Update app.py

app.py

@@ -1,75 +1,24 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
 import cv2
 import mediapipe as mp
-import numpy as np
-import tensorflow as tf
-import gradio as gr
 
-# Load pre-trained model (replace 'model.h5' with the actual model path)
+# Load your model (Make sure the model is uploaded to the space or use a path to the model file)
 model = tf.keras.models.load_model("sign_language_model.h5")
 
-# MediaPipe Hands setup
-mp_hands = mp.solutions.hands
-mp_drawing = mp.solutions.drawing_utils
-
-# Define labels (replace with your model's classes)
-labels = ["A", "B", "C", "D", "E"]  # Example labels
-
-# Process webcam frame and predict sign language gesture
-def recognize_sign(image):
-    # Convert the input image to RGB
-    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    # MediaPipe Hand Detection
-    with mp_hands.Hands(static_image_mode=False,
-                        max_num_hands=1,
-                        min_detection_confidence=0.7,
-                        min_tracking_confidence=0.7) as hands:
-        result = hands.process(rgb_image)
-        
-        if result.multi_hand_landmarks:
-            for hand_landmarks in result.multi_hand_landmarks:
-                # Draw landmarks on the image
-                mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
-
-                # Extract hand landmark coordinates as features
-                landmarks = []
-                for lm in hand_landmarks.landmark:
-                    landmarks.extend([lm.x, lm.y, lm.z])
-                
-                # Reshape data for prediction
-                features = np.array(landmarks).reshape(1, -1)
-
-                # Predict gesture
-                prediction = model.predict(features)
-                gesture = labels[np.argmax(prediction)]
-
-                # Display the predicted gesture on the image
-                h, w, _ = image.shape
-                cv2.putText(image, gesture, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-                return gesture, image
-
-        return "No hand detected", image
-
-# Gradio interface wrapper
-def gradio_wrapper(image):
-    # Convert Gradio input to OpenCV format
-    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-    gesture, annotated_image = recognize_sign(image)
-    # Convert the annotated image back to RGB for display
-    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
-    return gesture, annotated_image
-
-# Create Gradio Interface
-interface = gr.Interface(
-    fn=gradio_wrapper,
-    inputs=gr.inputs.Image(source="webcam", tool=None),
-    outputs=[gr.outputs.Textbox(label="Predicted Gesture"),
-             gr.outputs.Image(label="Annotated Image")],
-    live=True,
-    title="Sign Language Recognition",
-    description="Predicts sign language gestures using TensorFlow and MediaPipe."
-)
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    interface.launch()
+# Define your inference function
+def predict(image):
+    # Preprocess the image here
+    # For example, resizing or normalizing
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = cv2.resize(image, (224, 224))  # Adjust based on your model input size
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    prediction = model.predict(image)
+    # Post-process the output if necessary (e.g., applying a threshold or mapping to class labels)
+    return prediction
+
+# Set up the Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(224, 224)), outputs="text")
+
+iface.launch()