Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
-import cv2
 import numpy as np
 from PIL import Image
+from transformers import pipeline
 
 # Set the page config
 st.set_page_config(page_title="Emotion Recognition App", layout="centered")
@@ -11,23 +11,12 @@ st.title("Emotion Recognition App")
 # Upload an image
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
-# Load OpenCV's face detection model
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+# Allocate the Hugging Face pipeline
+@st.cache_resource  # Cache the model to avoid reloading it
+def load_model():
+    return pipeline("image-classification", model="Xenova/facial_emotions_image_detection")
 
-# Load ONNX emotion detection model
-emotion_model_path = "emotion_recognition.onnx"  # Replace with your model path
-emotion_net = cv2.dnn.readNetFromONNX(emotion_model_path)
-
-# Emotion labels (based on model documentation)
-emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
-
-# Resize image to reduce memory usage
-def resize_image(image, max_size=(800, 800)):
-    """
-    Resizes the image to the specified maximum size while maintaining aspect ratio.
-    """
-    image.thumbnail(max_size, Image.Resampling.LANCZOS)
-    return image
+emotion_classifier = load_model()
 
 # Process the uploaded image
 if uploaded_file is not None:
@@ -35,46 +24,19 @@ if uploaded_file is not None:
     if uploaded_file.size > 10 * 1024 * 1024:  # 10 MB limit
         st.error("File too large. Please upload an image smaller than 10 MB.")
     else:
-        # Open and resize the image
-        image = Image.open(uploaded_file)
-        image = resize_image(image)
-
-        # Convert image to numpy array
-        image_np = np.array(image)
-
-        # Convert image to grayscale for face detection
-        gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-
-        # Detect faces
-        faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-        if len(faces) > 0:
-            for (x, y, w, h) in faces:
-                # Extract face ROI
-                face_roi = image_np[y:y+h, x:x+w]
-                face_blob = cv2.dnn.blobFromImage(face_roi, 1.0, (64, 64), (104, 117, 123), swapRB=True)
-
-                # Predict emotion
-                emotion_net.setInput(face_blob)
-                predictions = emotion_net.forward()
-                emotion_idx = np.argmax(predictions)
-                emotion = emotion_labels[emotion_idx]
+        # Open and preprocess the image
+        image = Image.open(uploaded_file).convert("RGB")
+        image_resized = image.resize((224, 224))  # Resize to match model input size
 
-                # Draw rectangle around the face
-                cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        # Convert image to numpy array and predict emotion
+        predictions = emotion_classifier(image_resized)
 
-                # Display emotion
-                cv2.putText(
-                    image_np,
-                    emotion,
-                    (x, y - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    0.9,
-                    (255, 0, 0),
-                    2,
-                )
+        # Extract the top prediction
+        if predictions:
+            top_prediction = predictions[0]  # Assuming the model returns a list of predictions
+            emotion = top_prediction["label"]
+            confidence = top_prediction["score"]
 
-            # Display the processed image
-            st.image(image_np, caption="Processed Image", use_column_width=True)
+            st.image(image, caption=f"Detected Emotion: {emotion} (Confidence: {confidence:.2f})", use_column_width=True)
         else:
-            st.warning("No faces detected in the image.")
+            st.warning("Unable to determine emotion. Try another image.")