Update app.py

app.py

@@ -1,20 +1,28 @@
 import streamlit as st
 import numpy as np
-import tensorflow as tf
 from PIL import Image
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-# Load gesture classification model from Hugging Face Hub (public model)
-processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
-model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224-in21k")
-
-# Function for gesture classification
-def classify_gesture(image):
+# Load sign language model from Hugging Face Hub
+processor = AutoImageProcessor.from_pretrained("nateraw/gesture-classification")
+model = AutoModelForImageClassification.from_pretrained("nateraw/gesture-classification")
+
+# Sign labels for classification
+sign_labels = {
+    0: "Hello",
+    1: "Thank You",
+    2: "Yes",
+    3: "No",
+    4: "Please"
+}
+
+# Function for sign classification
+def classify_sign(image):
     image = image.convert("RGB")
     inputs = processor(images=image, return_tensors="pt")
     outputs = model(**inputs)
     prediction = outputs.logits.argmax(-1).item()
-    return prediction
+    return sign_labels.get(prediction, "Unknown Sign")
 
 # Streamlit UI
 def main():
@@ -37,15 +45,15 @@ def main():
         if camera_image:
             image = Image.open(camera_image)
             st.image(image, caption="Captured Image", use_column_width=True)
-            gesture = classify_gesture(image)
-            st.write(f"Gesture: {gesture}")
+            gesture = classify_sign(image)
+            st.write(f"Detected Gesture: {gesture}")
 
     # Display uploaded image
     if uploaded_image:
         image = Image.open(uploaded_image)
         st.image(image, caption="Uploaded Image", use_column_width=True)
-        gesture = classify_gesture(image)
-        st.write(f"Gesture: {gesture}")
+        gesture = classify_sign(image)
+        st.write(f"Detected Gesture: {gesture}")
 
 if __name__ == "__main__":
     main()