Update app.py

app.py

@@ -1,24 +1,44 @@
+import cv2
 import gradio as gr
-import torch
-from transformers import AutoModelForImageClassification, AutoFeatureExtractor
-
-# Load model and feature extractor (adjust these according to your model)
-model_name = "sign_language_model(1).h5"  # Replace with your model path or Hugging Face model
-model = AutoModelForImageClassification.from_pretrained(model_name)
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-
-# Define prediction function
-def predict(image):
-    inputs = feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    predicted_class = logits.argmax(-1).item()
-    return predicted_class
-
-# Set up the Gradio interface
-interface = gr.Interface(fn=predict, 
-                         inputs=gr.Image(type="pil"), 
-                         outputs=gr.Text(label="Predicted Sign Language"))
-
-if __name__ == "__main__":
-    interface.launch()
+import numpy as np
+from transformers import pipeline
+
+# Load the hand detection model from Hugging Face
+gesture_pipeline = pipeline("image-classification", model="google/vit-base-patch16-224-in21k")
+
+# Function to process the video stream
+def process_frame(frame):
+    # Convert the frame to RGB for the Hugging Face model
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+    # You can apply hand gesture recognition logic here (e.g., hand landmarks tracking)
+    gesture = gesture_pipeline(rgb_frame)
+
+    # Output gesture recognition results
+    gesture_name = gesture[0]["label"]
+    gesture_confidence = gesture[0]["score"]
+
+    # Display gesture on the screen (in this case, we'll move the elements or give a thumbs up)
+    if "Thumbs up" in gesture_name:
+        print("Gesture recognized: Thumbs Up!")
+    if "Heart" in gesture_name:
+        print("Gesture recognized: Heart!")
+
+    # Update the frame with the recognized gesture
+    cv2.putText(frame, f"Gesture: {gesture_name}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    return frame
+
+# Gradio interface function
+def video_input(video):
+    # Process the video frame by frame
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+        processed_frame = process_frame(frame)
+        yield processed_frame
+
+# Set up the Gradio interface with the webcam
+iface = gr.Interface(fn=video_input, inputs=gr.Video(source="webcam"), outputs="video", live=True)
+
+iface.launch()