Update app.py

app.py

@@ -7,7 +7,7 @@ from huggingface_hub import InferenceClient
 # Initialize the Hugging Face Inference Client
 client = InferenceClient("microsoft/llava-med-7b-delta")
 
-# Function to encode image as base64 (optional if Gradio handles image conversion)
+# Function to encode image as base64
 def image_to_base64(image):
     buffered = io.BytesIO()
     image.save(buffered, format="PNG")
@@ -35,8 +35,14 @@ def respond(
     messages.append({"role": "user", "content": message})
 
     if image:
-        # Gradio handles image processing internally, so no need for manual base64 encoding
-        messages.append({"role": "user", "content": "Image uploaded"})
+        # Convert image(s) to base64
+        if isinstance(image, Image.Image):
+            image_b64 = image_to_base64(image)
+            messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
+        else:
+            for img in image:
+                image_b64 = image_to_base64(img)
+                messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
 
     # Call Hugging Face model for response
     try:
@@ -78,22 +84,4 @@ try:
         ],
         outputs=[
             gr.Textbox(label="Response", placeholder="Model response will appear here..."),
-            gr.Image(label="Generated Image", type="pil", output=True)
-        ],
-        title="LLAVA Model - Medical Image and Question",
-        description="Upload a medical image and ask a specific question about the image for a medical description.",
-        additional_inputs=[
-            gr.Textbox(label="System message", value="You are a friendly Chatbot."),
-            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
-        ]
-    )
-
-    # Launch the Gradio interface
-    if __name__ == "__main__":
-        print("Launching Gradio interface...")
-        demo.launch()
-
-except Exception as e:
-    print(f"Error during Gradio setup: {str(e)}")
+            gr.Image(label="Gene