Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+from PIL import UnidentifiedImageError
+
+def sign_classifier(input_image):
+  try:
+    # Load the image
+    image = input_image
+
+    # Emotion classifier
+    sign_pipe = pipeline("image-classification", model="Marxulia/asl_aplhabet_img_classifier_v3")
+    sign_result = sign_pipe(image)
+    predicted_sign = sign_result[0]['label']
+    sign_confidence = sign_result[0]['score']
+
+    # Format the results
+    sign_output = f"Sign Prediction: {predicted_sign}\nConfidence: {sign_confidence}"
+
+    return sign_output
+
+  except UnidentifiedImageError:
+    return "Error: Invalid input image format."
+
+# Load an example image (replace 'path/to/your/image.jpg' with your actual path)
+example_image1 = Image.open('H3.jpg')
+example_image2 = Image.open('B3.jpg')
+
+# Create Gradio interface
+input_image = gr.Image(type="pil", label="Upload Image")
+output_sign = gr.Textbox(label="Sign Classifier")
+
+# Provide a list of examples, where each element is a list with the input and output
+examples = [[example_image1, "H Sign"],[example_image2, "B Sign"]]  # Modify the output based on your image
+
+# Include examples in the interface
+interface = gr.Interface(fn=sign_classifier, inputs=input_image, outputs=[output_sign], 
+                         title="Image Classifier", description="Upload an image and translate the sign", examples=examples)
+
+interface.launch(debug=True)