Update app.py

app.py

@@ -14,6 +14,7 @@ def image_to_base64(image):
     img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
     return img_str
 
+# Function to interact with LLAVA model
 # Function to interact with LLAVA model
 def respond(
     message,
@@ -24,53 +25,65 @@ def respond(
     top_p,
     image=None
 ):
-    messages = [{"role": "system", "content": system_message}]
+    try:
+        messages = [{"role": "system", "content": system_message}]
+
+        for val in history:
+            if val[0]:
+                messages.append({"role": "user", "content": val[0]})
+            if val[1]:
+                messages.append({"role": "assistant", "content": val[1]})
+
+        messages.append({"role": "user", "content": message})
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+        if image:
+            # Convert image to base64
+            image_b64 = image_to_base64(image)
+            messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
 
-    messages.append({"role": "user", "content": message})
+        # Call Hugging Face model for response
+        response = ""
+        for message in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message.choices[0].delta.content
 
-    if image:
-        # Convert image to base64
-        image_b64 = image_to_base64(image)
-        messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
+            response += token
+            yield response
 
-    # Call Hugging Face model for response
-    response = ""
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    except Exception as e:
+        print(f"Error in respond function: {str(e)}")
+        yield f"Error occurred: {str(e)}"
 
-        response += token
-        yield response
+# Debugging print statements
+print("Starting Gradio interface setup...")
+try:
+    # Create a Gradio interface
+    demo = gr.Interface(
+        fn=respond,
+        inputs=[
+            gr.inputs.Textbox(label="Message"),
+            gr.inputs.Image(label="Upload Medical Image", type="pil")
+        ],
+        outputs=gr.outputs.Textbox(label="Response", placeholder="Model response will appear here..."),
+        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)")
+        ]
+    )
 
-# Create a Gradio interface
-demo = gr.Interface(
-    fn=respond,
-    inputs=[
-        gr.Textbox(label="Message"),
-        gr.Image(label="Upload Medical Image", type="pil")
-    ],
-    outputs=gr.Textbox(label="Response", placeholder="Model response will appear here..."),
-    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()
 
-# Launch the Gradio interface
-if __name__ == "__main__":
-    demo.launch()
+except Exception as e:
+    print(f"Error during Gradio setup: {str(e)}")