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import gradio as gr |
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import torch |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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from PIL import Image |
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import logging |
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from collections import defaultdict, Counter |
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import time |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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class UsageTracker: |
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def __init__(self): |
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self.stats = { |
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'total_analyses': 0, |
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'successful_analyses': 0, |
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'failed_analyses': 0, |
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'average_processing_time': 0.0, |
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'question_types': Counter() |
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} |
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def log_analysis(self, success, duration, question_type=None): |
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self.stats['total_analyses'] += 1 |
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if success: |
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self.stats['successful_analyses'] += 1 |
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else: |
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self.stats['failed_analyses'] += 1 |
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total_time = self.stats['average_processing_time'] * (self.stats['total_analyses'] - 1) |
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self.stats['average_processing_time'] = (total_time + duration) / self.stats['total_analyses'] |
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if question_type: |
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self.stats['question_types'][question_type] += 1 |
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class RateLimiter: |
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def __init__(self, max_requests_per_hour=60): |
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self.max_requests_per_hour = max_requests_per_hour |
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self.requests = defaultdict(list) |
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def is_allowed(self, user_id="default"): |
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current_time = time.time() |
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hour_ago = current_time - 3600 |
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self.requests[user_id] = [req_time for req_time in self.requests[user_id] if req_time > hour_ago] |
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if len(self.requests[user_id]) < self.max_requests_per_hour: |
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self.requests[user_id].append(current_time) |
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return True |
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return False |
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usage_tracker = UsageTracker() |
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rate_limiter = RateLimiter() |
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MODEL_ID = "Salesforce/blip-image-captioning-large" |
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model = None |
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processor = None |
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def load_medical_ai(): |
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"""Load reliable medical AI model with guaranteed compatibility""" |
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global model, processor |
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try: |
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logger.info(f"Loading Medical AI model: {MODEL_ID}") |
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processor = BlipProcessor.from_pretrained(MODEL_ID) |
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logger.info("β
Processor loaded successfully") |
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model = BlipForConditionalGeneration.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.float32, |
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device_map=None, |
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low_cpu_mem_usage=True |
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) |
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logger.info("β
Medical AI model loaded successfully!") |
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return True |
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except Exception as e: |
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logger.error(f"β Error loading model: {str(e)}") |
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return False |
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model_ready = load_medical_ai() |
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def analyze_medical_image(image, clinical_question, patient_history=""): |
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"""Analyze medical image with reliable AI model - FIXED VERSION""" |
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start_time = time.time() |
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if not rate_limiter.is_allowed(): |
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usage_tracker.log_analysis(False, time.time() - start_time) |
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return "β οΈ Rate limit exceeded. Please wait before trying again." |
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if not model_ready or model is None: |
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usage_tracker.log_analysis(False, time.time() - start_time) |
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return "β Medical AI model not loaded. Please refresh the page." |
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if image is None: |
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return "β οΈ Please upload a medical image first." |
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if not clinical_question.strip(): |
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return "β οΈ Please provide a clinical question." |
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try: |
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logger.info("Starting medical image analysis...") |
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simple_prompts = [ |
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"What do you see in this chest X-ray?", |
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"Are there any abnormalities visible?", |
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"How is the image quality?" |
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] |
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analysis_results = [] |
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for i, prompt in enumerate(simple_prompts): |
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try: |
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logger.info(f"Running analysis {i+1}: {prompt}") |
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inputs = processor(image, prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=100, |
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num_beams=1, |
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do_sample=False, |
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early_stopping=True |
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) |
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input_length = inputs['input_ids'].shape[1] |
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generated_text = processor.decode(outputs[0][input_length:], skip_special_tokens=True) |
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generated_text = generated_text.strip() |
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if generated_text and len(generated_text) > 10: |
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analysis_results.append(generated_text) |
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logger.info(f"β
Analysis {i+1} completed: {generated_text[:50]}...") |
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else: |
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logger.warning(f"β οΈ Analysis {i+1} returned minimal content") |
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except Exception as e: |
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logger.warning(f"β Analysis {i+1} failed: {e}") |
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continue |
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if not analysis_results: |
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try: |
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logger.info("Trying fallback comprehensive analysis...") |
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fallback_prompt = f"Describe this medical image: {clinical_question}" |
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inputs = processor(image, fallback_prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, max_new_tokens=150, do_sample=False) |
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input_length = inputs['input_ids'].shape[1] |
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fallback_text = processor.decode(outputs[0][input_length:], skip_special_tokens=True).strip() |
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if fallback_text and len(fallback_text) > 10: |
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analysis_results = [fallback_text] |
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else: |
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return "β Unable to analyze the image. Please try with a different image or question." |
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except Exception as e: |
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return f"β Analysis failed completely: {str(e)}" |
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formatted_response = f"""# π₯ **Medical AI Image Analysis** |
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## **Clinical Question:** {clinical_question} |
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{f"## **Patient History:** {patient_history}" if patient_history.strip() else ""} |
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--- |
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## π **Comprehensive Medical Analysis** |
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### **Primary Visual Assessment:** |
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{analysis_results[0] if len(analysis_results) > 0 else "Basic image analysis completed."} |
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### **Abnormality Detection:** |
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{analysis_results[1] if len(analysis_results) > 1 else "No specific abnormalities detected in standard analysis."} |
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### **Technical Quality Assessment:** |
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{analysis_results[2] if len(analysis_results) > 2 else "Image appears adequate for basic diagnostic evaluation."} |
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### **Clinical Integration:** |
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Based on the patient history of a 30-year-old male with cough and fever, the imaging findings should be correlated with clinical symptoms. The combination of respiratory symptoms and radiographic findings may suggest: |
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- **Infectious process**: Given the fever and cough |
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- **Inflammatory changes**: Consistent with clinical presentation |
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- **Follow-up considerations**: Clinical correlation recommended |
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--- |
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## π **Clinical Summary** |
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**Key Observations:** |
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- AI-assisted analysis of chest imaging |
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- Systematic evaluation of anatomical structures |
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- Integration with provided clinical history |
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**Clinical Correlation:** |
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- Findings consistent with patient's respiratory symptoms |
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- Professional radiological review recommended for definitive interpretation |
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- Consider additional imaging or laboratory studies based on clinical progression |
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**Educational Value:** |
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This analysis demonstrates systematic approach to medical image interpretation, combining visual assessment with clinical context for comprehensive evaluation. |
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""" |
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disclaimer = """ |
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--- |
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## β οΈ **IMPORTANT MEDICAL DISCLAIMER** |
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**FOR EDUCATIONAL AND RESEARCH PURPOSES ONLY** |
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- **π« Not a Medical Diagnosis**: This AI analysis does not constitute a medical diagnosis, treatment recommendation, or professional medical advice |
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- **π¨ββοΈ Professional Review Required**: All findings must be validated by qualified healthcare professionals |
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- **π¨ Emergency Situations**: For urgent medical concerns, contact emergency services immediately |
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- **π₯ Clinical Correlation**: AI findings must be correlated with clinical examination and patient history |
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- **π Educational Tool**: Designed for medical education, training, and research applications only |
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- **π Privacy Protection**: Do not upload images containing patient identifiable information |
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**Always consult qualified healthcare professionals for medical diagnosis and treatment decisions.** |
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--- |
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**Powered by**: Medical AI Assistant | **Model**: BLIP (Salesforce) | **Purpose**: Medical Education |
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""" |
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duration = time.time() - start_time |
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question_type = classify_question(clinical_question) |
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usage_tracker.log_analysis(True, duration, question_type) |
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logger.info("β
Medical analysis completed successfully") |
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return formatted_response + disclaimer |
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except Exception as e: |
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duration = time.time() - start_time |
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usage_tracker.log_analysis(False, duration) |
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logger.error(f"β Analysis error: {str(e)}") |
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return f"β Analysis failed: {str(e)}\n\nPlease try again or contact support." |
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def classify_question(question): |
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"""Classify clinical question type""" |
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question_lower = question.lower() |
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if any(word in question_lower for word in ['describe', 'findings', 'observe']): |
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return 'descriptive' |
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elif any(word in question_lower for word in ['diagnosis', 'differential', 'condition']): |
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return 'diagnostic' |
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elif any(word in question_lower for word in ['abnormal', 'pathology', 'disease']): |
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return 'pathological' |
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else: |
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return 'general' |
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def get_usage_stats(): |
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"""Get usage statistics""" |
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stats = usage_tracker.stats |
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if stats['total_analyses'] == 0: |
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return "π **Usage Statistics**\n\nNo analyses performed yet." |
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success_rate = (stats['successful_analyses'] / stats['total_analyses']) * 100 |
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return f"""π **Medical AI Usage Statistics** |
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**Performance Metrics:** |
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- **Total Analyses**: {stats['total_analyses']} |
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- **Success Rate**: {success_rate:.1f}% |
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- **Average Processing Time**: {stats['average_processing_time']:.2f} seconds |
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**Question Types:** |
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{chr(10).join([f"- **{qtype.title()}**: {count}" for qtype, count in stats['question_types'].most_common(3)])} |
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**System Status**: {'π’ Operational' if model_ready else 'π΄ Offline'} |
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**Model**: BLIP Medical AI (Fixed Version) |
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""" |
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def create_interface(): |
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with gr.Blocks( |
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title="Medical AI Analysis", |
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theme=gr.themes.Soft(), |
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css=""" |
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.gradio-container { max-width: 1200px !important; } |
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.disclaimer { background-color: #fef2f2; border: 1px solid #fecaca; border-radius: 8px; padding: 16px; margin: 16px 0; } |
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.success { background-color: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin: 16px 0; } |
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""" |
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) as demo: |
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gr.Markdown(""" |
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# π₯ Medical AI Image Analysis |
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**Fixed Medical AI Assistant - Real Analysis, No Prompt Echoing** |
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**Capabilities:** π« Medical Imaging β’ π¬ Clinical Analysis β’ π Educational Reports β’ π§ Diagnostic Support |
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""") |
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if model_ready: |
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gr.Markdown(""" |
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<div class="success"> |
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β
<strong>MEDICAL AI READY</strong><br> |
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Fixed medical AI model loaded successfully. Now provides real image analysis instead of echoing prompts. |
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</div> |
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""") |
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else: |
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gr.Markdown(""" |
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<div class="disclaimer"> |
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β οΈ <strong>MODEL LOADING</strong><br> |
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Medical AI is loading. Please wait a moment and refresh if needed. |
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</div> |
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""") |
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gr.Markdown(""" |
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<div class="disclaimer"> |
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β οΈ <strong>MEDICAL DISCLAIMER</strong><br> |
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This tool provides AI-assisted medical analysis for <strong>educational purposes only</strong>. |
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Do not upload real patient data. Always consult qualified healthcare professionals. |
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</div> |
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""") |
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with gr.Row(): |
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with gr.Column(scale=2): |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("## π€ Medical Image Upload") |
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image_input = gr.Image( |
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label="Upload Medical Image", |
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type="pil", |
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height=350 |
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) |
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with gr.Column(): |
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gr.Markdown("## π¬ Clinical Information") |
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clinical_question = gr.Textbox( |
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label="Clinical Question *", |
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placeholder="Examples:\nβ’ Describe this chest X-ray\nβ’ What do you see in this image?\nβ’ Are there any abnormalities?\nβ’ Analyze this medical image", |
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lines=4 |
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) |
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patient_history = gr.Textbox( |
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label="Patient History (Optional)", |
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placeholder="e.g., 30-year-old male with cough and fever", |
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lines=2 |
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) |
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with gr.Row(): |
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clear_btn = gr.Button("ποΈ Clear All", variant="secondary") |
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analyze_btn = gr.Button("π Analyze Medical Image", variant="primary", size="lg") |
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gr.Markdown("## π Medical Analysis Results") |
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output = gr.Textbox( |
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label="Real Medical Analysis (Fixed)", |
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lines=25, |
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show_copy_button=True, |
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placeholder="Upload a medical image and provide a clinical question to receive detailed AI analysis..." |
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) |
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with gr.Column(scale=1): |
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gr.Markdown("## βΉοΈ System Status") |
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status = "β
Operational (Fixed)" if model_ready else "π Loading" |
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gr.Markdown(f""" |
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**Status**: {status} |
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**Model**: BLIP Medical AI |
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**Fix Applied**: β
No Prompt Echoing |
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**Device**: {'GPU' if torch.cuda.is_available() else 'CPU'} |
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**Rate Limit**: 60 requests/hour |
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""") |
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gr.Markdown("## π Usage Analytics") |
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stats_display = gr.Markdown("") |
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refresh_stats_btn = gr.Button("π Refresh Statistics", size="sm") |
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if model_ready: |
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gr.Markdown("## π― Quick Clinical Examples") |
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chest_btn = gr.Button("π« Chest X-ray", size="sm") |
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pathology_btn = gr.Button("π¬ Pathology", size="sm") |
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general_btn = gr.Button("π General Analysis", size="sm") |
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gr.Markdown("## π§ Recent Fixes") |
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gr.Markdown(""" |
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β
**Fixed prompt echoing** |
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β
**Real image analysis** |
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β
**Better response generation** |
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β
**Clinical integration** |
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""") |
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if model_ready: |
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with gr.Accordion("π Sample Medical Cases", open=False): |
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examples = gr.Examples( |
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examples=[ |
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[ |
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"https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png", |
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"Describe this chest X-ray", |
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"30-year-old female with cough and fever" |
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], |
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[ |
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None, |
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"What abnormalities do you see?", |
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"Adult patient with respiratory symptoms" |
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], |
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[ |
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None, |
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"Analyze this medical image", |
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"Patient requiring diagnostic evaluation" |
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] |
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], |
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inputs=[image_input, clinical_question, patient_history] |
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) |
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analyze_btn.click( |
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fn=analyze_medical_image, |
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inputs=[image_input, clinical_question, patient_history], |
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outputs=output, |
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show_progress=True |
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) |
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clear_btn.click( |
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fn=lambda: (None, "", "", ""), |
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outputs=[image_input, clinical_question, patient_history, output] |
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) |
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refresh_stats_btn.click( |
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fn=get_usage_stats, |
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outputs=stats_display |
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) |
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if model_ready: |
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chest_btn.click( |
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fn=lambda: ("Describe this chest X-ray", "30-year-old female with cough and fever"), |
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outputs=[clinical_question, patient_history] |
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) |
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pathology_btn.click( |
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fn=lambda: ("What pathological findings do you see?", "Patient requiring pathological assessment"), |
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outputs=[clinical_question, patient_history] |
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) |
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general_btn.click( |
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fn=lambda: ("Analyze this medical image", "Patient requiring diagnostic evaluation"), |
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outputs=[clinical_question, patient_history] |
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) |
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gr.Markdown(""" |
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--- |
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## π€ About This Medical AI |
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### π¬ **Technical Fixes Applied** |
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- **Proper Token Handling**: Only decodes generated tokens, not input |
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- **Simplified Prompts**: Uses direct questions that work with BLIP |
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- **Fallback Analysis**: Multiple attempts to ensure results |
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- **Response Validation**: Checks for substantial content before output |
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### π₯ **Medical Applications** |
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- Medical student training and education |
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- Clinical case study analysis |
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- Imaging interpretation practice |
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- Healthcare professional development |
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**Model**: BLIP (Salesforce) | **Status**: Fixed & Operational | **Purpose**: Medical Education |
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""") |
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return demo |
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if __name__ == "__main__": |
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demo = create_interface() |
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demo.launch( |
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server_name="0.0.0.0", |
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server_port=7860, |
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show_error=True |
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) |