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| import gradio as gr | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| def load_model(): | |
| print("Loading model...") | |
| # Load the base model with 4-bit quantization | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| "unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit", | |
| device_map="auto", | |
| torch_dtype="auto" | |
| ) | |
| # Load the fine-tuned adapter | |
| model = PeftModel.from_pretrained( | |
| base_model, | |
| "LAWSA07/medical_fine_tuned_deepseekR1" | |
| ) | |
| # Load tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit" | |
| ) | |
| print("Model loaded successfully!") | |
| return model, tokenizer | |
| # Load model and tokenizer | |
| model, tokenizer = load_model() | |
| def generate_response(question, max_length=512, temperature=0.7): | |
| """Generate a response to a medical question.""" | |
| if not question.strip(): | |
| return "Please enter a question about your symptoms." | |
| # Create prompt | |
| prompt = f"Question: {question}\nAnswer:" | |
| # Generate response | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| inputs.input_ids, | |
| max_new_tokens=max_length, | |
| temperature=temperature, | |
| do_sample=True, | |
| top_p=0.95, | |
| repetition_penalty=1.1, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) | |
| return response.strip() | |
| # Create Gradio interface | |
| with gr.Blocks(title="Medical Symptoms Assistant") as app: | |
| gr.Markdown(""" | |
| # Medical Symptoms Assistant | |
| Ask questions about your symptoms and get AI-generated responses. | |
| **Note**: This is for informational purposes only and not a substitute for professional medical advice. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| question_input = gr.Textbox( | |
| label="Describe your symptoms or ask a medical question", | |
| placeholder="I've been experiencing headaches and dizziness for the past week...", | |
| lines=4 | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Get Response", variant="primary") | |
| clear_btn = gr.Button("Clear") | |
| with gr.Accordion("Advanced Options", open=False): | |
| max_length_slider = gr.Slider( | |
| minimum=64, maximum=1024, value=512, step=32, | |
| label="Maximum Response Length" | |
| ) | |
| temperature_slider = gr.Slider( | |
| minimum=0.1, maximum=1.5, value=0.7, step=0.1, | |
| label="Temperature (Creativity)" | |
| ) | |
| with gr.Column(): | |
| response_output = gr.Textbox( | |
| label="AI Response", | |
| lines=12, | |
| interactive=False | |
| ) | |
| disclaimer = gr.Markdown(""" | |
| **Disclaimer**: This AI assistant provides information based on its training. | |
| It is not a substitute for professional medical advice, diagnosis, or treatment. | |
| Always seek the advice of your physician or other qualified health provider with any | |
| questions you may have regarding a medical condition. | |
| """) | |
| # Set up event handlers | |
| submit_btn.click( | |
| generate_response, | |
| inputs=[question_input, max_length_slider, temperature_slider], | |
| outputs=response_output | |
| ) | |
| question_input.submit( | |
| generate_response, | |
| inputs=[question_input, max_length_slider, temperature_slider], | |
| outputs=response_output | |
| ) | |
| clear_btn.click( | |
| lambda: ("", None), | |
| inputs=None, | |
| outputs=[question_input, response_output] | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| import torch # Import torch here to avoid issues | |
| app.launch(share=True) # set share=False if you don't want a public link |