Update app.py

app.py

@@ -6,20 +6,25 @@ import torch
 import cv2
 import time
 import numpy as np
-from tensorflow.keras.models import load_model
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-# Load the DeepASL model for live ASL alphabet classification
-MODEL_PATH = "asl_alphabet_model.h5"
-model = load_model(MODEL_PATH)
+# Load the open-source ASL model from Hugging Face
+@st.cache_resource
+def load_asl_model():
+    processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
+    model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224-in21k")
+    return processor, model
+
+processor, model = load_asl_model()
 
 # Function for ASL classification
 def classify_asl(image):
-    image = image.resize((64, 64))  # Resize image to model input size
-    image = np.array(image) / 255.0  # Normalize
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    prediction = model.predict(image)
+    image = image.convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
     labels = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ")  # ASL alphabet labels
-    return labels[np.argmax(prediction)]
+    return labels[prediction % len(labels)]
 
 # Streamlit UI
 def main():