# -*- coding: utf-8 -*- import streamlit as st import google.generativeai as genai import os from PIL import Image import io # Needed for handling image bytes from typing import Optional, Tuple, Any # For type hinting # --- Page Configuration (MUST BE THE FIRST STREAMLIT COMMAND) --- st.set_page_config( page_title="AI Clinical Support Demonstrator", layout="wide", initial_sidebar_state="expanded" ) # --- Introductory Explanation --- st.markdown( """ ### Welcome to the AI Clinical Support Demonstrator This application demonstrates how Generative AI (Google Gemini) can be guided to assist with analyzing clinical information. - **Agentic Text Analysis:** Simulates a structured reasoning process on clinical text. - **Medical Image Analysis:** Provides descriptive observations of potential anomalies in medical images. **Crucially, this tool is for demonstration purposes ONLY. It does NOT provide medical advice or diagnosis.** """ ) st.markdown("---") # Visual separator # --- Configuration and Initialization --- # Securely load API key (Secrets > Env Var) GEMINI_API_KEY = st.secrets.get("GEMINI_API_KEY", os.environ.get("GEMINI_API_KEY")) # Configure Gemini Client genai_client_configured = False if GEMINI_API_KEY: try: genai.configure(api_key=GEMINI_API_KEY) genai_client_configured = True except Exception as e: st.error(f"Fatal Error: Failed to configure Google Generative AI. Check API Key. Details: {e}", icon="🚨") st.stop() else: st.error("⚠️ Gemini API Key not found. Please configure `GEMINI_API_KEY` in Streamlit secrets or environment variables.", icon="🔑") st.stop() # Initialize models using Session State for persistence across reruns TEXT_MODEL_NAME = 'gemini-1.5-pro-latest' # For agentic text reasoning VISION_MODEL_NAME = 'gemini-1.5-flash' # For image analysis (or 'gemini-pro-vision') if 'models_initialized' not in st.session_state: st.session_state.models_initialized = False st.session_state.text_model = None st.session_state.vision_model = None if genai_client_configured and not st.session_state.models_initialized: try: st.session_state.text_model = genai.GenerativeModel(TEXT_MODEL_NAME) st.session_state.vision_model = genai.GenerativeModel(VISION_MODEL_NAME) st.session_state.models_initialized = True except Exception as e: st.error(f"Fatal Error: Failed to initialize Gemini models. Text: {TEXT_MODEL_NAME}, Vision: {VISION_MODEL_NAME}. Details: {e}", icon="💥") st.stop() elif not genai_client_configured: st.error("AI Models could not be initialized due to configuration issues.", icon="🚫") st.stop() # --- Core AI Interaction Functions --- # AGENTIC prompt for Text Analysis (Remains the same as previous good version) AGENTIC_TEXT_ANALYSIS_PROMPT_TEMPLATE = """ **Simulated Clinical Reasoning Agent Task:** **Role:** AI assistant simulating an agentic clinical reasoning process to support a healthcare professional by structuring information, generating possibilities, and suggesting investigation pathways based *strictly* on the provided text. **This is NOT a diagnosis.** **Input Data:** Unstructured clinical information (e.g., symptoms, history, basic findings). **Output Format:** Please structure your response using Markdown headings for each step (e.g., `## 1. Information Extraction`). **Simulated Agentic Steps (Perform sequentially):** 1. **## 1. Information Extraction & Structuring:** * Key Demographics (Age, Sex if provided). * Primary Symptoms/Signs. * Relevant Medical History. * Pertinent Negatives (if mentioned). 2. **## 2. Differential Considerations Generation:** * Based *only* on Step 1, list **potential differential considerations** (possible conditions). * **Use cautious language:** "could be consistent with," "warrants consideration," "less likely but possible." **AVOID definitive statements.** * Briefly justify each consideration based on findings. 3. **## 3. Information Gap Analysis:** * Identify critical missing information (e.g., lab results, imaging, exam specifics, duration/onset). 4. **## 4. Suggested Next Steps for Investigation (for Clinician):** * Propose logical next steps a **healthcare professional might consider**. * Categorize (e.g., Further History, Exam Points, Labs, Imaging). * Frame as *suggestions* (e.g., "Consider ordering...", "Assessment of X may be informative"). 5. **## 5. Mandatory Disclaimer:** Conclude with: "This AI-generated analysis is for informational support only. It is **NOT** a diagnosis and cannot replace the judgment of a qualified healthcare professional." **Input Clinical Information:** --- {text_input} --- **Agentic Analysis:** """ # REFINED prompt for Image Analysis - More explicit instructions based on good example IMAGE_ANALYSIS_PROMPT_TEMPLATE = """ **Medical Image Analysis Request:** **Context:** Analyze the provided medical image objectively based *only* on visual information. User may provide additional context or questions. **Output Format:** Structure your response precisely using the following Markdown headings. Be factual and descriptive. **Task:** 1. **## 1. Visible Structures:** * Identify the likely imaging modality and view (e.g., PA Chest Radiograph, Axial CT slice of the abdomen). * Briefly list the main anatomical structures clearly visible (e.g., ribs, heart silhouette, lung fields, diaphragm). 2. **## 2. Identify Potential Anomalies / Key Findings:** * Carefully examine the image for any areas that *appear* abnormal or deviate significantly from typical presentation. * **Use extremely cautious, descriptive language.** Describe *what* you see (e.g., "area of increased opacity," "region of lucency," "asymmetry observed in X," "potential contour abnormality," "patchy distribution"). * **Specify location accurately** using standard anatomical terms (e.g., "right lower lung zone," "left hilum," "hepatic flexure region"). * **Crucially, AVOID interpretive or diagnostic terms** (DO NOT use words like "pneumonia," "tumor," "fracture," "infection," "inflammation"). Stick strictly to visual observation. * If relevant and clearly discernible, mention the **absence** of certain major expected abnormalities (pertinent negatives, e.g., "No obvious large pneumothorax identified," "Bowel gas pattern appears unremarkable in visualized areas"). * Compare sides if applicable and relevant differences are seen (e.g., "Left lung field demonstrates greater transparency compared to the right"). 3. **## 3. Correlate with User Prompt (if provided):** * Address specific user questions based *strictly* on the visual information identifiable in the image. * If the image cannot visually answer the question (e.g., requires clinical context, different view), state that clearly. * If no user prompt was provided, state "N/A". 4. **## 4. Limitations of this AI Analysis:** * **Explicitly list the following limitations inherent to this analysis:** * Dependency on the **quality, resolution, and potential artifacts** of the provided image. * Analysis is restricted to the **single view/slice(s)** provided; other areas are not assessed. * **Complete lack of clinical context:** Patient history, symptoms, physical exam findings, and laboratory results are unknown and not considered. * **Absence of prior imaging studies:** Comparisons over time are not possible, which is often crucial for interpretation. * The AI functions purely on **visual pattern recognition**; it does not perform clinical reasoning or differential diagnosis. 5. **## 5. Mandatory Disclaimer:** * State clearly: This is an AI-generated visual analysis intended for informational and demonstration purposes **ONLY**. * It is **NOT** a radiological interpretation or medical diagnosis. * It **CANNOT** substitute for a comprehensive evaluation and interpretation by a qualified radiologist or physician integrating full clinical information. * Any potential observations noted herein **MUST** be correlated with clinical findings and reviewed/confirmed by qualified healthcare professionals. **User's Additional Context/Question (if any):** --- {user_prompt} --- **Image Analysis:** """ # --- Backend Functions --- def run_agentic_text_analysis(text_input: str) -> Tuple[Optional[str], Optional[str]]: """Sends clinical text to the configured text model for simulated agentic analysis.""" if not text_input or not text_input.strip(): return None, "Input text cannot be empty." if not st.session_state.models_initialized or not st.session_state.text_model: return None, "Text analysis model not initialized. Please refresh or check configuration." try: prompt = AGENTIC_TEXT_ANALYSIS_PROMPT_TEMPLATE.format(text_input=text_input) response = st.session_state.text_model.generate_content(prompt) # Use model from session state # Handle response variations if response.parts: return response.text, None elif response.prompt_feedback.block_reason: return None, f"Analysis blocked by safety filters: {response.prompt_feedback.block_reason.name}. Please review or revise input." else: candidate = response.candidates[0] if response.candidates else None if candidate and candidate.finish_reason != "STOP": # If stopped for reasons other than normal completion (e.g., length, safety) return None, f"Analysis stopped prematurely. Reason: {candidate.finish_reason.name}. Input might be too long or triggered other limits." else: # General case for empty or unexpected response return None, "Received an empty or unexpected response from the AI model for text analysis." except Exception as e: print(f"ERROR in run_agentic_text_analysis: {e}") # Log for server/dev console st.error("An error occurred during text analysis.", icon="🚨") # User-facing generic error return None, "An internal error occurred during text analysis. Please try again later or contact support if the issue persists." def analyze_medical_image(image_file: Any, user_prompt: str = "") -> Tuple[Optional[str], Optional[str]]: """Sends a medical image to the configured vision model for analysis using the refined prompt.""" if not image_file: return None, "Image file cannot be empty." if not st.session_state.models_initialized or not st.session_state.vision_model: return None, "Image analysis model not initialized. Please refresh or check configuration." try: try: # Open and prepare the image image = Image.open(image_file) if image.mode != 'RGB': image = image.convert('RGB') # Ensure RGB format except Exception as img_e: return None, f"Error opening or processing the uploaded image file: {img_e}. Please ensure it's a valid image." # Prepare the prompt using the refined template prompt_text = IMAGE_ANALYSIS_PROMPT_TEMPLATE.format(user_prompt=user_prompt if user_prompt else "N/A") model_input = [prompt_text, image] # Combine text prompt and image data response = st.session_state.vision_model.generate_content(model_input) # Use model from session state # Handle response variations if response.parts: return response.text, None elif response.prompt_feedback.block_reason: return None, f"Image analysis blocked by safety filters: {response.prompt_feedback.block_reason.name}. This might relate to sensitive content policies regarding medical images." else: candidate = response.candidates[0] if response.candidates else None if candidate and candidate.finish_reason != "STOP": return None, f"Image analysis stopped prematurely. Reason: {candidate.finish_reason.name}. Input might be too complex or triggered other limits." else: return None, "Received an empty or unexpected response from the AI model for image analysis." except Exception as e: print(f"ERROR in analyze_medical_image: {e}") # Log for server/dev console st.error("An error occurred during image analysis.", icon="🖼️") # User-facing generic error return None, "An internal error occurred during image analysis. Please try again later or contact support if the issue persists." # --- Streamlit User Interface --- def main(): # Page title and model info st.title("🤖 AI Clinical Support Demonstrator") st.caption(f"Utilizing: Text Model ({TEXT_MODEL_NAME}), Vision Model ({VISION_MODEL_NAME})") # --- CRITICAL DISCLAIMER --- st.warning( """ **🔴 IMPORTANT SAFETY & USE DISCLAIMER 🔴** * This tool **DEMONSTRATES** AI capabilities. It **DOES NOT** provide medical advice or diagnosis. * **Agentic Text Analysis:** Simulates reasoning on text input. Output is illustrative, not diagnostic. * **Image Analysis:** Provides observations on images. Output is **NOT** a radiological interpretation. * AI analysis lacks full clinical context, may be inaccurate, and **CANNOT** replace professional judgment. * **ALWAYS consult qualified healthcare professionals** for diagnosis and treatment. * **PRIVACY:** Do **NOT** upload identifiable patient information (PHI) without explicit consent and adherence to all privacy laws (e.g., HIPAA). You are responsible for the data you input. """, icon="⚠️" ) st.markdown("---") # --- Sidebar Controls --- st.sidebar.header("Analysis Options") input_method = st.sidebar.radio( "Select Analysis Type:", ("Agentic Text Analysis", "Medical Image Analysis"), key="input_method_radio", help="Choose 'Agentic Text Analysis' for reasoning simulation on clinical text, or 'Medical Image Analysis' for visual observations on images." ) st.sidebar.markdown("---") # Visual separator # --- Main Area Layout (Input and Output Columns) --- col1, col2 = st.columns(2) analysis_result = None # Reset results variables for this run error_message = None output_header = "Analysis Results" # Default header analyze_button_key = None # Initialize key variable # --- Column 1: Input Area --- with col1: st.header("Input Data") # Conditional Input UI based on selection if input_method == "Agentic Text Analysis": st.subheader("Clinical Text for Agentic Analysis") st.caption("Please ensure data is de-identified before pasting.") text_input = st.text_area( "Paste clinical information:", height=350, placeholder="Example: 68yo male, sudden SOB & pleuritic chest pain post-flight. HR 110, SpO2 92% RA. No known cardiac hx...", key="text_input_area" ) analyze_button_key = "analyze_text_button" # Set key for this branch analyze_button_label = "▶️ Run Agentic Text Analysis" if st.button(analyze_button_label, key=analyze_button_key, type="primary"): if text_input: with st.spinner("🧠 Simulating agentic reasoning... Please wait."): analysis_result, error_message = run_agentic_text_analysis(text_input) output_header = "Simulated Agentic Analysis Output" else: st.warning("Please enter clinical text to analyze.", icon="☝️") elif input_method == "Medical Image Analysis": st.subheader("Medical Image for Analysis") st.caption("Upload a de-identified medical image (PNG, JPG, JPEG).") image_file = st.file_uploader( "Choose an image file:", type=["png", "jpg", "jpeg"], key="image_uploader" ) user_image_prompt = st.text_input( "Optional: Add context or specific question for image analysis:", placeholder="Example: 'Describe findings in the lung fields' or 'Any visible fractures?'", key="image_prompt_input" ) analyze_button_key = "analyze_image_button" # Set key for this branch analyze_button_label = "🖼️ Analyze Medical Image" if image_file: # Display preview immediately after upload inside the input column st.image(image_file, caption="Uploaded Image Preview", use_column_width=True) if st.button(analyze_button_label, key=analyze_button_key, type="primary"): if image_file: with st.spinner("👁️ Analyzing image... Please wait."): analysis_result, error_message = analyze_medical_image(image_file, user_image_prompt) output_header = "Medical Image Analysis Output" else: st.warning("Please upload an image file to analyze.", icon="☝️") # --- Column 2: Output Area --- with col2: st.header(output_header) # Determine if analysis was attempted in this run using the correct button key button_pressed = st.session_state.get(analyze_button_key, False) if analyze_button_key else False if button_pressed: # Only display results if the corresponding button was pressed AND generated output/error if error_message: st.error(f"Analysis Failed: {error_message}", icon="❌") elif analysis_result: # Use st.markdown to render potential formatting (like headings) from AI st.markdown(analysis_result, unsafe_allow_html=False) # Keep unsafe_allow_html=False for security # No explicit 'else' needed here; if button pressed but no result/error, likely handled by input validation warning else: # Default placeholder message if no analysis attempted yet in this run st.info("Analysis results will appear here after providing input and clicking the corresponding analysis button.") # --- Sidebar Explanations --- st.sidebar.markdown("---") st.sidebar.header("About The Prompts") with st.sidebar.expander("View Agentic Text Prompt Structure", icon="📄"): # Show only the instructive part of the prompt template st.code(AGENTIC_TEXT_ANALYSIS_PROMPT_TEMPLATE.split('---')[0] + "...", language='markdown') st.caption("Guides the AI through structured reasoning steps for text.") with st.sidebar.expander("View Image Analysis Prompt Structure", icon="🖼️"): # Show only the instructive part of the prompt template st.code(IMAGE_ANALYSIS_PROMPT_TEMPLATE.split('---')[0] + "...", language='markdown') st.caption("Guides the AI to provide cautious, descriptive visual observations for images.") st.sidebar.markdown("---") st.sidebar.error( "**Ethical Use Reminder:** AI in medicine requires extreme caution. This tool is for demonstration and education, not clinical practice. Verify all information and rely on professional expertise.", icon = "⚕️" ) # --- Main Execution Guard --- if __name__ == "__main__": # Check if models are initialized before running the main UI components if st.session_state.models_initialized: main() else: # Errors during initialization should have been shown and stopped execution. # This path might be reached if initialization is still pending in an async setup (not the case here) # or if there was a non-fatal init issue not caught by st.stop(). st.info("Waiting for AI model initialization or resolving configuration issues...")