Create app.py

app.py

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+import cv2
+import numpy as np
+import urllib
+import tensorflow as tf
+from tensorflow.keras import layers, models, optimizers
+import gradio as gr
+
+# Load pre-trained model
+model = tf.keras.models.load_model('facial_condition_model.h5')
+
+# Function to preprocess image for model input
+def preprocess_image(image):
+    # Convert image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # Resize image to 128x128 pixels
+    resized = cv2.resize(gray, (128, 128), interpolation=cv2.INTER_AREA)
+    # Convert image to float32 and normalize pixel values
+    normalized = np.float32(resized) / 255.0
+    # Add batch dimension to image
+    batched = np.expand_dims(normalized, axis=0)
+    return batched
+
+# Function to calculate facial condition scores
+def calculate_scores(image):
+    # Preprocess image for model input
+    preprocessed_image = preprocess_image(image)
+    # Use pre-trained model to predict facial condition scores
+    score = model.predict(preprocessed_image)[0][0]
+    # Calculate wrinkle score
+    wrinkle_score = 1 - (1 / (1 + np.exp(-5*(score-50)/50)))
+    # Calculate laxity score
+    laxity_score = np.tanh(score / 10)
+    # Calculate facial age
+    facial_age = int(np.round(score / 10) * 10)
+    # Calculate final score
+    final_score = np.mean([wrinkle_score, laxity_score, facial_age])
+    return wrinkle_score, laxity_score, facial_age, final_score
+
+# Define Gradio interface
+inputs = gr.inputs.Image(label="Input Image")
+outputs = [gr.outputs.Label(label="Wrinkle Score"), 
+           gr.outputs.Label(label="Laxity Score"), 
+           gr.outputs.Label(label="Facial Age"), 
+           gr.outputs.Label(label="Final Score")]
+title = "Facial Condition Evaluator"
+description = "Upload a photo to evaluate your facial condition. This tool will calculate your wrinkle score, laxity score, facial age, and a final score based on a deep learning model."
+examples = [["https://media.licdn.com/dms/image/C5603AQEiW9vZPeekiw/profile-displayphoto-shrink_800_800/0/1516317900912?e=2147483647&v=beta&t=Rs0LpxDfsrHmQFzD0w1vRpCRln0q-sPWrAY9lgiqYeU"], 
+            ["https://scontent.ftpe8-1.fna.fbcdn.net/v/t1.6435-9/195542178_605699050027_1477839146608127555_n.jpg?_nc_cat=108&ccb=1-7&_nc_sid=dbeb18&_nc_ohc=fUhwWIuKSb0AX_Irg8T&_nc_ht=scontent.ftpe8-1.fna&oh=00_AfAg2ep5yEM_fzNubC3hS7P22hGWq_rR0WluvTBRkrSGvA&oe=6465EF97"]]
+iface = gr.Interface(calculate_scores, inputs, outputs, title=title, description=description, examples=examples)
+
+# Launch Gradio
+iface.launch()