Update app.py

app.py

@@ -1,7 +1,45 @@
 import gradio as gr
+from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
+import torch
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Initialize the model and processor
+processor = AutoImageProcessor.from_pretrained("alexdekan030/autotrain-awcru-nr8j7")
+model = AutoModelForImageClassification.from_pretrained("alexdekan030/autotrain-awcru-nr8j7")
+pipe = pipeline("image-classification", model=model, image_processor=processor)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def predict_pneumonia(image):
+    """
+    Predict whether an image shows pneumonia or normal chest X-ray
+    Args:
+        image: Input image
+    Returns:
+        dict: Dictionary containing prediction probabilities
+    """
+    # Make prediction
+    result = pipe(image)
+    
+    # Create a formatted output dictionary
+    probabilities = {pred['label']: float(pred['score']) for pred in result}
+    
+    return probabilities
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_pneumonia,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Pneumonia Detection from Chest X-rays",
+    description="""Upload a chest X-ray image to detect if it shows signs of pneumonia.
+                   The model will classify the image as either 'NORMAL' or 'PNEUMONIA'
+                   and provide confidence scores for each class.""",
+    examples=[
+        # You can add example images here if you have them
+        # ["path/to/example1.jpg"],
+        # ["path/to/example2.jpg"]
+    ],
+    theme=gr.themes.Base()
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(share=True)