Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from dataclasses import dataclass
+import random
+from transformers import pipeline
+from huggingface_hub import InferenceClient, login
+import os
+from datetime import datetime
+import json
+from enum import Enum
+
+class PromptFormat(Enum):
+    XML = "xml"
+    JSON = "json"
+    MARKDOWN = "markdown"
+
+@dataclass
+class PatientMetadata:
+    age: int
+    smoking_status: str
+    family_history: bool
+    menopause_status: str
+    previous_mammogram: bool
+    breast_density: str
+    hormone_therapy: bool
+
+class MicrowaveBreastAnalyzer:
+    def __init__(self, hf_token: str, prompt_format: PromptFormat = PromptFormat.XML):
+        """Initialize the analyzer with models and specified prompt format."""
+        print(f"Initializing system with {prompt_format.value} prompt format...")
+        
+        # Set prompt format
+        self.prompt_format = prompt_format
+        
+        # Login to Hugging Face
+        login(token=hf_token)
+        
+        # Initialize vision pipelines for tumor detection and size classification
+        self.tumor_classifier = pipeline(
+            "image-classification",
+            model="SIATCN/vit_tumor_classifier",
+            device="cpu"
+        )
+        
+        self.size_classifier = pipeline(
+            "image-classification",
+            model="SIATCN/vit_tumor_radius_detection_finetuned",
+            device="cpu"
+        )
+        
+        # Initialize Mistral client for report generation
+        self.report_generator = InferenceClient(
+            model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+            token=hf_token
+        )
+        
+        print("Initialization complete!")
+
+    def _generate_synthetic_metadata(self) -> PatientMetadata:
+        """Generate realistic patient metadata for screening."""
+        age = random.randint(40, 75)
+        smoking_status = random.choice(["Never Smoker", "Former Smoker", "Current Smoker"])
+        family_history = random.choice([True, False])
+        menopause_status = "Post-menopausal" if age > 50 else "Pre-menopausal"
+        previous_mammogram = random.choice([True, False])
+        breast_density = random.choice([
+            "A: Almost entirely fatty", 
+            "B: Scattered fibroglandular",
+            "C: Heterogeneously dense",
+            "D: Extremely dense"
+        ])
+        hormone_therapy = random.choice([True, False])
+
+        return PatientMetadata(
+            age=age,
+            smoking_status=smoking_status,
+            family_history=family_history,
+            menopause_status=menopause_status,
+            previous_mammogram=previous_mammogram,
+            breast_density=breast_density,
+            hormone_therapy=hormone_therapy
+        )
+
+    def _process_image(self, image: Image.Image) -> Image.Image:
+        """Process input image for model consumption."""
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+        return image.resize((224, 224))
+
+    def _generate_xml_prompt(self, has_tumor: bool, tumor_size: str, metadata: PatientMetadata) -> str:
+        """Generate XML-style prompt."""
+        return f"""<s>[INST] Generate a structured medical report for a microwave breast imaging scan using the following format exactly.
+Keep sections consistent and use proper medical terminology. Be concise yet thorough.
+
+EXAMINATION PERFORMED:
+- Microwave Breast Imaging Scan
+- Date: {datetime.now().strftime('%B %d, %Y')}
+
+IMAGING FINDINGS:
+Primary Finding: {'Abnormal area detected' if has_tumor else 'No abnormalities detected'}
+{f'Detected Mass Size: {tumor_size} cm' if has_tumor else ''}
+
+PATIENT HISTORY:
+- Age: {metadata.age} years
+- Menopausal Status: {metadata.menopause_status}
+- Previous Screening: {'Yes' if metadata.previous_mammogram else 'No'}
+- Tissue Characteristics: {metadata.breast_density}
+
+RISK FACTORS:
+{f'• Family History: {"Present" if metadata.family_history else "None"}'}
+• Smoking Status: {metadata.smoking_status}
+• Hormone Therapy: {'Yes' if metadata.hormone_therapy else 'No'}
+
+Please generate a report with these exact sections:
+
+1. DETAILED FINDINGS
+[Describe the microwave imaging findings in detail, including location and characteristics of any detected abnormalities]
+
+2. INTERPRETATION
+[Provide a clear assessment of the microwave imaging results and their clinical significance]
+
+3. RECOMMENDATIONS
+[List specific follow-up actions and timeline]
+
+4. TECHNICAL NOTES
+[Include any relevant information about the scan quality and any technical considerations]
+
+Format each section consistently and maintain professional medical terminology throughout. Note that this uses microwave imaging technology, not mammography. [/INST]</s>"""
+
+    def _generate_json_prompt(self, has_tumor: bool, tumor_size: str, metadata: PatientMetadata) -> str:
+        """Generate JSON-style prompt."""
+        prompt_data = {
+            "instruction": "Generate a structured medical report for a microwave breast imaging scan",
+            "format_requirements": "Keep sections consistent and use proper medical terminology. Be concise yet thorough.",
+            "input_data": {
+                "examination": {
+                    "type": "Microwave Breast Imaging Scan",
+                    "date": datetime.now().strftime('%B %d, %Y')
+                },
+                "imaging_findings": {
+                    "primary_finding": "Abnormal area detected" if has_tumor else "No abnormalities detected",
+                    "mass_size": f"{tumor_size} cm" if has_tumor else None
+                },
+                "patient_history": {
+                    "age": metadata.age,
+                    "menopausal_status": metadata.menopause_status,
+                    "previous_screening": metadata.previous_mammogram,
+                    "tissue_characteristics": metadata.breast_density
+                },
+                "risk_factors": {
+                    "family_history": "Present" if metadata.family_history else "None",
+                    "smoking_status": metadata.smoking_status,
+                    "hormone_therapy": "Yes" if metadata.hormone_therapy else "No"
+                }
+            },
+            "required_sections": [
+                "DETAILED FINDINGS",
+                "INTERPRETATION",
+                "RECOMMENDATIONS",
+                "TECHNICAL NOTES"
+            ],
+            "section_guidelines": {
+                "DETAILED_FINDINGS": "Describe the microwave imaging findings in detail, including location and characteristics of any detected abnormalities",
+                "INTERPRETATION": "Provide a clear assessment of the microwave imaging results and their clinical significance",
+                "RECOMMENDATIONS": "List specific follow-up actions and timeline",
+                "TECHNICAL_NOTES": "Include any relevant information about the scan quality and any technical considerations"
+            }
+        }
+        
+        return f"<s>[INST] {json.dumps(prompt_data, indent=2)} [/INST]</s>"
+
+    def _generate_markdown_prompt(self, has_tumor: bool, tumor_size: str, metadata: PatientMetadata) -> str:
+        """Generate Markdown-style prompt."""
+        return f"""<s>[INST]
+# Medical Report Generation Request
+
+## Context
+Generate a structured medical report for a microwave breast imaging scan.
+
+## Current Examination Data
+* **Type:** Microwave Breast Imaging Scan
+* **Date:** {datetime.now().strftime('%B %d, %Y')}
+
+## Current Findings
+* **Primary Finding:** {"Abnormal area detected" if has_tumor else "No abnormalities detected"}
+* **Mass Size:** {f"{tumor_size} cm" if has_tumor else "N/A"}
+
+## Patient Information
+* **Age:** {metadata.age} years
+* **Menopausal Status:** {metadata.menopause_status}
+* **Previous Screening:** {"Yes" if metadata.previous_mammogram else "No"}
+* **Tissue Characteristics:** {metadata.breast_density}
+
+## Risk Assessment
+* **Family History:** {"Present" if metadata.family_history else "None"}
+* **Smoking Status:** {metadata.smoking_status}
+* **Hormone Therapy:** {"Yes" if metadata.hormone_therapy else "No"}
+
+## Required Report Sections
+1. **Detailed Findings**
+   - Include location and characteristics of any detected abnormalities
+2. **Interpretation**
+   - Assess microwave imaging results and clinical significance
+3. **Recommendations**
+   - Specify follow-up actions and timeline
+4. **Technical Notes**
+   - Document scan quality and technical considerations
+
+Please maintain professional medical terminology throughout the report.
+[/INST]</s>"""
+
+    def _generate_medical_report(self, has_tumor: bool, tumor_size: str, metadata: PatientMetadata) -> str:
+        """Generate a standardized report for microwave breast imaging."""
+        # Select prompt format based on configuration
+        if self.prompt_format == PromptFormat.XML:
+            prompt = self._generate_xml_prompt(has_tumor, tumor_size, metadata)
+        elif self.prompt_format == PromptFormat.JSON:
+            prompt = self._generate_json_prompt(has_tumor, tumor_size, metadata)
+        else:  # PromptFormat.MARKDOWN
+            prompt = self._generate_markdown_prompt(has_tumor, tumor_size, metadata)
+
+        # Generate response using Mistral
+        response = self.report_generator.text_generation(
+            prompt,
+            max_new_tokens=800,
+            temperature=0.3,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            do_sample=True,
+            seed=42
+        )
+            
+        # Post-process the response to ensure consistent formatting
+        formatted_response = f"""MICROWAVE BREAST IMAGING REPORT
+Date: {datetime.now().strftime('%B %d, %Y')}
+----------------------------------------
+
+{response.strip()}
+
+----------------------------------------
+NOTE: This report was generated using AI assistance and should be reviewed by a qualified healthcare professional.
+This screening was performed using microwave imaging technology."""
+
+        return formatted_response
+
+    def analyze(self, image: Image.Image) -> str:
+        """Main analysis pipeline with standardized output."""
+        try:
+            processed_image = self._process_image(image)
+            metadata = self._generate_synthetic_metadata()
+
+            # Detect tumor
+            tumor_result = self.tumor_classifier(processed_image)
+            has_tumor = tumor_result[0]['label'] == 'tumor'
+            tumor_confidence = tumor_result[0]['score']
+
+            # Measure size if tumor detected
+            size_result = self.size_classifier(processed_image)
+            tumor_size = size_result[0]['label'].replace('tumor-', '')
+
+            # Generate report
+            report = self._generate_medical_report(has_tumor, tumor_size, metadata)
+
+            return f"""MICROWAVE BREAST IMAGING ANALYSIS
+========================================
+
+INITIAL SCAN ASSESSMENT:
+{'⚠️ ABNORMAL FINDING DETECTED' if has_tumor else '✓ NO ABNORMALITIES DETECTED'}
+Detection Confidence: {tumor_confidence:.2%}
+{f'Estimated Mass Size: {tumor_size} cm' if has_tumor else ''}
+
+----------------------------------------
+{report}"""
+
+        except Exception as e:
+            import traceback
+            return f"Error during analysis: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+
+def create_interface(hf_token: str, prompt_format: PromptFormat = PromptFormat.XML) -> gr.Interface:
+    """Create the Gradio interface."""
+    analyzer = MicrowaveBreastAnalyzer(hf_token, prompt_format)
+    
+    interface = gr.Interface(
+        fn=analyzer.analyze,
+        inputs=[
+            gr.Image(type="pil", label="Upload Microwave Breast Image for Analysis")
+        ],
+        outputs=[
+            gr.Textbox(label="Analysis Results", lines=20)
+        ],
+        title=f"Microwave Breast Imaging Analysis System ({prompt_format.value.upper()} Format)",
+        description="""Upload a microwave breast image for comprehensive analysis. The system will:
+        1. Detect the presence of tumors using microwave imaging technology
+        2. Classify tumor size if present
+        3. Generate a detailed medical report with recommendations
+        
+        Note: This system uses microwave imaging technology for breast screening, which offers a safe, 
+        radiation-free alternative to traditional mammography.""",
+    )
+    
+    return interface
+
+if __name__ == "__main__":
+    print("Starting microwave breast imaging analysis system...")
+    # Load HuggingFace token from secrets
+    HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
+    if not HF_TOKEN:
+        raise ValueError("Please set HUGGINGFACE_TOKEN environment variable")
+    
+    # Create interfaces for different formats
+    interface_xml = create_interface(HF_TOKEN, PromptFormat.XML)
+    interface_json = create_interface(HF_TOKEN, PromptFormat.JSON)
+    interface_markdown = create_interface(HF_TOKEN, PromptFormat.MARKDOWN)
+    
+    # Launch the XML version by default
+    interface_xml.launch(
+        debug=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )