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| import gradio as gr | |
| import torch | |
| from transformers import AutoProcessor, Qwen2VLForConditionalGeneration | |
| from PIL import Image | |
| # 1. HuggingFace Space Deployment Settings | |
| MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct" # Base model | |
| # To use your fine-tuned model from Kaggle: | |
| # 1. model.push_to_hub("your-name/med-qwen-vl-adapter") | |
| # 2. Add adapter load here for PEFT | |
| ADAPTER_ID = "hssling/med-qwen-vl-adapter" | |
| # Initialize Model and Processor globally | |
| print("Starting App Engine...") | |
| print(f"Loading {MODEL_ID}...") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| processor = AutoProcessor.from_pretrained(MODEL_ID) | |
| model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| MODEL_ID, | |
| torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
| device_map="auto" | |
| ) | |
| if ADAPTER_ID: | |
| print(f"Loading custom fine-tuned LoRA weights: {ADAPTER_ID}") | |
| model.load_adapter(ADAPTER_ID) | |
| # 2. Main API Function called by our Next App | |
| def diagnose_api(history: str, examination: str, image: Image.Image = None, audio_path: str = None, temp: float = 0.2, max_tokens: int = 1500): | |
| try: | |
| if image is None: | |
| # Fallback if no image is passed | |
| return "Error: Qwen-VL requires at least one image/diagnostic input to function accurately." | |
| # Re-construct the specific structured prompt our diagnostic copilot uses | |
| system_prompt = "You are a highly advanced Multi-Modal Diagnostic Co-Pilot Medical AI. Provide ## Integrated Analysis, ## Decision Making, and ## Management & Treatment Plan." | |
| user_prompt = f"History: {history}\nExamination: {examination}\nAnalyze the provided scan and history." | |
| messages = [ | |
| {"role": "system", "content": system_prompt}, | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image"}, | |
| {"type": "text", "text": user_prompt} | |
| ] | |
| } | |
| ] | |
| text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = processor( | |
| text=[text_input], | |
| images=[image], | |
| padding=True, | |
| return_tensors="pt" | |
| ).to(device) | |
| with torch.no_grad(): | |
| generated_ids = model.generate(**inputs, max_new_tokens=int(max_tokens), temperature=float(temp), top_p=0.9, do_sample=True) | |
| 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)[0] | |
| return output_text | |
| except Exception as e: | |
| return f"Model Error: {str(e)}" | |
| # 3. Create the Gradio interface | |
| # This acts as the visual UI for the HF Space, but more importantly, | |
| # exposes an API endpoint via `/api/predict` that our React app can connect to securely. | |
| demo = gr.Interface( | |
| fn=diagnose_api, | |
| inputs=[ | |
| gr.Textbox(lines=5, label="Patient History (String)", placeholder="Age, symptoms, past medical history..."), | |
| gr.Textbox(lines=5, label="Examination Findings (String)", placeholder="Vitals, systemic exam..."), | |
| gr.Image(type="pil", label="Diagnostic Scan / Image"), | |
| gr.Audio(type="filepath", label="Optional Dictation Audio", visible=False), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, label="Temperature (Creativity)"), | |
| gr.Slider(minimum=256, maximum=4096, value=1500, step=256, label="Max Output Tokens") | |
| ], | |
| outputs=gr.Markdown(label="Clinical Report Output"), | |
| title="Multi-Modal Diagnostic Co-Pilot API (Trained via Kaggle)", | |
| description="This Space hosts the fine-tuned medical vision-language model for the Diagnostic Co-Pilot ecosystem." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |