Update app.py

app.py

@@ -1,70 +1,184 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import os
 
+# Model path configuration - can be loaded from environment variable or default path
+MODEL_PATH = os.getenv("MODEL_PATH", "Jiaqi-hkust/Robust-R1")
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+# Global variables to store model and processor
+model = None
+processor = None
 
-    messages = [{"role": "system", "content": system_message}]
+def load_model():
+    """Load model and processor"""
+    global model, processor
+    
+    if model is None or processor is None:
+        print(f"Loading model: {MODEL_PATH}")
+        
+        # Load model
+        model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            MODEL_PATH,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+        )
+        
+        # Load processor
+        processor = AutoProcessor.from_pretrained(MODEL_PATH)
+        
+        print("Model loaded successfully!")
+    
+    return model, processor
 
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+def inference(image, question, max_new_tokens=1024, temperature=0.7):
+    """Perform inference"""
+    try:
+        # Ensure model is loaded
+        model, processor = load_model()
+        
+        # Validate multimodal inputs
+        if image is None:
+            return "⚠️ Error: Please upload an image. This is a multimodal model that requires both an image and text input."
+        
+        if not question or question.strip() == "":
+            return "⚠️ Error: Please enter your question. This is a multimodal model that requires both an image and text input."
+        
+        # Build multimodal messages (image + text)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": image,  # Image input
+                    },
+                    {"type": "text", "text": question},  # Text input
+                ],
+            }
+        ]
+        
+        # Prepare inputs
+        text = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        
+        # Move inputs to the device where the model is located
+        device = next(model.parameters()).device
+        inputs = inputs.to(device)
+        
+        # Generate response
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            do_sample=True if temperature > 0 else False,
+        )
+        
+        # Decode output
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] 
+            for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False
+        )
+        
+        return output_text[0]
+        
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
+# Create Gradio interface
+with gr.Blocks(title="Robust-R1: Visual Understanding Demo", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        ## Citation
+        The following is a BibTeX reference:
+        
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                type="pil",
+                label="📸 Upload Image (Required)",
+                height=400,
+                info="Upload an image that you want to ask questions about"
+            )
+            question_input = gr.Textbox(
+                label="💬 Your Question (Required)",
+                placeholder="e.g., Describe the content of this image",
+                lines=3,
+                info="Enter your question about the uploaded image"
+            )
+            
+            with gr.Row():
+                max_tokens = gr.Slider(
+                    minimum=64,
+                    maximum=2048,
+                    value=512,
+                    step=64,
+                    label="Max Generation Length"
+                )
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature"
+                )
+            
+            submit_btn = gr.Button("Submit", variant="primary", size="lg")
+            clear_btn = gr.Button("Clear", variant="secondary")
+        
+        with gr.Column(scale=1):
+            output = gr.Textbox(
+                label="Model Response",
+                lines=15,
+                interactive=False
+            )
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["Describe this image", "What does this image show?"],
+        ],
+        inputs=[question_input],
+        label="Example Questions"
+    )
+    
+    # Bind events
+    submit_btn.click(
+        fn=inference,
+        inputs=[image_input, question_input, max_tokens, temperature],
+        outputs=output
+    )
+    
+    clear_btn.click(
+        fn=lambda: (None, "", 512, 0.7, ""),
+        outputs=[image_input, question_input, max_tokens, temperature, output]
+    )
+    
+    # Show message when page loads
+    demo.load(
+        fn=lambda: "Model is loading, please wait...",
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    # When running in Space, Gradio will automatically handle the port
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
+