ude20",
+    "disgust": "\ud83e\udd2e",
+    "fear": "\ud83d\ude28",
+    "happy": "\ud83d\ude0a",
+    "sad": "\ud83d\ude22",
+    "surprise": "\ud83d\ude32",
+    "neutral": "\ud83d\ude10"
+}
+
+def preprocess_image(image_data):
+    try:
+        # Decode base64 string to bytes
+        image_bytes = base64.b64decode(image_data)
+
+        # Open the image
+        img = Image.open(io.BytesIO(image_bytes))
+
+        # Convert to RGB if needed
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+
+        # Save grayscale version for response
+        img_gray = img.convert('L')
+        buffered = io.BytesIO()
+        img_gray.save(buffered, format="PNG")
+        grayscale_image_base64 = base64.b64encode(buffered.getvalue()).decode()
+
+        return {
+            "image": img,
+            "grayscale_base64": grayscale_image_base64
+        }
+    except Exception as e:
+        print("Error in preprocess_image:", str(e))
+        raise
+
+@app.route('/', methods=['GET'])  # Fixed keyword 'methods'
+def index():
+    return app.send_static_file('index.html')
+
+@app.route('/upload', methods=['POST'])
+def upload_image():
+    try:
+        data = request.get_json()
+        if not data or 'image' not in data:
+            return jsonify({'error': 'No image data provided'}), 400
+
+        # Preprocess image and get steps
+        preprocessing_results = preprocess_image(data['image'])
+        img = preprocessing_results["image"]
+
+        # Run inference
+        predictions = pipe(img)
+        top_prediction = predictions[0]
+
+        emotion = top_prediction['label'].lower()
+        emoji = emotion_to_emoji.get(emotion, "\ud83e\udd14")
+
+        # Get probabilities for all emotions
+        prob_dict = {pred['label'].lower(): float(pred['score']) for pred in predictions}
+
+        # Detailed process steps
+        process_steps = {
+            "image_acquisition": [
+                "Webcam capture using getUserMedia API",
+                "Canvas API used for image capture",
+                "Base64 encoding for data transfer",
+                "CORS-enabled secure transmission"
+            ],
+            "preprocessing": [
+                "Base64 decoding to binary data",
+                "PIL Image processing pipeline",
+                "RGB format conversion",
+                "Grayscale conversion for visualization",
+                "Image resizing and normalization"
+            ],
+            "model_pipeline": [
+                "Hugging Face Transformers pipeline",
+                "ViT-based image classification",
+                "Multi-head self-attention mechanism",
+                "Feature extraction from image patches",
+                "Emotion classification head"
+            ],
+            "classification": [
+                "7-class emotion detection",
+                "Softmax probability distribution",
+                "Confidence score calculation",
+                "Emoji mapping for visualization",
+                "Real-time result generation"
+            ]
+        }
+
+        response_data = {
+            "emotion": emotion,
+            "emoji": emoji,
+            "grayscale_image": f"data:image/png;base64,{preprocessing_results['grayscale_base64']}",
+            "model_probabilities": prob_dict,
+            "processing_steps": {
+                "original_size": img.size,
+                "color_mode": img.mode,
+                "detailed_steps": process_steps,
+                "model_type": "Vision Transformer (ViT)",
+                "input_shape": "224x224x3",
+                "output_classes": "7 emotions (angry, disgust, fear, happy, sad, surprise, neutral)"
+            }
+        }
+        
+        print("Response data:", response_data)  # Debug print
+        return jsonify(response_data)
+
+    except Exception as e:  
+        print(f"Error processing image: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

facedec.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "WARNING:absl:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 41\u001b[0m\n\u001b[0;32m     38\u001b[0m face_image \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([face_image])\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# Predict emotion using the loaded model\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_best\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mface_image\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     42\u001b[0m emotion_label \u001b[38;5;241m=\u001b[39m class_names[np\u001b[38;5;241m.\u001b[39margmax(predictions)]\n\u001b[0;32m     44\u001b[0m \u001b[38;5;66;03m# Display the emotion label on the frame\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:448\u001b[0m, in \u001b[0;36mTensorFlowTrainer.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[0;32m    443\u001b[0m \u001b[38;5;129m@traceback_utils\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_traceback\n\u001b[0;32m    444\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\n\u001b[0;32m    445\u001b[0m     \u001b[38;5;28mself\u001b[39m, x, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m, steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, callbacks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    446\u001b[0m ):\n\u001b[0;32m    447\u001b[0m     \u001b[38;5;66;03m# Create an iterator that yields batches of input data.\u001b[39;00m\n\u001b[1;32m--> 448\u001b[0m     epoch_iterator \u001b[38;5;241m=\u001b[39m \u001b[43mTFEpochIterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    449\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    450\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    451\u001b[0m \u001b[43m        \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    453\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdistribute_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistribute_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    454\u001b[0m \u001b[43m        \u001b[49m\u001b[43msteps_per_execution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msteps_per_execution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    455\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    457\u001b[0m     \u001b[38;5;66;03m# Container that configures and calls callbacks.\u001b[39;00m\n\u001b[0;32m    458\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(callbacks, callbacks_module\u001b[38;5;241m.\u001b[39mCallbackList):\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:666\u001b[0m, in \u001b[0;36mTFEpochIterator.__init__\u001b[1;34m(self, distribute_strategy, *args, **kwargs)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    665\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribute_strategy \u001b[38;5;241m=\u001b[39m distribute_strategy\n\u001b[1;32m--> 666\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dataset, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedDataset):\n\u001b[0;32m    668\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribute_strategy\u001b[38;5;241m.\u001b[39mexperimental_distribute_dataset(\n\u001b[0;32m    669\u001b[0m         dataset\n\u001b[0;32m    670\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:675\u001b[0m, in \u001b[0;36mTFEpochIterator._get_iterator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_iterator\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 675\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_adapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tf_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\array_data_adapter.py:232\u001b[0m, in \u001b[0;36mArrayDataAdapter.get_tf_dataset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    229\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mwith_options(options)\n\u001b[0;32m    230\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n\u001b[1;32m--> 232\u001b[0m indices_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mindices_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_batch_indices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shuffle \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    234\u001b[0m     indices_dataset \u001b[38;5;241m=\u001b[39m indices_dataset\u001b[38;5;241m.\u001b[39mmap(tf\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mshuffle)\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py:2389\u001b[0m, in \u001b[0;36mDatasetV2.flat_map\u001b[1;34m(self, map_func, name)\u001b[0m\n\u001b[0;32m   2385\u001b[0m \u001b[38;5;66;03m# Loaded lazily due to a circular dependency (dataset_ops -> flat_map_op ->\u001b[39;00m\n\u001b[0;32m   2386\u001b[0m \u001b[38;5;66;03m# dataset_ops).\u001b[39;00m\n\u001b[0;32m   2387\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top,protected-access\u001b[39;00m\n\u001b[0;32m   2388\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m flat_map_op\n\u001b[1;32m-> 2389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mflat_map_op\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_map\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\flat_map_op.py:24\u001b[0m, in \u001b[0;36m_flat_map\u001b[1;34m(input_dataset, map_func, name)\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_flat_map\u001b[39m(input_dataset, map_func, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# pylint: disable=unused-private-name\u001b[39;00m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"See `Dataset.flat_map()` for details.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 24\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_FlatMapDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\flat_map_op.py:33\u001b[0m, in \u001b[0;36m_FlatMapDataset.__init__\u001b[1;34m(self, input_dataset, map_func, name)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_dataset, map_func, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m     32\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_dataset \u001b[38;5;241m=\u001b[39m input_dataset\n\u001b[1;32m---> 33\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func \u001b[38;5;241m=\u001b[39m \u001b[43mstructured_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mStructuredFunctionWrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     34\u001b[0m \u001b[43m      \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transformation_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_dataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     35\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func\u001b[38;5;241m.\u001b[39moutput_structure, dataset_ops\u001b[38;5;241m.\u001b[39mDatasetSpec):\n\u001b[0;32m     36\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `map_func` argument must return a `Dataset` object. Got \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     38\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset_ops\u001b[38;5;241m.\u001b[39mget_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func\u001b[38;5;241m.\u001b[39moutput_structure)\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:265\u001b[0m, in \u001b[0;36mStructuredFunctionWrapper.__init__\u001b[1;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[0;32m    258\u001b[0m       warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    259\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEven though the `tf.config.experimental_run_functions_eagerly` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    260\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moption is set, this option does not apply to tf.data functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    261\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo force eager execution of tf.data functions, please use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`tf.data.experimental.enable_debug_mode()`.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    263\u001b[0m     fn_factory \u001b[38;5;241m=\u001b[39m trace_tf_function(defun_kwargs)\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function \u001b[38;5;241m=\u001b[39m \u001b[43mfn_factory\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;66;03m# There is no graph to add in eager mode.\u001b[39;00m\n\u001b[0;32m    267\u001b[0m add_to_graph \u001b[38;5;241m&\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecuting_eagerly()\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:1251\u001b[0m, in \u001b[0;36mFunction.get_concrete_function\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1249\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_concrete_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1250\u001b[0m   \u001b[38;5;66;03m# Implements PolymorphicFunction.get_concrete_function.\u001b[39;00m\n\u001b[1;32m-> 1251\u001b[0m   concrete \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_concrete_function_garbage_collected\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1252\u001b[0m   concrete\u001b[38;5;241m.\u001b[39m_garbage_collector\u001b[38;5;241m.\u001b[39mrelease()  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1253\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m concrete\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:1221\u001b[0m, in \u001b[0;36mFunction._get_concrete_function_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1219\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1220\u001b[0m     initializers \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m-> 1221\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madd_initializers_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitializers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1222\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_uninitialized_variables(initializers)\n\u001b[0;32m   1224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m   1225\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m   1226\u001b[0m   \u001b[38;5;66;03m# version which is guaranteed to never create variables.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:696\u001b[0m, in \u001b[0;36mFunction._initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    691\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_scoped_tracing_options(\n\u001b[0;32m    692\u001b[0m     variable_capturing_scope,\n\u001b[0;32m    693\u001b[0m     tracing_compilation\u001b[38;5;241m.\u001b[39mScopeType\u001b[38;5;241m.\u001b[39mVARIABLE_CREATION,\n\u001b[0;32m    694\u001b[0m )\n\u001b[0;32m    695\u001b[0m \u001b[38;5;66;03m# Force the definition of the function for these arguments\u001b[39;00m\n\u001b[1;32m--> 696\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_concrete_variable_creation_fn \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrace_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    697\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    698\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    700\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvalid_creator_scope\u001b[39m(\u001b[38;5;241m*\u001b[39munused_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39munused_kwds):\n\u001b[0;32m    701\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Disables variable creation.\"\"\"\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:178\u001b[0m, in \u001b[0;36mtrace_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    175\u001b[0m     args \u001b[38;5;241m=\u001b[39m tracing_options\u001b[38;5;241m.\u001b[39minput_signature\n\u001b[0;32m    176\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m--> 178\u001b[0m   concrete_function \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_define_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    179\u001b[0m \u001b[43m      \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\n\u001b[0;32m    180\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mbind_graph_to_function:\n\u001b[0;32m    183\u001b[0m   concrete_function\u001b[38;5;241m.\u001b[39m_garbage_collector\u001b[38;5;241m.\u001b[39mrelease()  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:283\u001b[0m, in \u001b[0;36m_maybe_define_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    282\u001b[0m   target_func_type \u001b[38;5;241m=\u001b[39m lookup_func_type\n\u001b[1;32m--> 283\u001b[0m concrete_function \u001b[38;5;241m=\u001b[39m \u001b[43m_create_concrete_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    284\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_func_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlookup_func_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\n\u001b[0;32m    285\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mfunction_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    288\u001b[0m   tracing_options\u001b[38;5;241m.\u001b[39mfunction_cache\u001b[38;5;241m.\u001b[39madd(\n\u001b[0;32m    289\u001b[0m       concrete_function, current_func_context\n\u001b[0;32m    290\u001b[0m   )\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:310\u001b[0m, in \u001b[0;36m_create_concrete_function\u001b[1;34m(function_type, type_context, func_graph, tracing_options)\u001b[0m\n\u001b[0;32m    303\u001b[0m   placeholder_bound_args \u001b[38;5;241m=\u001b[39m function_type\u001b[38;5;241m.\u001b[39mplaceholder_arguments(\n\u001b[0;32m    304\u001b[0m       placeholder_context\n\u001b[0;32m    305\u001b[0m   )\n\u001b[0;32m    307\u001b[0m disable_acd \u001b[38;5;241m=\u001b[39m tracing_options\u001b[38;5;241m.\u001b[39mattributes \u001b[38;5;129;01mand\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mattributes\u001b[38;5;241m.\u001b[39mget(\n\u001b[0;32m    308\u001b[0m     attributes_lib\u001b[38;5;241m.\u001b[39mDISABLE_ACD, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    309\u001b[0m )\n\u001b[1;32m--> 310\u001b[0m traced_func_graph \u001b[38;5;241m=\u001b[39m \u001b[43mfunc_graph_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc_graph_from_py_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    311\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtracing_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    312\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtracing_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpython_function\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    313\u001b[0m \u001b[43m    \u001b[49m\u001b[43mplaceholder_bound_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    314\u001b[0m \u001b[43m    \u001b[49m\u001b[43mplaceholder_bound_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    315\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    316\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    317\u001b[0m \u001b[43m    \u001b[49m\u001b[43madd_control_dependencies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdisable_acd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43marg_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction_type_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_arg_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction_type\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_placeholders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    320\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    322\u001b[0m transform\u001b[38;5;241m.\u001b[39mapply_func_graph_transforms(traced_func_graph)\n\u001b[0;32m    324\u001b[0m graph_capture_container \u001b[38;5;241m=\u001b[39m traced_func_graph\u001b[38;5;241m.\u001b[39mfunction_captures\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py:987\u001b[0m, in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, create_placeholders)\u001b[0m\n\u001b[0;32m    984\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    985\u001b[0m   deps_control_manager \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39mNullContextmanager()\n\u001b[1;32m--> 987\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeps_control_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdeps_ctx\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m    988\u001b[0m \u001b[43m  \u001b[49m\u001b[43mcurrent_scope\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvariable_scope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_variable_scope\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    989\u001b[0m \u001b[43m  \u001b[49m\u001b[43mdefault_use_resource\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcurrent_scope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_resource\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps.py:533\u001b[0m, in \u001b[0;36mAutomaticControlDependencies.__exit__\u001b[1;34m(self, unused_type, unused_value, unused_traceback)\u001b[0m\n\u001b[0;32m    526\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m r\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mbuilding_function:\n\u001b[0;32m    527\u001b[0m   \u001b[38;5;66;03m# There may be many stateful ops in the graph. Adding them as\u001b[39;00m\n\u001b[0;32m    528\u001b[0m   \u001b[38;5;66;03m# control inputs to each function output could create excessive\u001b[39;00m\n\u001b[0;32m    529\u001b[0m   \u001b[38;5;66;03m# control edges in the graph. Thus we create an intermediate No-op to\u001b[39;00m\n\u001b[0;32m    530\u001b[0m   \u001b[38;5;66;03m# chain the control dependencies between stateful ops and function\u001b[39;00m\n\u001b[0;32m    531\u001b[0m   \u001b[38;5;66;03m# outputs.\u001b[39;00m\n\u001b[0;32m    532\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 533\u001b[0m     control_output_op \u001b[38;5;241m=\u001b[39m \u001b[43mcontrol_flow_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mno_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    534\u001b[0m     control_output_op\u001b[38;5;241m.\u001b[39m_add_control_inputs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops_which_must_run)\n\u001b[0;32m    535\u001b[0m   updated_ops_which_must_run \u001b[38;5;241m=\u001b[39m [control_output_op]\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\ops\\gen_control_flow_ops.py:531\u001b[0m, in \u001b[0;36mno_op\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    529\u001b[0m \u001b[38;5;66;03m# Add nodes to the TensorFlow graph.\u001b[39;00m\n\u001b[0;32m    530\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 531\u001b[0m   _, _, _op, _outputs \u001b[38;5;241m=\u001b[39m \u001b[43m_op_def_library\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_op_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    532\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNoOp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m    534\u001b[0m   _result \u001b[38;5;241m=\u001b[39m _dispatch\u001b[38;5;241m.\u001b[39mdispatch(\n\u001b[0;32m    535\u001b[0m         no_op, (), \u001b[38;5;28mdict\u001b[39m(name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m    536\u001b[0m       )\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:796\u001b[0m, in \u001b[0;36m_apply_op_helper\u001b[1;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    791\u001b[0m must_colocate_inputs \u001b[38;5;241m=\u001b[39m [val \u001b[38;5;28;01mfor\u001b[39;00m arg, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(op_def\u001b[38;5;241m.\u001b[39minput_arg, inputs)\n\u001b[0;32m    792\u001b[0m                         \u001b[38;5;28;01mif\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mis_ref]\n\u001b[0;32m    793\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _MaybeColocateWith(must_colocate_inputs):\n\u001b[0;32m    794\u001b[0m   \u001b[38;5;66;03m# Add Op to graph\u001b[39;00m\n\u001b[0;32m    795\u001b[0m   \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m--> 796\u001b[0m   op \u001b[38;5;241m=\u001b[39m \u001b[43mg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_op_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_type_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    797\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscope\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    798\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattr_protos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    800\u001b[0m \u001b[38;5;66;03m# `outputs` is returned as a separate return value so that the output\u001b[39;00m\n\u001b[0;32m    801\u001b[0m \u001b[38;5;66;03m# tensors can the `op` per se can be decoupled so that the\u001b[39;00m\n\u001b[0;32m    802\u001b[0m \u001b[38;5;66;03m# `op_callbacks` can function properly. See framework/op_callbacks.py\u001b[39;00m\n\u001b[0;32m    803\u001b[0m \u001b[38;5;66;03m# for more details.\u001b[39;00m\n\u001b[0;32m    804\u001b[0m outputs \u001b[38;5;241m=\u001b[39m op\u001b[38;5;241m.\u001b[39moutputs\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py:670\u001b[0m, in \u001b[0;36mFuncGraph._create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m    668\u001b[0m   inp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcapture(inp)\n\u001b[0;32m    669\u001b[0m   captured_inputs\u001b[38;5;241m.\u001b[39mappend(inp)\n\u001b[1;32m--> 670\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_op_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    671\u001b[0m \u001b[43m    \u001b[49m\u001b[43mop_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    672\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute_device\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:2701\u001b[0m, in \u001b[0;36mGraph._create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m   2698\u001b[0m \u001b[38;5;66;03m# _create_op_helper mutates the new Operation. `_mutation_lock` ensures a\u001b[39;00m\n\u001b[0;32m   2699\u001b[0m \u001b[38;5;66;03m# Session.run call cannot occur between creating and mutating the op.\u001b[39;00m\n\u001b[0;32m   2700\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutation_lock():\n\u001b[1;32m-> 2701\u001b[0m   ret \u001b[38;5;241m=\u001b[39m \u001b[43mOperation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_node_def\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2702\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnode_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2703\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2704\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2705\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2706\u001b[0m \u001b[43m      \u001b[49m\u001b[43mcontrol_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontrol_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2707\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2708\u001b[0m \u001b[43m      \u001b[49m\u001b[43moriginal_op\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_default_original_op\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2709\u001b[0m \u001b[43m      \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2710\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2711\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_op_helper(ret, compute_device\u001b[38;5;241m=\u001b[39mcompute_device)\n\u001b[0;32m   2712\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1196\u001b[0m, in \u001b[0;36mOperation.from_node_def\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m   1193\u001b[0m     control_input_ops\u001b[38;5;241m.\u001b[39mappend(control_op)\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;66;03m# Initialize c_op from node_def and other inputs\u001b[39;00m\n\u001b[1;32m-> 1196\u001b[0m c_op \u001b[38;5;241m=\u001b[39m \u001b[43m_create_c_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_def\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontrol_input_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1197\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m Operation(c_op, SymbolicTensor)\n\u001b[0;32m   1198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init(g)\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1026\u001b[0m, in \u001b[0;36m_create_c_op\u001b[1;34m(graph, node_def, inputs, control_inputs, op_def, extract_traceback)\u001b[0m\n\u001b[0;32m   1024\u001b[0m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1025\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m graph\u001b[38;5;241m.\u001b[39m_c_graph\u001b[38;5;241m.\u001b[39mget() \u001b[38;5;28;01mas\u001b[39;00m c_graph:\n\u001b[1;32m-> 1026\u001b[0m   op_desc \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tf_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTF_NewOperation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1027\u001b[0m \u001b[43m                                              \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_def\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1028\u001b[0m \u001b[43m                                              \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_def\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1029\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m node_def\u001b[38;5;241m.\u001b[39mdevice:\n\u001b[0;32m   1030\u001b[0m   pywrap_tf_session\u001b[38;5;241m.\u001b[39mTF_SetDevice(op_desc, compat\u001b[38;5;241m.\u001b[39mas_str(node_def\u001b[38;5;241m.\u001b[39mdevice))\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "\n",
+    "# Load the trained model\n",
+    "model_best = load_model('./model/face_modelCNN.h5') # set your machine model file path here\n",
+    "\n",
+    "# Classes 7 emotional states\n",
+    "class_names = ['Angry', 'Disgusted', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
+    "\n",
+    "# Load the pre-trained face cascade\n",
+    "face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "# Open a connection to the webcam (0 is usually the default camera)\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    # Capture frame-by-frame\n",
+    "    ret, frame = cap.read()\n",
+    "\n",
+    "    # Convert the frame to grayscale for face detection\n",
+    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "    # Detect faces in the frame\n",
+    "    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))\n",
+    "\n",
+    "    # Process each detected face\n",
+    "    for (x, y, w, h) in faces:\n",
+    "        # Extract the face region\n",
+    "        face_roi = frame[y:y + h, x:x + w]\n",
+    "\n",
+    "        # Resize the face image to the required input size for the model\n",
+    "        face_image = cv2.resize(face_roi, (48, 48))\n",
+    "        face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)\n",
+    "        face_image = image.img_to_array(face_image)\n",
+    "        face_image = np.expand_dims(face_image, axis=0)\n",
+    "        face_image = np.vstack([face_image])\n",
+    "\n",
+    "        # Predict emotion using the loaded model\n",
+    "        predictions = model_best.predict(face_image)\n",
+    "        emotion_label = class_names[np.argmax(predictions)]\n",
+    "\n",
+    "        # Display the emotion label on the frame\n",
+    "        cv2.putText(frame, f'Emotion: {emotion_label}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                    0.9, (0, 0, 255), 2)\n",
+    "\n",
+    "        # Draw a rectangle around the face\n",
+    "        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
+    "\n",
+    "    # Display the resulting frame\n",
+    "    cv2.imshow('Emotion Detection', frame)\n",
+    "\n",
+    "    # Break the loop if 'q' key is pressed\n",
+    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "        break\n",
+    "\n",
+    "# Release the webcam and close the window\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

model_attempt.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'cv2'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import svm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "# Define the emotions\n",
+    "emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']\n",
+    "\n",
+    "# Load the dataset\n",
+    "train_dir = 'path_to_your_dataset/train'\n",
+    "test_dir = 'path_to_your_dataset/test'\n",
+    "\n",
+    "# Define the data generator\n",
+    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        train_dir,\n",
+    "        target_size=(48, 48),\n",
+    "        batch_size=32,\n",
+    "        class_mode='categorical')\n",
+    "\n",
+    "test_generator = test_datagen.flow_from_directory(\n",
+    "        test_dir,\n",
+    "        target_size=(48, 48),\n",
+    "        batch_size=32,\n",
+    "        class_mode='categorical')\n",
+    "\n",
+    "# Define the CNN model\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(7, activation='softmax'))\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(train_generator, epochs=10)\n",
+    "\n",
+    "# Define the SVM model\n",
+    "svm_model = svm.SVC(kernel='rbf', C=1)\n",
+    "\n",
+    "# Extract features from the CNN model\n",
+    "cnn_features = model.layers[-2].output\n",
+    "\n",
+    "# Train the SVM model\n",
+    "svm_model.fit(cnn_features, train_generator.classes)\n",
+    "\n",
+    "# Define a function to predict the emotion\n",
+    "def predict_emotion(face):\n",
+    "    face = cv2.resize(face, (48, 48))\n",
+    "    face = face.reshape(1, 48, 48, 3)\n",
+    "    face = face / 255.0\n",
+    "    features = model.predict(face)\n",
+    "    emotion = svm_model.predict(features)\n",
+    "    return emotions[emotion[0]]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,6 @@
+Flask==3.0.0
+Pillow==10.2.0
+flask-cors==4.0.0
+transformers==4.39.1
+tensorflow
+tf-keras

static/index.html

@@ -0,0 +1,663 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Emojiifier</title>
+    <script nonce="<%= nonce %>" src="/static/script.js"></script>
+    <link
+      rel="stylesheet"
+      href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"
+      integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm"
+      crossorigin="anonymous"
+    />
+    <link
+      href="https://fonts.googleapis.com/css2?family=Open+Sans:wght@300;400;600;700&display=swap"
+      rel="stylesheet"
+    />
+    <link
+      rel="stylesheet"
+      href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"
+      integrity="sha512-iBBXm8fW90+nuLcSKlbmrPcLa0OT92xO1BIsZ+ywDWZCvqsWgccV3gFoRBv0z+8dLJgyAHIhR35VZc2oM/gI1w=="
+      crossorigin="anonymous"
+    />
+    <style nonce="<%= nonce %>">
+      body {
+        background-color: rgb(255, 187, 0);
+      }
+
+      .navbar {
+        transition: transform 0.3s ease-in-out, padding 0.3s ease-in-out,
+          background-color 0.3s ease-in-out;
+        padding: 1rem 2rem;
+        background-color: #ffdc6b;
+        border: black 2px solid;
+        border-radius: 30px;
+        width: 100%;
+        margin: 0 auto;
+        transform: scale(1);
+        position: relative;
+        padding-left: 20px;
+        padding-right: 20px;
+      }
+
+      .navbar.scrolled {
+        padding: 0.5rem 2rem;
+        border-radius: 0;
+        width: 100%;
+        position: fixed;
+        top: 0;
+        left: 0;
+        background-color: #ffffff6e;
+        backdrop-filter: blur(3px);
+        -webkit-backdrop-filter: blur(3px);
+        border: none !important;
+      }
+
+      .navbar-brand {
+        transition: transform 1s ease-in-out;
+        position: absolute;
+        left: 50%;
+        transform: translateX(-50%);
+      }
+
+      .navbar.scrolled .navbar-brand {
+        transform: translateX(-50%) scale(0.8);
+      }
+
+      .content-area {
+        width: 100vw;
+        height: calc(90vh);
+        padding: 20px;
+      }
+
+      @keyframes emojiChange {
+        0% {
+          content: "😃";
+        }
+        25% {
+          content: "😎";
+        }
+        50% {
+          content: "🤪";
+        }
+        75% {
+          content: "🥳";
+        }
+        100% {
+          content: "😃";
+        }
+      }
+
+      .emoji-text::after {
+        content: "😃";
+        animation: emojiChange 4s infinite;
+      }
+
+      .spacer {
+        margin-top: 13.6vh;
+      }
+
+      .content-wrapper {
+        margin-top: -13.6vh;
+      }
+
+      .main-content {
+        width: 95%;
+        margin: 0 auto;
+        min-height: calc(100vh - 13.6vh);
+      }
+
+      .bg-light-gray {
+        background-color: #f0f0f0;
+        min-height: 70vh;
+        border-top-left-radius: 30px !important;
+        border-bottom-left-radius: 30px;
+      }
+
+      .bg-dark-red {
+        background-color: #324376;
+        color: white;
+        min-height: 70vh;
+        border-top-right-radius: 30px;
+        border-bottom-right-radius: 30px;
+      }
+
+      .webcam-video {
+        object-fit: cover;
+        border-top-left-radius: 30px !important;
+        border-bottom-left-radius: 30px;
+      }
+
+      .instructions-container {
+        padding: 1.5rem;
+        background-color: rgba(255, 255, 255, 0.043);
+        border-radius: 20px;
+        margin: 1rem;
+        height: calc(100% - 2rem);
+        display: flex;
+        flex-direction: column;
+      }
+
+      .instructions-container h1 {
+        font-size: 1.6rem;
+        font-weight: 600;
+        margin-bottom: 1rem;
+        color: #ffc857;
+      }
+
+      .instructions-container h2,
+      .instructions-container h3 {
+        font-size: 1.1rem;
+        font-weight: 500;
+        color: #ffb627;
+        margin-top: 0.8rem;
+      }
+
+      .instructions-container ul {
+        margin-left: 0;
+        padding-left: 0;
+      }
+
+      .instructions-container li {
+        margin-bottom: 0.6rem;
+        font-size: 0.95rem;
+        line-height: 1.4;
+        color: #f0f0f0;
+      }
+
+      .instructions-container p {
+        color: #ffb627;
+        font-weight: 500;
+      }
+
+      .capture-btn {
+        margin-top: auto;
+        background-color: #ffc857;
+        color: #15224b;
+        border: none;
+        padding: 1rem;
+        border-radius: 10px;
+        font-weight: 600;
+        transition: all 0.3s ease;
+        opacity: 1;
+      }
+
+      .capture-btn:hover {
+        background-color: #ffb627;
+        transform: scale(1.02);
+      }
+
+      .calculating-container {
+        display: none;
+        text-align: center;
+        height: 100%;
+        justify-content: center;
+        align-items: center;
+        flex-direction: column;
+      }
+
+      .calculating-text {
+        font-size: 2rem;
+        color: #ffc857;
+        margin-bottom: 2rem;
+      }
+
+      .result-emoji {
+        font-size: 8rem;
+        margin-bottom: 1rem;
+        animation: pulse 2s infinite;
+      }
+
+      .result-comment {
+        font-size: 1.2rem;
+        color: #f0f0f0;
+      }
+
+      @keyframes bounce {
+        0%,
+        20%,
+        50%,
+        80%,
+        100% {
+          transform: translateY(0);
+        }
+        40% {
+          transform: translateY(-20px);
+        }
+        60% {
+          transform: translateY(-10px);
+        }
+      }
+
+      @keyframes pulse {
+        0% {
+          transform: scale(1);
+        }
+        50% {
+          transform: scale(1.1);
+        }
+        100% {
+          transform: scale(1);
+        }
+      }
+
+      @keyframes spin {
+        0% {
+          transform: rotate(0deg);
+        }
+        100% {
+          transform: rotate(360deg);
+        }
+      }
+
+      .loading-spinner {
+        width: 50px;
+        height: 50px;
+        border: 5px solid #f3f3f3;
+        border-top: 5px solid #ffc857;
+        border-radius: 50%;
+        animation: spin 1s linear infinite;
+        margin: 20px auto;
+      }
+
+      .bounce {
+        animation: bounce 2s infinite;
+      }
+
+      #capturedImage {
+        display: none;
+        width: 100%;
+        height: 100%;
+        object-fit: cover;
+        border-top-left-radius: 30px !important;
+        border-bottom-left-radius: 30px;
+      }
+
+      .technical-section {
+        background-color: #324376;
+        color: white;
+        padding: 4rem 2rem;
+      }
+
+      .technical-section h2 {
+        color: #ffc857;
+        margin-bottom: 2rem;
+      }
+
+      .step-box {
+        background: rgba(255, 255, 255, 0.1);
+        border-radius: 10px;
+        padding: 1.5rem;
+        margin-bottom: 1.5rem;
+      }
+
+      .step-box h3 {
+        color: #ffb627;
+        margin-bottom: 1rem;
+      }
+
+      .step-box p {
+        color: #f0f0f0;
+        line-height: 1.6;
+      }
+
+      .code-block {
+        background: #1e2a4a;
+        padding: 1rem;
+        border-radius: 5px;
+        margin: 1rem 0;
+        font-family: monospace;
+      }
+
+      .probability-bar {
+        height: 20px;
+        background: #ffc857;
+        border-radius: 10px;
+        margin: 5px 0;
+      }
+
+      .probability-label {
+        display: flex;
+        justify-content: space-between;
+        color: #f0f0f0;
+        margin-bottom: 5px;
+      }
+
+      .detected-emoji {
+        font-size: 4rem;
+      }
+
+      .highlight-text {
+        color: #ffc857;
+      }
+
+      .confidence-text {
+        color: #ffc857;
+      }
+
+      .preprocessed-image {
+        max-width: 100%;
+        border-radius: 5px;
+      }
+
+      .probability-bar-custom {
+        width: var(--percentage);
+      }
+
+      .processing-info {
+        margin-top: 10px;
+        font-size: 0.9em;
+        color: #666;
+      }
+
+      .step-list {
+        list-style: none;
+        padding: 0;
+      }
+
+      .step-list li {
+        padding: 5px 0;
+        color: #f0f0f0;
+        position: relative;
+        padding-left: 20px;
+      }
+
+      .step-list li:before {
+        content: "→";
+        position: absolute;
+        left: 0;
+        color: #ffc857;
+      }
+
+      .final-analysis {
+        background: #324376 !important;
+        border-radius: 30px !important;
+        padding: 40px !important;
+        margin-top: 40px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+      }
+
+      .analysis-container {
+        padding: 0;
+      }
+
+      .analysis-box {
+        background: rgba(255, 255, 255, 0.05) !important;
+        padding: 30px;
+        border-radius: 30px;
+        height: 100%;
+        border: none !important;
+        margin: 10px;
+        text-align: center;
+      }
+
+      .analysis-box h4 {
+        color: #f0f0f0;
+        font-size: 1.5rem;
+        margin-bottom: 20px;
+        font-weight: 300;
+        letter-spacing: 1px;
+      }
+
+      .detected-emoji {
+        font-size: 4.5rem;
+        margin: 15px 0;
+      }
+
+      .highlight-text {
+        color: #ffc857;
+        font-size: 2rem;
+        margin-top: 15px !important;
+        text-transform: capitalize;
+        font-weight: 300;
+      }
+
+      .confidence-text {
+        color: #ffc857;
+        font-size: 2rem;
+        font-weight: 300;
+      }
+
+      .final-analysis h3 {
+        color: #f0f0f0;
+        font-size: 2rem;
+        margin-bottom: 30px;
+        font-weight: 300;
+        letter-spacing: 1px;
+      }
+    </style>
+  </head>
+  <body>
+    <div class="container-fluid mt-4 fixed-top px-3">
+      <nav class="navbar navbar-expand navbar-light">
+        <div class="container-fluid">
+          <button
+            id="settingsBtn"
+            class="btn btn-link text-dark d-none d-lg-block"
+          >
+            <i class="fas fa-cog fa-lg"></i>
+          </button>
+
+          <a class="navbar-brand font-weight-bold" href="#">
+            EM<span class="emoji-text"></span>JIFIER
+          </a>
+
+          <button class="btn btn-link text-dark d-none d-lg-block">
+            <i class="fas fa-user fa-lg"></i>
+          </button>
+        </div>
+      </nav>
+    </div>
+
+    <div class="spacer"></div>
+
+    <div class="content-area">
+      <div class="row no-gutters camera-emoji-container">
+        <div class="col-md-6 w-100 vh-100 bg-light-gray">
+          <video
+            id="webcam"
+            autoplay
+            playsinline
+            class="w-100 h-100 webcam-video"
+          ></video>
+          <img id="capturedImage" alt="Captured Image" />
+        </div>
+        <div class="col-md-6 no-guttter w-100 vh-100 bg-dark-red">
+          <div class="instructions-container">
+            <div id="initial-content">
+              <h1>Welcome to our Emotion Detection Website!</h1>
+
+              <h2>Getting Started:</h2>
+              <ul class="list-unstyled">
+                <li>
+                  1. Allow webcam access and ensure your face is well-lit and
+                  centered
+                </li>
+                <li>
+                  2. Click "DETECT" to capture your image and display your
+                  emotion (e.g., Happy, Sad)
+                </li>
+                <li>
+                  3. Click "Show It's Working" to see how the system uses facial
+                  landmarks and SVM model analysis
+                </li>
+              </ul>
+
+              <h3>Best Practices:</h3>
+              <ul class="list-unstyled">
+                <li>• Ensure clear lighting</li>
+                <li>• Keep face unobstructed</li>
+                <li>• Check webcam permissions if issues arise</li>
+                <li>• Adjust lighting if needed</li>
+              </ul>
+            </div>
+
+            <div id="calculating-content" class="calculating-container">
+              <div class="loading-spinner"></div>
+              <div class="result-emoji"></div>
+              <div class="result-comment"></div>
+            </div>
+
+            <button id="detectBtn" class="capture-btn">
+              <i class="fas fa-smile mr-2"></i>Detect Emotion
+            </button>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <div id="technical-section" class="technical-section">
+      <div class="container">
+        <h2 class="text-center mb-4">Real-Time Model Analysis</h2>
+
+        <div class="step-box">
+          <h3>Step 1: Image Acquisition</h3>
+          <p>Capturing and preparing your image:</p>
+          <div id="preprocessed-image"></div>
+        </div>
+
+        <div class="step-box">
+          <h3>Step 2: Model Prediction</h3>
+          <p>Confidence scores for each emotion:</p>
+          <div id="emotion-probabilities">
+            <!-- Probabilities will be dynamically inserted here -->
+          </div>
+        </div>
+
+        <div class="step-box final-analysis">
+          <h3>Final Analysis</h3>
+          <div class="analysis-container">
+            <div class="row align-items-stretch">
+              <div class="col-md-6">
+                <div class="analysis-box">
+                  <h4>Primary Emotion</h4>
+                  <div id="primary-emotion" class="text-center">
+                    <span id="detected-emoji" class="detected-emoji"></span>
+                    <h4 id="detected-emotion" class="highlight-text"></h4>
+                  </div>
+                </div>
+              </div>
+              <div class="col-md-6">
+                <div class="analysis-box">
+                  <h4>Confidence Level</h4>
+                  <div id="confidence-score" class="text-center">
+                    <h4 class="confidence-text"></h4>
+                  </div>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </body>
+  <script nonce="<%= nonce %>">
+    const navbar = document.querySelector(".navbar");
+
+    window.addEventListener("scroll", () => {
+      if (window.scrollY > 10) {
+        navbar.classList.add("scrolled");
+      } else {
+        navbar.classList.remove("scrolled");
+      }
+    });
+
+    // Function to update technical section with model results
+    function updateTechnicalSection(result) {
+      // Update preprocessed image
+      document.getElementById("preprocessed-image").innerHTML = `
+        <img src="${result.grayscale_image}" alt="Preprocessed Image" class="preprocessed-image">
+      `;
+
+      // Update emotion probabilities
+      const probContainer = document.getElementById("emotion-probabilities");
+      probContainer.innerHTML = "";
+
+      Object.entries(result.model_probabilities).forEach(
+        ([emotion, probability]) => {
+          const percentage = (probability * 100).toFixed(1);
+          probContainer.innerHTML += `
+          <div class="probability-label">
+            <span>${emotion}</span>
+            <span>${percentage}%</span>
+          </div>
+          <div class="probability-bar probability-bar-custom" style="--percentage: ${percentage}%"></div>
+        `;
+        }
+      );
+
+      // Update primary emotion
+      document.getElementById("detected-emoji").textContent = result.emoji;
+      document.getElementById("detected-emotion").textContent = result.emotion;
+
+      // Update confidence score
+      const confidence = (
+        result.model_probabilities[result.emotion] * 100
+      ).toFixed(1);
+      document
+        .getElementById("confidence-score")
+        .querySelector("h4").textContent = `${confidence}% Confident`;
+    }
+  </script>
+  <script
+    nonce="<%= nonce %>"
+    src="https://code.jquery.com/jquery-3.2.1.slim.min.js"
+    integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN"
+    crossorigin="anonymous"
+  ></script>
+  <script
+    nonce="<%= nonce %>"
+    src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js"
+    integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q"
+    crossorigin="anonymous"
+  ></script>
+  <script
+    nonce="<%= nonce %>"
+    src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"
+    integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl"
+    crossorigin="anonymous"
+  ></script>
+  <script nonce="<%= nonce %>">
+    async function initWebcam() {
+      try {
+        const stream = await navigator.mediaDevices.getUserMedia({
+          video: true,
+        });
+        const video = document.getElementById("webcam");
+        video.srcObject = stream;
+      } catch (err) {
+        console.error("Error accessing webcam:", err);
+      }
+    }
+
+    function captureImage() {
+      const video = document.getElementById("webcam");
+      const canvas = document.createElement("canvas");
+      canvas.width = video.videoWidth;
+      canvas.height = video.videoHeight;
+      canvas
+        .getContext("2d")
+        .drawImage(video, 0, 0, canvas.width, canvas.height);
+
+      // Hide video and show captured image
+      video.style.display = "none";
+      const capturedImage = document.getElementById("capturedImage");
+      capturedImage.src = canvas.toDataURL("image/png");
+      capturedImage.style.display = "block";
+
+      // Start emotion detection animation
+      const initialContent = document.getElementById("initial-content");
+      const calculatingContent = document.getElementById("calculating-content");
+      const detectBtn = document.getElementById("detectBtn");
+
+      initialContent.style.display = "none";
+      calculatingContent.style.display = "flex";
+      detectBtn.style.opacity = "0";
+
+      return capturedImage;
+    }
+
+    initWebcam();
+  </script>
+</html>

static/script.js

@@ -0,0 +1,225 @@
+document.addEventListener("DOMContentLoaded", () => {
+  document
+    .getElementById("detectBtn")
+    .addEventListener("click", onDetectEmotionClick);
+  document.getElementById("settingsBtn").addEventListener("click", onDetectEmotionClick);
+  initWebcam();
+  const technicalSection = document.getElementById("technical-section");
+  technicalSection.style.display = "none";
+});
+
+ // Send the image to the Flask backend
+ async function sendImageToBackend(imageData) {
+  try {
+      // Get base64 string and remove the prefix
+      const base64String = imageData.src.split(',')[1];
+      
+      const response = await fetch("/upload", { // Changed to relative path
+          method: "POST",
+          headers: {
+              "Content-Type": "application/json",
+          },
+          body: JSON.stringify({
+              image: base64String
+          })
+      });
+
+      if (!response.ok) {
+          throw new Error(`Server responded with status: ${response.status}`);
+      }
+      const result = await response.json();
+      console.log("Emotion detection result:", result);
+      return result;
+  } catch (error) {
+      console.error("Error during API call:", error);
+      throw error;
+  }
+}
+  // Handle the Detect Emotion button click
+  async function onDetectEmotionClick() {
+    const initialContent = document.getElementById("initial-content");
+    const calculatingContent = document.getElementById("calculating-content");
+    const detectBtn = document.getElementById("detectBtn");
+    const technicalSection = document.getElementById("technical-section");
+    const loadingSpinner = document.querySelector(".loading-spinner");
+
+    try {
+        // Hide initial content and show loading
+        initialContent.style.display = "none";
+        calculatingContent.style.display = "flex";
+        detectBtn.style.opacity = "0";
+        loadingSpinner.style.display = "block";
+        
+        // Hide technical section while processing
+        technicalSection.style.display = "none";
+        
+        // Capture and process image
+        const capturedImage = captureImage();
+        const result = await sendImageToBackend(capturedImage);
+
+        if (result.error) {
+            throw new Error(result.error);
+        }
+
+        // Hide loading spinner after getting results
+        loadingSpinner.style.display = "none";
+
+        // Show and update technical section
+        technicalSection.style.display = "block";
+        void technicalSection.offsetWidth;
+        technicalSection.classList.add('visible');
+        
+        // Update technical section with processing steps
+        updateTechnicalSection(result);
+
+        // Update emotion display with personalized message
+        document.querySelector(".result-emoji").textContent = result.emoji;
+        document.querySelector(".result-comment").textContent = getEmotionMessage(result.emotion);
+
+        // Update button
+        detectBtn.innerHTML = '<i class="fas fa-code mr-2"></i>Show Details';
+        detectBtn.style.opacity = "1";
+        
+        // Remove old click handler and add new one
+        detectBtn.removeEventListener('click', onDetectEmotionClick);
+        detectBtn.addEventListener('click', handleShowDetails);
+
+    } catch (error) {
+        console.error("Error:", error);
+        loadingSpinner.style.display = "none";
+        document.querySelector(".result-emoji").textContent = "❌";
+        document.querySelector(".result-comment").textContent = "Failed to detect emotion. Please try again.";
+        detectBtn.style.opacity = "1";
+        technicalSection.style.display = "none";
+    }
+  }
+  
+  // Initialize when document is loaded
+  document.addEventListener("DOMContentLoaded", () => {
+    const detectBtn = document.getElementById("detectBtn");
+    const settingsBtn = document.getElementById("settingsBtn");
+    const technicalSection = document.getElementById("technical-section");
+
+    // Hide technical section initially
+    technicalSection.style.display = "none";
+
+    // Add click handlers
+    detectBtn.addEventListener("click", onDetectEmotionClick);
+    settingsBtn.addEventListener("click", onDetectEmotionClick);
+    
+    initWebcam();
+  });
+  
+  function updateTechnicalSection(result) {
+    console.log("Full result:", result);
+    console.log("Processing steps:", result.processing_steps);
+    console.log("Detailed steps:", result.processing_steps.detailed_steps);
+
+    // Update preprocessed image with processing info
+    document.getElementById("preprocessed-image").innerHTML = `
+        <img src="${result.grayscale_image}" alt="Preprocessed Image" class="preprocessed-image">
+        <div class="processing-info">
+            <p>Image Size: ${result.processing_steps.original_size[0]}x${result.processing_steps.original_size[1]}</p>
+            <p>Color Mode: ${result.processing_steps.color_mode}</p>
+        </div>
+    `;
+
+    // Update processing pipeline steps
+    const processSteps = result.processing_steps.detailed_steps;
+    if (!processSteps) {
+        console.error("No processing steps found in result");
+        return;
+    }
+
+    let stepsHtml = '<div class="process-flow">';
+    
+    // Add each processing stage with error handling
+    try {
+        for (const [stage, steps] of Object.entries(processSteps)) {
+            console.log("Processing stage:", stage, steps);
+            const stageName = stage.split('_').map(word => 
+                word.charAt(0).toUpperCase() + word.slice(1)
+            ).join(' ');
+            
+            stepsHtml += `
+                <div class="process-stage">
+                    <h4>${stageName}</h4>
+                    <ul class="step-list">
+                        ${Array.isArray(steps) ? steps.map(step => `<li>${step}</li>`).join('') : ''}
+                    </ul>
+                </div>
+            `;
+        }
+        stepsHtml += '</div>';
+        
+        // Add model information
+        stepsHtml += `
+            <div class="model-info">
+                <h4>MODEL SPECIFICATIONS</h4>
+                <ul class="step-list">
+                    <li>Type: ${result.processing_steps.model_type}</li>
+                    <li>Input Shape: ${result.processing_steps.input_shape}</li>
+                    <li>Output: ${result.processing_steps.output_classes}</li>
+                </ul>
+            </div>
+        `;
+
+        console.log("Generated HTML:", stepsHtml);
+        document.getElementById("processing-steps").innerHTML = stepsHtml;
+    } catch (error) {
+        console.error("Error generating steps HTML:", error);
+    }
+
+    // Update emotion probabilities
+    const probContainer = document.getElementById("emotion-probabilities");
+    probContainer.innerHTML = "";
+    
+    Object.entries(result.model_probabilities).forEach(([emotion, probability]) => {
+        const percentage = (probability * 100).toFixed(1);
+        const barElement = document.createElement('div');
+        barElement.className = 'probability-bar probability-bar-custom probability-bar-width';
+        barElement.style.setProperty('--percentage', `${percentage}%`);
+        
+        probContainer.innerHTML += `
+            <div class="probability-label">
+                <span>${emotion}</span>
+                <span>${percentage}%</span>
+            </div>
+        `;
+        probContainer.appendChild(barElement);
+    });
+
+    // Update remaining elements
+    document.getElementById("detected-emoji").textContent = result.emoji;
+    document.getElementById("detected-emotion").textContent = result.emotion;
+    document.getElementById("confidence-score").querySelector("h4").textContent = 
+        `${(result.model_probabilities[result.emotion] * 100).toFixed(1)}%`;
+  }
+  
+  function getEmotionMessage(emotion) {
+    const messages = {
+        happy: "You're radiating happiness! Your smile lights up the room! 🌟",
+        sad: "I see some sadness there. Remember, every cloud has a silver lining! 🌈",
+        angry: "Whoa, looking pretty fired up! Take a deep breath and count to ten. 🧘",
+        disgust: "That's quite the expression! Something leave a bad taste? 😖",
+        fear: "I sense some anxiety there. Remember, you're stronger than you think! 💪",
+        surprise: "Well, that caught you off guard! What an unexpected moment! 😲",
+        neutral: "Keeping it cool and collected with that poker face! 😎"
+    };
+    return messages[emotion] || "Interesting expression you've got there! 🤔";
+  }
+  
+  // Add this new function for handling the "Show Details" click
+  function handleShowDetails() {
+    const technicalSection = document.getElementById("technical-section");
+    // First ensure the section is visible
+    technicalSection.style.display = "block";
+    // Then scroll to it
+    setTimeout(() => {
+        technicalSection.scrollIntoView({ 
+            behavior: "smooth",
+            block: "start"
+        });
+    }, 100);
+  }
+  

static/style.css

@@ -0,0 +1,349 @@
+/* Move all styles from the <style> tag in index.html here */ 
+.detected-emoji {
+    font-size: 4rem;
+}
+
+.highlight-text {
+    color: #ffc857;
+}
+
+.confidence-text {
+    color: #ffc857;
+    font-size: 5rem !important;
+    font-weight: 800 !important;
+    text-align: center;
+    margin: 0;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100%;
+    letter-spacing: -1px;
+}
+
+.preprocessed-image {
+    max-width: 100%;
+    border-radius: 5px;
+}
+
+.probability-bar-custom {
+    width: var(--percentage);
+}
+
+.processing-info p {
+    margin: 5px 0;
+    color: #f0f0f0;
+}
+
+.probability-bar-width {
+    width: var(--percentage);
+}
+
+.technical-display {
+    display: block;
+}
+
+.smooth-scroll {
+    scroll-behavior: smooth;
+}
+
+.process-flow {
+    display: flex;
+    flex-direction: column;
+    gap: 20px;
+    margin: 20px 0;
+}
+
+.process-stage {
+    background: rgba(255, 255, 255, 0.08);
+    padding: 20px;
+    border-radius: 8px;
+    margin-bottom: 15px;
+    border-left: 4px solid #ffc857;
+}
+
+.process-stage h4 {
+    color: #ffc857;
+    margin-bottom: 15px;
+    font-size: 1.1em;
+    text-transform: uppercase;
+    letter-spacing: 1px; 
+}
+
+.model-info {
+    margin-top: 20px;
+    padding: 15px;
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 8px;
+    border-left: 4px solid #4CAF50;
+}
+
+.model-info h4 {
+    color: #4CAF50;
+    margin-bottom: 10px;
+}
+
+.analysis-box {
+    background: rgba(255, 255, 255, 0.05);
+    padding: 20px;
+    border-radius: 8px;
+    margin-bottom: 15px;
+    border-left: 4px solid #ffc857;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    min-height: 250px;
+}
+
+.analysis-box h4 {
+    color: #ffc857;
+    margin-bottom: 15px;
+    text-align: center;
+}
+
+.step-box .row {
+    margin: 0;
+}
+
+.step-box h3 {
+    position: relative;
+    padding-left: 35px;
+}
+
+.step-box h3:before {
+    content: "";
+    position: absolute;
+    left: 0;
+    top: 50%;
+    transform: translateY(-50%);
+    width: 25px;
+    height: 25px;
+    background: #ffc857;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 14px;
+    color: #324376;
+}
+
+.step-box:nth-child(1) h3:before { content: "1"; }
+.step-box:nth-child(2) h3:before { content: "2"; }
+.step-box:nth-child(3) h3:before { content: "3"; }
+.step-box:nth-child(4) h3:before { content: "4"; }
+
+.step-list li {
+    color: #ffffff;
+    margin-bottom: 8px;
+    line-height: 1.4;
+}
+
+.step-list li:before {
+    color: #ffc857;
+    margin-right: 10px;
+}
+
+.final-analysis {
+    background: rgba(50, 67, 118, 0.3) !important;
+    border-radius: 30px !important;
+    padding: 40px !important;
+    margin-top: 40px;
+}
+
+.final-analysis h3 {
+    color: #f0f0f0;
+    font-size: 2.5rem;
+    margin-bottom: 30px;
+    font-weight: 300;
+    letter-spacing: 1px;
+    padding-left: 0;
+}
+
+.final-analysis .analysis-box {
+    background: rgba(50, 67, 118, 0.8) !important;
+    border-radius: 20px;
+    padding: 30px;
+    height: 100%;
+    min-height: 280px;
+    border: none !important;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    margin: 10px;
+}
+
+.final-analysis .analysis-box h4 {
+    color: #f0f0f0;
+    font-size: 1.8rem;
+    margin-bottom: 25px;
+    font-weight: 300;
+    letter-spacing: 1px;
+    text-align: center;
+}
+
+.confidence-circle {
+    width: 150px;
+    height: 150px;
+    border-radius: 50%;
+    background: rgba(255, 255, 255, 0.05);
+    margin: 0 auto;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border: 4px solid #4CAF50;
+}
+
+.confidence-text {
+    font-size: 1.5rem;
+    color: #4CAF50;
+    font-weight: bold;
+}
+
+.detected-emoji {
+    font-size: 5rem;
+    display: block;
+    margin-bottom: 10px;
+}
+
+.highlight-text {
+    font-size: 1.8rem;
+    text-transform: capitalize;
+}
+
+.step-box h3 {
+    color: #ffc857;
+    font-size: 1.8rem;
+    margin-bottom: 1.5rem;
+}
+
+.technical-section {
+    display: none;  /* Hide initially */
+    background-color: #324376;
+    color: white;
+    padding: 4rem 2rem;
+    opacity: 0;
+    transition: opacity 0.5s ease-in-out;
+}
+
+.technical-section.visible {
+    display: block;
+    opacity: 1;
+}
+
+.confidence-score {
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100%;
+    color: #ffc857 !important;
+}
+
+#confidence-score {
+    height: 100%;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    color: #ffc857 !important;
+
+}
+
+/* Primary Emotion Box */
+#primary-emotion {
+    text-align: center;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+    height: 100%;
+}
+
+.detected-emoji {
+    font-size: 5.5rem;
+    margin: 15px 0;
+}
+
+.highlight-text {
+    color: #ffc857;
+    font-size: 3.5rem !important;
+    margin-top: 15px !important;
+    text-transform: capitalize;
+    font-weight: 700 !important;
+    letter-spacing: -1px;
+}
+
+/* Confidence Score Box */
+#confidence-score {
+    height: 100%;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+
+.confidence-text {
+    color: #ffc857;
+    font-size: 3.5rem !important;
+    font-weight: 700 !important;
+    text-align: center;
+    margin: 0;
+    letter-spacing: -1px;
+}
+
+/* Container spacing */
+.analysis-container {
+    padding: 0;
+}
+
+.final-analysis .row {
+    margin: 0 -10px;
+}
+
+.final-analysis .col-md-6 {
+    padding: 10px;
+}
+
+/* Remove numbered circles from final analysis */
+.final-analysis h3:before {
+    display: none;
+}
+
+@keyframes emojiChange {
+    0% {
+        content: "😃";
+        transform: rotate(0deg);
+    }
+    25% {
+        content: "😎";
+        transform: rotate(90deg);
+    }
+    50% {
+        content: "🤪";
+        transform: rotate(180deg);
+    }
+    75% {
+        content: "🥳";
+        transform: rotate(270deg);
+    }
+    100% {
+        content: "😃";
+        transform: rotate(360deg);
+    }
+}
+
+.emoji-text {
+    position: relative;
+    display: inline-block;
+}
+
+.emoji-text::after {
+    content: "😃";
+    position: absolute;
+    animation: emojiChange 4s infinite linear;
+    display: inline-block;
+    transform-origin: center center;
+    width: 1em;
+    height: 1em;
+    left: 0;
+    top: 0;
+}
+