first commit

app.py

@@ -6,7 +6,8 @@ import os
 
 hf_token = os.environ["HF_TOKEN"]
 
-# Load the Gemma 3 pipeline
+# Load the Gemma 3 pipeline.
+# Gemma 3 is a multimodal model that accepts text and image inputs.
 pipe = pipeline(
     "image-text-to-text",
     model="google/gemma-3-4b-it",
@@ -14,60 +15,54 @@ pipe = pipeline(
     torch_dtype=torch.bfloat16,
     use_auth_token=hf_token
 )
-
 @spaces.GPU
 def generate_response(user_text, user_image, history):
-    # Initialize message structure
-    messages = [{
-        "role": "system",
-        "content": [{"type": "text", "text": "You are a helpful assistant."}]
-    }]
-    
-    # Build user content
+    messages = [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "You are a helpful assistant."}]
+        }
+    ]
     user_content = []
-    if user_text:
-        user_content.append({"type": "text", "text": user_text})
     if user_image is not None:
         user_content.append({"type": "image", "image": user_image})
+    if user_text:
+        user_content.append({"type": "text", "text": user_text})
+    messages.append({"role": "user", "content": user_content})
     
-    if user_content:
-        messages.append({"role": "user", "content": user_content})
+    # Call the pipeline with the provided messages.
+    output = pipe(text=messages, max_new_tokens=200)
+
+    print(output)
+    print(output[0]["generated_text"][-1]["content"])
     
+    # Attempt to extract the generated content using the expected structure.
     try:
-        # Generate response
-        output = pipe(text=messages, max_new_tokens=200)
-        
-        # Extract generated response
         response = output[0]["generated_text"][-1]["content"]
-    except Exception as e:
-        print(f"Error generating response: {e}")
-        response = "Sorry, I encountered an error processing your request."
-    
-    # Update chat history
-    if user_text or user_image:
-        history.append(((user_text, user_image) if user_image else user_text, response))
+        history.append((user_text, response))
+
+    except (KeyError, IndexError, TypeError):
+        # Fallback: return the raw output as a string.
+        #print(response)
+        pass
+        #response = str(output)
     
     return history, history
 
 with gr.Blocks() as demo:
     gr.Markdown("# Gemma 3 Chat Interface")
-    gr.Markdown("This interface lets you chat with the Gemma 3 model. You can type a message and optionally attach an image.")
-    
-    chatbot = gr.Chatbot()
-    
+    gr.Markdown(
+        "This interface lets you chat with the Gemma 3 model. "
+        "You can type a message and optionally attach an image."
+    )
+    # Specify type="messages" to avoid deprecation warnings.
+    chatbot = gr.Chatbot(type="messages")
     with gr.Row():
-        with gr.Column(scale=3):
-            txt = gr.Textbox(show_label=False, placeholder="Type your message here...", container=False)
-        with gr.Column(scale=1):
-            img = gr.Image(type="pil", label="Optional Image Upload")
-    
+        txt = gr.Textbox(show_label=False, placeholder="Type your message here...", container=False)
+        img = gr.Image(type="pil", label="Attach an image (optional)")
     state = gr.State([])
-    
-    txt.submit(
-        generate_response,
-        inputs=[txt, img, state],
-        outputs=[chatbot, state]
-    )
+
+    txt.submit(generate_response, inputs=[txt, img, state], outputs=[chatbot, state])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()