Update app.py

app.py

@@ -1,38 +1,24 @@
 import gradio as gr
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.models import load_model
-import string
+import torch
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
 
-# Load the trained model
-model = load_model('sign_language_model(1).h5')
+# 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)
 
-# Class names for prediction
-class_names = list(string.ascii_lowercase[:26].replace('j', '').replace('z', ''))
-
-# Function to predict the sign language from the image
+# Define prediction function
 def predict(image):
-    # Preprocess the input image
-    image = np.array(image).astype(np.float32) / 255.0  # Normalize
-    image = np.expand_dims(image, axis=-1)  # Add channel dimension
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    
-    # Predict using the trained model
-    predictions = model.predict(image)
-    predicted_class = np.argmax(predictions, axis=1)[0]
-    
-    # Return the predicted class
-    return class_names[predicted_class]
+    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
 
-# Define Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(shape=(28, 28), image_mode="L", source="upload"),
-    outputs="text",
-    live=True,
-    title="Sign Language Recognition",
-    description="Upload a sign language image, and the model will predict the corresponding letter."
-)
+# Set up the Gradio interface
+interface = gr.Interface(fn=predict, 
+                         inputs=gr.Image(type="pil"), 
+                         outputs=gr.Text(label="Predicted Sign Language"))
 
-# Launch the Gradio interface
-iface.launch()
+if __name__ == "__main__":
+    interface.launch()