Update README.md

README.md

@@ -11,7 +11,7 @@ short_description: Medical Image diagnostic
 license: mit
 ---
 RadVision AI Advanced
-RadVision AI Advanced is a cutting-edge, Streamlit-based medical imaging analysis application designed to assist clinicians and researchers with rapid, AI-powered interpretation of both DICOM and standard image formats. The tool integrates advanced image processing, region-of-interest (ROI) selection, and multiple AI services—including language models for analysis, Q&A, and disease-specific evaluations—to generate detailed reports and insights on medical images.
+RadVision AI Advanced is a cutting‑edge, Streamlit‑based medical imaging analysis application designed to assist clinicians and researchers with rapid, AI‑powered interpretation of both DICOM and standard image formats. The tool integrates advanced image processing, region-of‑interest (ROI) selection, and multiple AI services—including language models for analysis, Q&A, and disease‑specific evaluations—to generate detailed reports and insights on medical images.
 
 Table of Contents
 Overview
@@ -32,146 +32,156 @@ Contributing
 
 License
 
+Configuration Reference
+
 Overview
-RadVision AI Advanced leverages state-of-the-art AI models to process and analyze medical images. The application supports:
+RadVision AI Advanced leverages state‑of‑the‑art AI models to process and analyze medical images. The application supports both DICOM files and common image formats (JPG, PNG) and provides a user‑friendly, interactive interface. Key capabilities include:
 
-DICOM and Standard Images: Automatically detects and processes both DICOM and common image file formats (JPG, PNG).
+Multi‑Format Image Processing: Automatic detection and handling of DICOM images as well as standard image formats.
 
-ROI Selection: Users can draw on images to define regions of interest using an integrated drawable canvas.
+ROI Selection: Users can draw regions of interest on images using an integrated drawable canvas.
 
-Multi-Modal AI Analysis: Provides initial analyses, Q&A interactions, disease-specific evaluations, and confidence estimations.
+Multi‑Modal AI Analysis: Provides initial analyses, interactive Q&A sessions, disease‑specific evaluations, and confidence estimations.
 
-PDF Report Generation: Summarizes analysis results in a downloadable PDF report.
+PDF Report Generation: Summarizes analysis outputs in a downloadable PDF report.
 
-The app is designed for research and educational purposes, and its outputs should be verified by clinical experts.
+Advanced Translation Functionality: Uses the deep‑translator library with a Google Translate backend to detect and translate analysis text into multiple languages, preserving the original formatting (bullet points, numbering, spacing).
 
-Features
-Image Processing:
+Note: This application is intended for research and educational use only. Always verify results with clinical experts.
 
-DICOM parsing and metadata extraction.
+Features
+Image Processing
+DICOM Support: Parse DICOM files and extract metadata.
 
-Window/Level adjustment with interactive sliders.
+Window/Level Adjustment: Interactive sliders to optimize image visualization.
 
-Standard image processing using the Python Imaging Library (PIL).
+Standard Image Processing: Utilizes the Python Imaging Library (PIL) for common image formats.
 
-AI Integration:
+AI Integration
+Initial Analysis: Automated interpretation of the uploaded image.
 
-Initial analysis to describe and interpret the image.
+Q&A Interface: Enables users to ask questions about the image with region-of‑interest support.
 
-Q&A interface for detailed inquiries.
+Disease‑Specific Evaluation: Focused analysis for conditions such as pneumonia, tuberculosis, etc.
 
-Condition/disease-specific analysis.
+Confidence Estimation: Provides an AI‑generated confidence score for the analysis.
 
-Confidence estimation on AI outputs.
+Fallback Mechanisms: Uses external models (e.g., Hugging Face VQA APIs) when primary methods fail.
 
-Fallback mechanisms using Hugging Face’s VQA models when primary methods fail.
+Translation & Language Detection
+Translation Module: Implements translation using the deep‑translator library (Google Translate backend) with robust dependency checks and workarounds for known issues.
 
-Reporting:
+Language Detection: Detects the language of provided text snippets before translation.
 
-Generation of PDF reports that include embedded images, session IDs, and formatted analysis results.
+Formatting Preservation: Uses a few‑shot prompt with examples to ensure bullet points, numbering, and spacing are preserved in the translation.
 
-User Interface:
+Reporting
+PDF Report Generation: Generates downloadable PDF reports that include embedded images, session IDs, and formatted text summaries.
 
-Streamlit-based UI with a clean two-column layout:
+User Interface
+Streamlit‑Based Layout: Clean two‑column design.
 
-Left: Image viewer and ROI selection.
+Left Panel: Image viewer with ROI selection and DICOM metadata.
 
-Right: Analysis results and interactive controls.
+Right Panel: Analysis results, Q&A history, disease evaluation, confidence estimation, and translation features.
 
 File Structure
-graphql
-Copy
+pgsql
+Copy code
 ├── app.py                  # Main Streamlit application entry point.
-├── dicom_utils.py          # Functions to parse DICOM files, extract metadata, and convert images.
-├── hf_models.py            # Integration with Hugging Face Inference API for VQA fallback.
-├── llm_interactions.py     # Functions to interact with Gemini (and other LLMs) for analysis, Q&A, and more.
-├── report_utils.py         # Functions to generate PDF reports summarizing the session's analysis.
-├── ui_helpers.py           # Helper functions for the UI, including metadata display and window/level sliders.
+├── dicom_utils.py          # DICOM parsing, metadata extraction, and image conversion functions.
+├── hf_models.py            # Integration with external VQA models (e.g., Hugging Face) as a fallback.
+├── llm_interactions.py     # Functions for interfacing with language models for analysis and Q&A.
+├── report_utils.py         # Functions to generate PDF reports for analysis sessions.
+├── ui_helpers.py           # Helper functions for UI elements (e.g., metadata display, window/level sliders).
+├── translation_models.py   # Translation and language detection using deep‑translator (Google Translate backend).
 ├── requirements.txt        # List of Python dependencies.
 └── README.md               # Project documentation.
-app.py:
-Initializes the Streamlit interface, processes uploads, integrates all helper modules, and controls the overall workflow.
+app.py: Initializes the Streamlit interface, processes image uploads, integrates all modules, and controls the overall workflow.
 
-dicom_utils.py:
-Contains functions for DICOM file parsing, metadata extraction, image conversion, and window/level handling.
+dicom_utils.py: Handles DICOM file parsing, metadata extraction, image conversion, and window/level adjustments.
 
-hf_models.py:
-Handles querying external VQA models (e.g., from Hugging Face) as a fallback for multimodal analysis.
+hf_models.py: Provides integration with external VQA models for fallback in multimodal analysis.
 
-llm_interactions.py:
-Provides functions that interact with language models (like Gemini) to generate initial analyses, answer questions, run disease-specific evaluations, and estimate AI confidence.
+llm_interactions.py: Contains functions for communicating with large language models for initial analysis, Q&A, and confidence scoring.
 
-report_utils.py:
-Generates PDF reports summarizing the analysis session, including embedded images and formatted text.
+report_utils.py: Creates PDF reports summarizing the analysis session.
 
-ui_helpers.py:
-Contains UI-related helper functions such as displaying DICOM metadata and creating interactive window/level sliders.
+ui_helpers.py: Contains functions for UI enhancements like metadata display and interactive sliders.
 
-Installation
-Clone the Repository:
+translation_models.py: Implements translation and language detection using the deep‑translator library.
 
+Dependency Handling: Attempts to import deep‑translator and gracefully degrades translation features if unavailable.
+
+Workarounds: Applies a workaround for known issues with certain exceptions.
+
+Installation
+1. Clone the Repository
 bash
-Copy
+Copy code
 git clone https://github.com/yourusername/radvision-ai-advanced.git
 cd radvision-ai-advanced
-Create a Virtual Environment (Optional but Recommended):
-
+2. Create a Virtual Environment (Optional but Recommended)
 bash
-Copy
+Copy code
 python -m venv venv
-source venv/bin/activate  # On Windows use: venv\Scripts\activate
-Install Dependencies:
-
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+3. Install Dependencies
 bash
-Copy
+Copy code
 pip install -r requirements.txt
-Note: Ensure you have the required libraries such as Streamlit, Pillow, pydicom, fpdf2, and requests installed.
+Ensure you have the required libraries such as Streamlit, Pillow, pydicom, deep-translator, fpdf2, and transformers installed.
 
 Configuration
-Before running the application, configure the following environment variables or add them to a secrets.toml file for deployment:
+Before running the application, configure the following environment variables or add them to a secrets.toml file:
 
-HF_API_TOKEN:
-Your Hugging Face API token for accessing VQA models.
+HF_API_TOKEN: Your Hugging Face API token for VQA fallback.
 
-GEMINI_API_KEY:
-API key for the Gemini language model service.
+GEMINI_API_KEY: API key for the Gemini language model service.
 
-GEMINI_MODEL_OVERRIDE (Optional):
-To override the default Gemini model name (e.g., "gemini-2.5-pro-exp-03-25").
+GEMINI_MODEL_OVERRIDE (Optional): Override for the default Gemini model name (e.g., "gemini-2.5-pro-exp-03-25").
 
-For local testing with Streamlit, you can add these variables to a .env file or configure them in your terminal session.
+For local testing, these variables can be added to a .env file or set in your terminal session.
 
 Running the Application
 To start the application locally, run:
 
 bash
-Copy
+Copy code
 streamlit run app.py
-The app will open in your default browser. You can then upload images, adjust DICOM window/level settings, run various AI analyses, and generate PDF reports.
+The app will open in your default browser. From there, you can upload images, adjust DICOM settings, perform AI analysis, access translation features, and generate PDF reports.
 
 Usage Guide
-Upload an Image:
+Upload an Image
 Use the sidebar to upload a JPG, PNG, or DICOM file.
 
-Adjust DICOM Settings:
-If a DICOM image is detected, adjust the window center and width using the sliders.
+Adjust DICOM Settings
+For DICOM images, use interactive window/level sliders to optimize visualization.
+
+Run AI Analysis
+Click the action buttons (e.g., "Run Initial Analysis", "Ask AI", "Run Condition Analysis") in the sidebar. Optionally, draw an ROI on the image.
 
-Run AI Analysis:
-Click the appropriate action buttons (e.g., "Run Initial Analysis", "Ask AI", "Run Condition Analysis") in the sidebar. You can also draw on the image to define a region of interest (ROI).
+Translation Functionality
+In the Translation tab, select the text to translate (e.g., “Initial Analysis”).
 
-View Results:
-Analysis results, Q&A responses, disease-specific insights, and confidence estimations will appear in the two-column layout.
+Choose "Auto‑Detect" for the source language (or select a language manually) and choose a target language.
 
-Generate a Report:
-Use the "Generate PDF Data" button to create a downloadable report summarizing your session.
+The system uses deep‑translator to detect the source language and then translates the text. The few‑shot prompt provided in the app helps preserve formatting such as bullet points and numbering.
+
+View Analysis Results
+The right panel displays analysis results—including initial analysis, Q&A history, condition evaluation, confidence scores, and translations—in a clean, tabbed layout.
+
+Generate a Report
+Use the "Generate PDF Data" button to create a downloadable PDF report summarizing your session.
 
 Contributing
-Contributions are welcome! Feel free to submit pull requests or open issues with suggestions, bug reports, or feature requests. Please adhere to standard coding practices and document your changes accordingly.
+Contributions are welcome! Please submit pull requests or open issues for bug fixes, improvements, or new features. Follow standard coding practices and document your changes.
 
 License
 This project is open source and available under the MIT License.
 
-Disclaimer: This tool is intended for research and informational purposes only. Always consult a qualified healthcare professional for clinical interpretations and decisions.
-
+Configuration Reference
+For advanced configuration options for Hugging Face Spaces and similar deployment scenarios, please refer to the Hugging Face Spaces configuration reference.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Disclaimer
+This tool is intended for research and informational purposes only. The AI outputs should be verified by clinical experts, and it is not intended for clinical decision-making without professional validation.