import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import logging from typing import List, Dict import gc import os # Setup logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Set random seed for reproducibility torch.random.manual_seed(0) class HealthAssistant: def __init__(self): self.model_id = "microsoft/Phi-3-small-128k-instruct" self.model = None self.tokenizer = None self.pipe = None self.metrics = [] self.medications = [] self.initialize_model() def initialize_model(self): try: logger.info(f"Loading model: {self.model_id}") # Initialize tokenizer self.tokenizer = AutoTokenizer.from_pretrained( self.model_id, trust_remote_code=True ) logger.info("Tokenizer loaded") # Initialize model self.model = AutoModelForCausalLM.from_pretrained( self.model_id, torch_dtype="auto", trust_remote_code=True ) # Set device self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model = self.model.to(self.device) logger.info(f"Model loaded on {self.device}") # Setup pipeline self.pipe = pipeline( "text-generation", model=self.model, tokenizer=self.tokenizer, device=self.device ) logger.info("Pipeline created successfully") return True except Exception as e: logger.error(f"Error in model initialization: {str(e)}") raise def _prepare_prompt(self, message: str, history: List = None) -> str: """Prepare prompt with context and history""" prompt_parts = [ "You are a medical AI assistant providing healthcare information and guidance.", "Always be professional and include appropriate medical disclaimers.", "\nCurrent Health Information:", self._get_health_context(), "\nConversation:" ] if history: for prev_msg, prev_response in history[-3:]: prompt_parts.extend([ f"Human: {prev_msg}", f"Assistant: {prev_response}" ]) prompt_parts.extend([ f"Human: {message}", "Assistant:" ]) return "\n".join(prompt_parts) def generate_response(self, message: str, history: List = None) -> str: try: # Prepare prompt prompt = self._prepare_prompt(message, history) # Generation configuration generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.7, "do_sample": True, "top_k": 50, "top_p": 0.9, "repetition_penalty": 1.1 } # Generate response output = self.pipe(prompt, **generation_args) response = output[0]['generated_text'] # Cleanup gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() return response.strip() except Exception as e: logger.error(f"Error generating response: {str(e)}") return "I apologize, but I encountered an error. Please try again." def _get_health_context(self) -> str: context_parts = [] if self.metrics: latest = self.metrics[-1] context_parts.extend([ "Recent Health Metrics:", f"- Weight: {latest.get('Weight', 'N/A')} kg", f"- Steps: {latest.get('Steps', 'N/A')}", f"- Sleep: {latest.get('Sleep', 'N/A')} hours" ]) if self.medications: context_parts.append("\nCurrent Medications:") for med in self.medications: med_info = f"- {med['Medication']} ({med['Dosage']}) at {med['Time']}" if med.get('Notes'): med_info += f" | Note: {med['Notes']}" context_parts.append(med_info) return "\n".join(context_parts) if context_parts else "No health data recorded" def add_metrics(self, weight: float, steps: int, sleep: float) -> bool: try: self.metrics.append({ 'Weight': weight, 'Steps': steps, 'Sleep': sleep }) return True except Exception as e: logger.error(f"Error adding metrics: {e}") return False def add_medication(self, name: str, dosage: str, time: str, notes: str = "") -> bool: try: self.medications.append({ 'Medication': name, 'Dosage': dosage, 'Time': time, 'Notes': notes }) return True except Exception as e: logger.error(f"Error adding medication: {e}") return False class GradioInterface: def __init__(self): try: logger.info("Initializing Health Assistant...") self.assistant = HealthAssistant() logger.info("Health Assistant initialized successfully") except Exception as e: logger.error(f"Failed to initialize Health Assistant: {e}") raise def chat_response(self, message: str, history: List) -> tuple: if not message.strip(): return "", history response = self.assistant.generate_response(message, history) history.append([message, response]) return "", history def add_health_metrics(self, weight: float, steps: int, sleep: float) -> str: if not all([weight is not None, steps is not None, sleep is not None]): return "⚠️ Please fill in all metrics." if weight <= 0 or steps < 0 or sleep < 0: return "⚠️ Please enter valid positive numbers." if self.assistant.add_metrics(weight, steps, sleep): return f"""✅ Health metrics saved successfully! • Weight: {weight} kg • Steps: {steps} • Sleep: {sleep} hours""" return "❌ Error saving metrics." def add_medication_info(self, name: str, dosage: str, time: str, notes: str) -> str: if not all([name, dosage, time]): return "⚠️ Please fill in all required fields." if self.assistant.add_medication(name, dosage, time, notes): return f"""✅ Medication added successfully! • Medication: {name} • Dosage: {dosage} • Time: {time} • Notes: {notes if notes else 'None'}""" return "❌ Error adding medication." def create_interface(self): with gr.Blocks(title="Medical Health Assistant") as demo: gr.Markdown(""" # 🏥 Medical Health Assistant This AI assistant provides medical information and health guidance. **Note**: This is not a replacement for professional medical advice. """) with gr.Tabs(): # Chat Interface with gr.Tab("💬 Medical Consultation"): chatbot = gr.Chatbot( value=[], height=450, show_label=False ) with gr.Row(): msg = gr.Textbox( placeholder="Describe your medical concern... (Press Enter)", lines=2, show_label=False, scale=9 ) send_btn = gr.Button("Send", scale=1) clear_btn = gr.Button("Clear Chat") # Health Metrics with gr.Tab("📊 Health Metrics"): with gr.Row(): with gr.Column(): gr.Markdown("### Enter Your Health Metrics") weight_input = gr.Number( label="Weight (kg)", minimum=0, maximum=500 ) steps_input = gr.Number( label="Steps", minimum=0, maximum=100000 ) sleep_input = gr.Number( label="Hours Slept", minimum=0, maximum=24 ) metrics_btn = gr.Button("Save Metrics") metrics_status = gr.Markdown() # Medication Manager with gr.Tab("💊 Medication Manager"): with gr.Row(): with gr.Column(): gr.Markdown("### Add Medication Details") med_name = gr.Textbox( label="Medication Name", placeholder="Enter medication name" ) med_dosage = gr.Textbox( label="Dosage", placeholder="e.g., 500mg" ) med_time = gr.Textbox( label="Time", placeholder="e.g., 9:00 AM" ) med_notes = gr.Textbox( label="Notes (optional)", placeholder="Additional instructions or notes" ) med_btn = gr.Button("Add Medication") med_status = gr.Markdown() # Event handlers msg.submit(self.chat_response, [msg, chatbot], [msg, chatbot]) send_btn.click(self.chat_response, [msg, chatbot], [msg, chatbot]) clear_btn.click(lambda: [], None, chatbot) metrics_btn.click( self.add_health_metrics, inputs=[weight_input, steps_input, sleep_input], outputs=[metrics_status] ) med_btn.click( self.add_medication_info, inputs=[med_name, med_dosage, med_time, med_notes], outputs=[med_status] ) gr.Markdown(""" ### ⚠️ Important Medical Disclaimer This AI assistant provides general health information only. - Not a replacement for professional medical advice - Always consult healthcare professionals for medical decisions - Seek immediate medical attention for emergencies """) demo.queue() return demo def main(): try: logger.info("Starting Medical Health Assistant...") interface = GradioInterface() demo = interface.create_interface() logger.info("Launching interface...") demo.launch( server_name="0.0.0.0", server_port=7860, share=False ) except Exception as e: logger.error(f"Error starting application: {e}") raise if __name__ == "__main__": main()