Update app.py

app.py

@@ -4,17 +4,22 @@ import gradio as gr
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
 
-# Load the model from Hugging Face Model Hub
-model_path = hf_hub_download(repo_id="sign_language", filename="saved_model.pb", repo_type="model")
-model = tf.saved_model.load(model_path)
+# Download the entire model directory
+model_dir = hf_hub_download(repo_id="Par24/sign_language", filename="saved_model", repo_type="model")
+
+# Load the model correctly
+model = tf.saved_model.load(model_dir)
 infer = model.signatures["serving_default"]
 
-# Define class labels (modify as per your dataset)
+# Define class labels
 class_labels = ['Hello', 'Yes', 'No', 'Thank You', 'Please']
 
 def predict_sign(frame):
+    # Convert BGR (OpenCV) to RGB (TensorFlow format)
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
     # Preprocess the frame
-    img = cv2.resize(frame, (224, 224))  # Resize to match model input
+    img = cv2.resize(frame, (224, 224))  # Resize
     img = img / 255.0  # Normalize
     img = np.expand_dims(img, axis=0)  # Add batch dimension
     img = tf.convert_to_tensor(img, dtype=tf.float32)
@@ -23,6 +28,8 @@ def predict_sign(frame):
     predictions = infer(tf.constant(img))
     output_tensor_name = list(predictions.keys())[0]  # Get the output tensor name
     predictions = predictions[output_tensor_name].numpy()
+    
+    # Get predicted class and confidence
     predicted_class = class_labels[np.argmax(predictions)]
     confidence = np.max(predictions)
     
@@ -30,14 +37,23 @@ def predict_sign(frame):
 
 def process_frame(frame):
     pred, conf = predict_sign(frame)
+
+    # Convert RGB back to BGR for OpenCV
+    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+    # Overlay prediction text
     cv2.putText(frame, f"{pred} ({conf:.2f})", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
     return frame
 
 # Gradio Live Webcam Interface
-def webcam_feed():
-    return gr.Video(source="webcam", streaming=True, mirror=True)
-
-gui = gr.Interface(fn=process_frame, inputs=webcam_feed(), outputs="image", live=True)
+gui = gr.Interface(
+    fn=process_frame,  # Function to process frames
+    inputs="webcam",   # Use webcam as input
+    outputs="image",   # Output is an image
+    live=True
+)
 
+# Launch Gradio App
 if __name__ == "__main__":
     gui.launch(server_name="0.0.0.0", server_port=7860)