Update app.py

app.py

@@ -1,61 +1,90 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-
-# --- 1. Model Loading and Classes Configuration ---
-try:
-    # Load the Model from the /tmp path specified in the Dockerfile
-    # NOTE: You MUST fix the TensorFlow version in the Dockerfile for this line to work.
-    model = tf.keras.models.load_model('/tmp/sign_language_model.h5')
-except Exception as e:
-    print(f"Error loading model: {e}")
-    model = None 
-
-# Your Specific Sign Language Classes (Order MUST match your training labels!)
-SIGN_CLASSES = ["HELLO", "GOOD BYE", "THANKYOU", "PLEASE", "YES", "NO", "SEE YOU", "LOOK", "FOOD", "SORRY", "HELP", "LOVE", "FRIEND", "NAME", "ME"] 
-
-# --- 2. The Real-Time Prediction Function ---
-def classify_sign(input_image_data):
-    """Processes a single frame from the live webcam feed."""
-    if model is None or input_image_data is None:
-        return "Model Loading or Camera Feed Not Active..."
-
-    # Preprocessing (Adjust these dimensions/normalization to match your model's training)
-    image_resized = tf.image.resize(input_image_data, (64, 64)) 
-    image_normalized = image_resized / 255.0
-
-    # Ensure the image shape is correct (e.g., convert to grayscale if needed)
-    if image_normalized.shape[-1] == 3: 
-        image_normalized = tf.image.rgb_to_grayscale(image_normalized)
-        
-    input_tensor = np.expand_dims(image_normalized, axis=0)
-    
-    # Make Prediction
-    predictions = model.predict(input_tensor)[0]
-    
-    # Find the best prediction
-    predicted_index = np.argmax(predictions)
-    predicted_sign = SIGN_CLASSES[predicted_index]
-    confidence = predictions[predicted_index] * 100 
-
-    # Return the result string
-    return f"PREDICTED SIGN: {predicted_sign} | Confidence: {confidence:.2f}%"
-
-
-# --- 3. The Gradio Interface for Continuous Streaming ---
-gr.Interface(
-    fn=classify_sign,
-    inputs=gr.Image(
-        sources=['webcam'],
-        type="numpy",
-        shape=(300, 300),
-        label="Live Sign Camera"
-    ),
-    outputs=gr.Textbox(label="Real-Time Translation"), 
-    live=True, 
-    title="Real-Time Sign Language Translator",
-    description="Show your sign in front of the camera, and the prediction will update instantly.",
-    theme="soft",
-    # FIX for PermissionError: Disables creation of the 'flagged' folder
-    allow_flagging=False 
-).launch(server_name="0.0.0.0", server_port=7860)
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import os
+
+# --- 1. Model Loading and Classes Configuration ---
+interpreter = None
+model_loaded = False
+input_details = None
+output_details = None
+MODEL_PATH = '/tmp/sign_language_model_lite.tflite'
+
+try:
+    if os.path.exists(MODEL_PATH):
+        # Load the TFLite model file instead of the heavy H5 file
+        interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
+        interpreter.allocate_tensors()
+        
+        input_details = interpreter.get_input_details()
+        output_details = interpreter.get_output_details()
+        model_loaded = True
+        print("SUCCESS: TFLite Model loaded successfully.")
+    else:
+        print(f"ERROR: Model file not found at {MODEL_PATH}")
+        
+except Exception as e:
+    print(f"FATAL ERROR: Failed to initialize TFLite interpreter: {e}")
+    model_loaded = False 
+
+# Your Specific Sign Language Classes (Order MUST match your training labels!)
+SIGN_CLASSES = ["HELLO", "GOOD BYE", "THANKYOU", "PLEASE", "YES", "NO", "SEE YOU", "LOOK", "FOOD", "SORRY", "HELP", "LOVE", "FRIEND", "NAME", "ME"] 
+
+# --- 2. The Real-Time Prediction Function (Updated for TFLite) ---
+def classify_sign(input_image_data):
+    """Processes a single frame from the live webcam feed using the TFLite interpreter."""
+    
+    if not model_loaded or input_image_data is None:
+        return "Model Loading Error or Camera Feed Not Active..."
+
+    # 1. Preprocessing (adjust to model's input: 64x64 grayscale, required for the model)
+    image_resized = tf.image.resize(input_image_data, (64, 64)) 
+    image_normalized = image_resized / 255.0
+
+    # Convert to grayscale if the input is color (shape[3] == 3)
+    if image_normalized.shape[-1] == 3: 
+        image_normalized = tf.image.rgb_to_grayscale(image_normalized)
+        
+    # Add batch dimension (1, 64, 64, 1)
+    input_tensor = np.expand_dims(image_normalized, axis=0)
+    
+    # 2. TFLite Prediction Logic
+    try:
+        # Set the input tensor
+        interpreter.set_tensor(input_details[0]['index'], input_tensor.numpy().astype(np.float32))
+        
+        # Invoke the model
+        interpreter.invoke()
+        
+        # Get prediction results
+        predictions = interpreter.get_tensor(output_details[0]['index'])[0]
+        
+        # 3. Post-processing
+        predicted_index = np.argmax(predictions)
+        predicted_sign = SIGN_CLASSES[predicted_index]
+        confidence = predictions[predicted_index] * 100 
+
+        return f"PREDICTED SIGN: {predicted_sign} | Confidence: {confidence:.2f}%"
+    
+    except Exception as e:
+        # This catches runtime errors during invocation
+        return f"Prediction Runtime Error: {e}"
+
+
+# --- 3. The Gradio Interface for Continuous Streaming ---
+gr.Interface(
+    fn=classify_sign,
+    inputs=gr.Image(
+        sources=['webcam'],
+        type="numpy",
+        shape=(300, 300),
+        label="Live Sign Camera"
+    ),
+    outputs=gr.Textbox(label="Real-Time Translation"), 
+    live=True, 
+    title="Real-Time Sign Language Translator",
+    description="Show your sign in front of the camera, and the prediction will update instantly.",
+    theme="soft",
+    # FIX for PermissionError and general stability
+    allow_flagging=False 
+).launch(server_name="0.0.0.0", server_port=7860)