Add .gitattributes file, update app.py for Spaces compatibility, and create requirements files

.gitattributes

@@ -0,0 +1,4 @@
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.ann filter=lfs diff=lfs merge=lfs -text
+models/**/*.json filter=lfs diff=lfs merge=lfs -text
+customization/**/*.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,449 +1,121 @@
-# OnCall.ai - Medical Emergency Assistant
-
-A RAG-based medical assistant system that provides evidence-based clinical guidance for emergency medical situations using real medical guidelines and advanced language models.
-
-## 🎯 Project Overview
-
-OnCall.ai helps healthcare professionals by:
-
-- Processing medical queries through multi-level validation
-- Retrieving relevant medical guidelines from curated datasets
-- Generating evidence-based clinical advice using specialized medical LLMs
-- Providing transparent, traceable medical guidance
-
-## ✅ Current Implementation Status
-
-### **🎉 COMPLETED MODULES (2025-07-31)**
-
-#### **1. Multi-Level Query Processing System**
-
-- ✅ **UserPromptProcessor** (`src/user_prompt.py`)
-  - Level 1: Predefined medical condition mapping (instant response)
-  - Level 2: LLM-based condition extraction (Llama3-Med42-70B)
-  - Level 3: Semantic search fallback
-  - Level 4: Medical query validation (100% non-medical rejection)
-  - Level 5: Generic medical search for rare conditions
-
-#### **2. Dual-Index Retrieval System**
-
-- ✅ **BasicRetrievalSystem** (`src/retrieval.py`)
-  - Emergency medical guidelines index (emergency.ann)
-  - Treatment protocols index (treatment.ann)
-  - Vector-based similarity search using PubMedBERT embeddings
-  - Intelligent deduplication and result ranking
-
-#### **3. Medical Knowledge Base**
-
-- ✅ **MedicalConditions** (`src/medical_conditions.py`)
-  - Predefined condition-keyword mappings
-  - Medical terminology validation
-  - Extensible condition database
-
-#### **4. LLM Integration**
-
-- ✅ **Med42-70B Client** (`src/llm_clients.py`)
-  - Specialized medical language model integration
-  - Dual-layer rejection detection for non-medical queries
-  - Robust error handling and timeout management
-
-#### **5. Medical Advice Generation**
-
-- ✅ **MedicalAdviceGenerator** (`src/generation.py`)
-  - RAG-based prompt construction
-  - Intention-aware chunk selection (treatment/diagnosis)
-  - Confidence scoring and response formatting
-  - Integration with Med42-70B for clinical advice generation
-
-#### **6. Data Processing Pipeline**
-
-- ✅ **Processed Medical Guidelines** (`src/data_processing.py`)
-  - ~4000 medical guidelines from EPFL-LLM dataset
-  - Emergency subset: ~2000-2500 records
-  - Treatment subset: ~2000-2500 records
-  - PubMedBERT embeddings (768 dimensions)
-  - ANNOY vector indices for fast retrieval
-
-## 📊 **System Performance (Validated)**
-
-### **Comprehensive Evaluation Results (Metrics 1-8)**
-
-```
-🎯 Multi-Level Fallback Performance: 5-layer processing pipeline
-   - Level 1 (Predefined): Instant response for known conditions
-   - Level 2+4 (Combined LLM): 40% time reduction through optimization
-   - Level 3 (Semantic Search): High-quality embedding retrieval
-   - Level 5 (Generic): 100% fallback coverage
-
-📈 RAG vs Direct LLM Comparison (9 test queries):
-   - RAG System Actionability: 0.900 vs Direct: 0.789 (14.1% improvement)
-   - RAG Evidence Quality: 0.900 vs Direct: 0.689 (30.6% improvement)
-   - Category Performance: RAG superior in all categories (Diagnosis, Treatment, Mixed)
-   - Complex Queries (Mixed): RAG shows 30%+ advantage over Direct LLM
-```
-
-### **Detailed Performance Metrics**
-
-```
-🔍 Metric 1 - Latency Analysis:
-   - Average Response Time: 15.5s (RAG) vs 8.2s (Direct)
-   - Condition Extraction: 2.6s average
-   - Retrieval + Generation: 12.9s average
-
-📊 Metric 2-4 - Quality Assessment:
-   - Extraction Success Rate: 69.2% across fallback levels
-   - Retrieval Relevance: 0.245-0.326 (medical domain optimized)
-   - Content Coverage: 8-9 guidelines per query with balanced emergency/treatment
-
-🎯 Metrics 5-6 - Clinical Quality (LLM Judge Evaluation):
-   - Clinical Actionability: RAG (9.0/10) > Direct (7.9/10)
-   - Evidence Quality: RAG (9.0/10) > Direct (6.9/10)
-   - Treatment Queries: RAG achieves highest scores (9.3/10)
-   - All scores exceed clinical thresholds (7.0 actionability, 7.5 evidence)
-
-📈 Metrics 7-8 - Precision & Ranking:
-   - Precision@5: High relevance in medical guideline retrieval
-   - MRR (Mean Reciprocal Rank): Optimized for clinical decision-making
-   - Source Diversity: Balanced emergency and treatment protocol coverage
-```
-
-## 📈 **EVALUATION SYSTEM**
-
-### **Comprehensive Medical AI Evaluation Pipeline**
-
-OnCall.ai includes a complete evaluation framework with 8 key metrics to assess system performance across multiple dimensions:
-
-#### **🎯 General Pipeline Overview**
-
-```
-Query Input → RAG/Direct Processing → Multi-Metric Evaluation → Comparative Analysis
-     │                │                       │                      │
-     └─ Test Queries  └─ Medical Outputs     └─ Automated Metrics   └─ Visualization
-        (9 scenarios)    (JSON format)         (Scores & Statistics)   (4-panel charts)
-```
-
-#### **📊 Metrics 1-8: Detailed Assessment Framework**
-
-##### **⚡ Metric 1: Latency Analysis**
-
-- **Purpose**: Measure system response time and processing efficiency
-- **Operation**: `python evaluation/latency_evaluator.py`
-- **Key Findings**: RAG averages 15.5s, Direct averages 8.2s
-
-##### **🔍 Metric 2-4: Quality Assessment**
-
-- **Components**: Extraction success, retrieval relevance, content coverage
-- **Key Findings**: 69.2% extraction success, 0.245-0.326 relevance scores
-
-##### **🏥 Metrics 5-6: Clinical Quality (LLM Judge)**
-
-- **Purpose**: Professional evaluation of clinical actionability and evidence quality
-- **Operation**: `python evaluation/fixed_judge_evaluator.py rag,direct --batch-size 3`
-- **Charts**: `python evaluation/metric5_6_llm_judge_chart_generator.py`
-- **Key Findings**: RAG (9.0/10) significantly outperforms Direct (7.9/10 actionability, 6.9/10 evidence)
-
-##### **🎯 Metrics 7-8: Precision & Ranking**
-
-- **Operation**: `python evaluation/metric7_8_precision_MRR.py`
-- **Key Findings**: High precision in medical guideline retrieval
-
-#### **🏆 Evaluation Results Summary**
-
-- **RAG Advantages**: 30.6% better evidence quality, 14.1% higher actionability
-- **System Reliability**: 100% fallback coverage, clinical threshold compliance
-- **Human Evaluation**: Raw outputs available in `evaluation/results/medical_outputs_*.json`
-
-## 🛠️ **Technical Architecture**
-
-### **Data Flow**
-
-```
-User Query → Level 1: Predefined Mapping
-     ↓ (if fails)
-Level 2: LLM Extraction
-     ↓ (if fails)
-Level 3: Semantic Search
-     ↓ (if fails)
-Level 4: Medical Validation
-     ↓ (if fails)
-Level 5: Generic Search
-     ↓ (if fails)
-No Match Found
-```
-
-### **Core Technologies**
-
-- **Embeddings**: NeuML/pubmedbert-base-embeddings (768D)
-- **Vector Search**: ANNOY indices with angular distance
-- **LLM**: m42-health/Llama3-Med42-70B (medical specialist)
-- **Dataset**: EPFL-LLM medical guidelines (~4000 documents)
-
-### **Fallback Mechanism**
-
-```
-Level 1: Predefined Mapping (0.001s) → Success: Direct return
-Level 2: LLM Extraction (8-15s) → Success: Condition mapping
-Level 3: Semantic Search (1-2s) → Success: Sliding window chunks
-Level 4: Medical Validation (8-10s) → Fail: Return rejection
-Level 5: Generic Search (1s) → Final: General medical guidance
-```
-
-## 🚀 **NEXT PHASE: System Optimization & Enhancement**
-
-### **📊 Current Status (2025-08-09)**
-
-#### **✅ COMPLETED: Comprehensive Evaluation System**
-
-- **Metrics 1-8 Framework**: Complete assessment pipeline implemented
-- **RAG vs Direct Comparison**: Validated RAG system superiority (30%+ better evidence quality)
-- **LLM Judge Evaluation**: Automated clinical quality assessment with 4-panel visualization
-- **Performance Benchmarking**: Quantified system capabilities across all dimensions
-- **Human Evaluation Tools**: Raw output comparison framework available
-
-#### **✅ COMPLETED: Production-Ready Pipeline**
-
-- **5-Layer Fallback System**: 69.2% success rate with 100% coverage
-- **Dual-Index Retrieval**: Emergency and treatment guidelines optimized
-- **Med42-70B Integration**: Specialized medical LLM with robust error handling
-
-### **🎯 Future Goals**
-
-#### **🔊 Phase 1: Audio Integration Enhancement**
-
-- [ ] **Voice Input Pipeline**
-  - [ ] Whisper ASR integration for medical terminology
-  - [ ] Audio preprocessing and noise reduction
-  - [ ] Medical vocabulary optimization for transcription accuracy
-- [ ] **Voice Output System**
-  - [ ] Text-to-Speech (TTS) for medical advice delivery
-  - [ ] SSML markup for proper medical pronunciation
-  - [ ] Audio response caching for common scenarios
-- [ ] **Multi-Modal Interface**
-  - [ ] Simultaneous text + audio input support
-  - [ ] Audio quality validation and fallback to text
-  - [ ] Mobile-friendly voice interface optimization
-
-#### **⚡ Phase 2: System Performance Optimization (5→4 Layer Architecture)**
-
-Based on `docs/20250809optimization/5level_to_4layer.md` analysis:
-
-- [ ] **Query Cache Implementation** (80% P95 latency reduction expected)
-  - [ ] String similarity matching (0.85 threshold)
-  - [ ] In-memory LRU cache (1000 query limit)
-  - [ ] Cache hit monitoring and optimization
-- [ ] **Layer Reordering Optimization**
-  - [ ] L1: Enhanced Predefined Mapping (expand from 12 to 154 keywords)
-  - [ ] L2: Semantic Search (moved up for better coverage)
-  - [ ] L3: LLM Analysis (combined extraction + validation)
-  - [ ] L4: Generic Search (final fallback)
-- [ ] **Performance Targets**:
-  - P95 latency: 15s → 3s (80% improvement)
-  - L1 success rate: 15% → 30% (2x improvement)
-  - Cache hit rate: 0% → 30% (new capability)
-
-#### **📱 Phase 3: Interactive Interface Polish**
-
-- [ ] **Enhanced Gradio Interface** (`app.py` improvements)
-  - [ ] Real-time processing indicators
-  - [ ] Audio input/output controls
-  - [ ] Advanced debug mode with performance metrics
-  - [ ] Mobile-responsive design optimization
-- [ ] **User Experience Enhancements**
-  - [ ] Query suggestion system based on common medical scenarios
-  - [ ] Progressive disclosure of technical details
-  - [ ] Integrated help system with usage examples
-
-### **🔮 Further Enhancements (1-2 Months)**
-
-#### **📊 Advanced Analytics & Monitoring**
-
-- [ ] **Real-time Performance Dashboard**
-  - [ ] Layer success rate monitoring
-  - [ ] Cache effectiveness analysis
-  - [ ] User query pattern insights
-- [ ] **Continuous Evaluation Pipeline**
-  - [ ] Automated regression testing
-  - [ ] Performance benchmark tracking
-  - [ ] Clinical accuracy monitoring with expert review
-
-#### **🎯 Medical Specialization Expansion**
-
-- [ ] **Specialty-Specific Modules**
-  - [ ] Cardiology-focused pipeline
-  - [ ] Pediatric emergency protocols
-  - [ ] Trauma surgery guidelines integration
-- [ ] **Multi-Language Support**
-  - [ ] Spanish medical terminology
-  - [ ] French healthcare guidelines
-  - [ ] Localized medical protocol adaptation
-
-#### **🔬 Research & Development**
-
-- [ ] **Advanced RAG Techniques**
-  - [ ] Hierarchical retrieval architecture
-  - [ ] Dynamic chunk sizing optimization
-  - [ ] Cross-reference validation systems
-- [ ] **AI Safety & Reliability**
-  - [ ] Uncertainty quantification in medical advice
-  - [ ] Adversarial query detection
-  - [ ] Bias detection and mitigation in clinical recommendations
-
-### **📋 Updated Performance Targets**
-
-#### **Post-Optimization Goals**
-
-```
-⚡ Latency Improvements:
-   - P95 Response Time: <3 seconds (current: 15s)
-   - P99 Response Time: <0.5 seconds (current: 25s)
-   - Cache Hit Rate: >30% (new metric)
-
-🎯 Quality Maintenance:
-   - Clinical Actionability: ≥9.0/10 (maintain current RAG performance)
-   - Evidence Quality: ≥9.0/10 (maintain current RAG performance)
-   - System Reliability: 100% fallback coverage (maintain)
-
-🔊 Audio Experience:
-   - Voice Recognition Accuracy: >95% for medical terms
-   - Audio Response Latency: <2 seconds
-   - Multi-modal Success Rate: >90%
-```
-
-#### **System Scalability**
-
-```
-📈 Capacity Targets:
-   - Concurrent Users: 100+ simultaneous queries
-   - Query Cache: 10,000+ cached responses
-   - Audio Processing: Real-time streaming support
+---
+title: OnCall.ai - Medical Emergency Assistant
+emoji: 🏥
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: "5.38.0"
+app_file: app.py
+python_version: "3.11"
+pinned: false
+license: mit
+tags:
+  - medical
+  - healthcare
+  - RAG
+  - emergency
+  - clinical-guidance
+  - gradio
+---
 
-🔧 Infrastructure:
-   - HuggingFace Spaces deployment optimization
-   - Container orchestration for scaling
-   - CDN integration for audio content delivery
-```
+# 🏥 OnCall.ai - Medical Emergency Assistant
 
-## 📋 **Target Performance Metrics**
+A RAG-based medical assistant system that provides **evidence-based clinical guidance** for emergency medical situations using real medical guidelines and advanced language models.
 
-### **Response Quality**
+## 🎯 What This App Does
 
-- [ ] Physician satisfaction: ≥ 4/5
-- [ ] RAG content coverage: ≥ 80%
-- [ ] Retrieval precision (P@5): ≥ 0.7
-- [ ] Medical advice faithfulness: ≥ 0.8
+OnCall.ai helps healthcare professionals by:
+- **Processing medical queries** through multi-level validation system
+- **Retrieving relevant medical guidelines** from curated emergency medicine datasets
+- **Generating evidence-based clinical advice** using specialized medical LLMs (Llama3-Med42-70B)
+- **Providing transparent, traceable medical guidance** with source attribution
 
-### **System Performance**
+## 🚀 How to Use
 
-- [ ] Total response latency: ≤ 30 seconds
-- [ ] Condition extraction: ≤ 5 seconds
-- [ ] Guideline retrieval: ≤ 2 seconds
-- [ ] Medical advice generation: ≤ 25 seconds
+1. **Enter your medical query** in the text box (e.g., "Patient with chest pain and shortness of breath")
+2. **Click Submit** to process your query through our RAG pipeline
+3. **Review the response** which includes:
+   - Clinical guidance based on medical guidelines
+   - Evidence sources and reasoning
+   - Confidence level and validation status
 
-### **User Experience**
+## ⚙️ Configuration
 
-- [ ] Non-medical query rejection: 100%
-- [ ] System availability: ≥ 99%
-- [ ] Error handling: Graceful degradation
-- [ ] Interface responsiveness: Immediate feedback
+This app requires a **HuggingFace token** for accessing the medical language models.
 
-## 🏗️ **Project Structure**
+### Environment Variables
+- `HF_TOKEN`: Your HuggingFace API token (required for LLM access)
+- `ONCALL_DEBUG`: Set to 'true' to enable debug mode (optional)
 
-```
-OnCall.ai/
-├── src/                          # Core modules (✅ Complete)
-│   ├── user_prompt.py           # Multi-level query processing
-│   ├── retrieval.py             # Dual-index vector search
-│   ├── generation.py            # RAG-based advice generation
-│   ├── llm_clients.py           # Med42-70B integration
-│   ├── medical_conditions.py    # Medical knowledge configuration
-│   └── data_processing.py       # Dataset preprocessing
-├── models/                       # Pre-processed data (✅ Complete)
-│   ├── embeddings/              # Vector embeddings and chunks
-│   └── indices/                 # ANNOY vector indices
-├── evaluation/                   # Comprehensive evaluation system (✅ Complete)
-│   ├── fixed_judge_evaluator.py # LLM judge evaluation (Metrics 5-6)
-│   ├── latency_evaluator.py     # Performance analysis (Metrics 1-4)
-│   ├── metric7_8_precision_MRR.py # Precision/ranking analysis
-│   ├── results/                 # Evaluation outputs and comparisons
-│   ├── charts/                  # Generated visualization charts
-│   └── queries/test_queries.json # Standard test scenarios
-├── docs/                         # Documentation and optimization plans
-│   ├── 20250809optimization/    # System performance optimization
-│   │   └── 5level_to_4layer.md # Layer architecture improvements
-│   └── next/                    # Current implementation docs
-├── app.py                        # ✅ Gradio interface (Complete)
-├── united_requirements.txt       # 🔧 Updated: All dependencies
-└── README.md                     # This file
-```
+### To set up your HuggingFace token:
+1. Get your token from [HuggingFace Settings](https://huggingface.co/settings/tokens)
+2. In this Space, go to **Settings** → **Variables and Secrets**
+3. Add `HF_TOKEN` with your token value
 
-## 🧪 **Testing Validation**
+## 🏗️ System Architecture
 
-### **Completed Tests**
+### Multi-Level Query Processing Pipeline
+1. **Level 1**: Predefined medical condition mapping (instant response)
+2. **Level 2**: LLM-based condition extraction (Llama3-Med42-70B)
+3. **Level 3**: Semantic search fallback
+4. **Level 4**: Medical query validation (100% non-medical rejection)
+5. **Level 5**: Generic medical search for rare conditions
 
-- ✅ **Multi-level fallback validation**: 13 test cases, 69.2% success
-- ✅ **End-to-end pipeline testing**: 6 scenarios, 100% technical completion
-- ✅ **Component integration**: All modules working together
-- ✅ **Error handling**: Graceful degradation and user-friendly messages
+### Dual-Index Retrieval System
+- **Emergency Guidelines Index**: Fast retrieval for critical conditions
+- **Treatment Protocols Index**: Comprehensive clinical procedures
+- **Semantic Search**: Vector-based similarity matching using sentence transformers
 
-### **Key Findings**
+## 📋 Technical Details
 
-- **Predefined mapping**: Instant response for known conditions
-- **LLM extraction**: Reliable for complex symptom descriptions
-- **Non-medical rejection**: Perfect accuracy with updated prompt engineering
-- **Retrieval quality**: High-relevance medical guidelines (0.2-0.4 relevance scores)
-- **Generation capability**: Evidence-based advice with proper medical caution
+### Key Features
+- **Complete RAG Pipeline**: Query → Condition Extraction → Retrieval → Generation
+- **Multi-level fallback validation** for robust query processing
+- **Evidence-based medical advice** with transparent source attribution
+- **Gradio interface** for easy interaction
+- **Environment-controlled debug mode** for development
 
-## 🤝 **Contributing & Development**
+### Models Used
+- **Medical LLM**: Llama3-Med42-70B (specialized medical reasoning)
+- **Embedding Model**: Sentence Transformers for semantic search
+- **Retrieval**: Annoy index for fast approximate nearest neighbor search
 
-### **Environment Setup**
+### Dataset
+- Curated medical guidelines and emergency protocols
+- Treatment procedures and clinical decision trees
+- Evidence-based medical knowledge base
 
-```bash
-# Clone repository
-git clone [repository-url]
+## ⚠️ Important Disclaimers
 
-# Setup virtual environment
-python -m venv genAIvenv
-source genAIvenv/bin/activate  # On Windows: genAIvenv\Scripts\activate
+🚨 **This tool is for educational and research purposes only.**
 
-# Install dependencies
-pip install -r united_requirements.txt
+- **Not a substitute for professional medical advice**
+- **Not for use in actual medical emergencies**
+- **Always consult qualified healthcare professionals**
+- **Verify all information with authoritative medical sources**
 
-# Run tests
-python tests/test_end_to_end_pipeline.py
+## 🔧 Development Information
 
-# Start Gradio interface (coming soon)
-python app.py
+### Project Structure
 ```
-
-### **API Configuration**
-
-```bash
-# Set up HuggingFace token for LLM access
-export HF_TOKEN=your_huggingface_token
-
-# Enable debug mode for development
-export ONCALL_DEBUG=true
+├── app.py              # Main Gradio application
+├── src/                # Core modules
+│   ├── user_prompt.py    # Query processing
+│   ├── retrieval.py      # RAG retrieval system
+│   ├── generation.py     # Medical advice generation
+│   ├── llm_clients.py    # LLM interface
+│   └── medical_conditions.py # Condition mapping
+├── models/             # Pre-trained models and indices
+│   ├── embeddings/       # Vector embeddings
+│   └── indices/          # Search indices
+└── requirements.txt    # Dependencies
 ```
 
-## ⚠️ **Important Notes**
-
-### **Medical Disclaimer**
-
-This system is designed for **research and educational purposes only**. It should not replace professional medical consultation, diagnosis, or treatment. Always consult qualified healthcare providers for medical decisions.
-
-### **Current Limitations**
-
-- **API Dependencies**: Requires HuggingFace API access for LLM functionality
-- **Dataset Scope**: Currently focused on emergency and treatment guidelines
-- **Language Support**: English medical terminology only
-- **Validation Stage**: System under active development and testing
-
-## 📞 **Contact & Support**
-
-**Development Team**: OnCall.ai Team  
-**Last Updated**: 2025-08-09  
-**Version**: 1.0.0 (Evaluation Complete)  
-**Status**: 🎯 Ready for Optimization & Audio Enhancement Phase
+### Version
+- **Current Version**: 0.9.0
+- **Last Updated**: 2025-07-31
+- **Author**: OnCall.ai Team
 
 ---
 
-_Built with ❤️ for healthcare professionals_
+**🔗 For technical details, issues, or contributions, please refer to the project documentation.**

app.py

@@ -714,15 +714,20 @@ def main():
         # Create interface
         interface = create_oncall_interface()
         
-        # Launch configuration
+        # Launch configuration (Spaces-compatible)
         launch_config = {
-            "server_name": "0.0.0.0",  # Allow external connections
-            "server_port": 7860,       # Standard Gradio port
             "share": False,            # Set to True for public links
             "debug": DEBUG_MODE,
             "show_error": DEBUG_MODE
         }
         
+        # Only set server config for local development
+        if not os.getenv('SPACE_ID'):  # Not running in Hugging Face Spaces
+            launch_config.update({
+                "server_name": "0.0.0.0",  # Allow external connections
+                "server_port": 7860,       # Standard Gradio port
+            })
+        
         print("🚀 Launching OnCall.ai interface...")
         print(f"🌐 Interface will be available at: http://localhost:7860")
         

united_requirements.txt → requirements.txt

@@ -1,3 +1,7 @@
+# OnCall.ai Medical Emergency Assistant - Dependencies
+# Requires Python 3.11+ for optimal performance and compatibility
+# Developed and tested on Python 3.11.9
+
 accelerate==1.9.0
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1

requirements_optimized.txt

@@ -0,0 +1,43 @@
+# OnCall.ai Medical Emergency Assistant - Optimized Dependencies
+# Requires Python 3.11+ for optimal performance and compatibility
+# Version ranges locked to prevent breaking changes during deployment
+
+# Core ML and AI dependencies - locked to major versions
+gradio>=5.38,<6.0
+huggingface-hub>=0.33,<0.35
+transformers>=4.53,<4.55
+torch>=2.7,<2.8
+sentence-transformers>=3.0,<3.1
+
+# Retrieval and indexing
+annoy>=1.17,<1.18
+datasets>=4.0,<4.1
+
+# Web server and API
+fastapi>=0.116,<0.117
+uvicorn>=0.35,<0.36
+
+# Data processing
+pandas>=2.2,<2.3
+numpy>=2.2,<2.3
+scikit-learn>=1.7,<1.8
+
+# File processing
+pypdf>=5.8,<6.0
+python-dotenv>=1.1,<1.2
+
+# Image processing (for EasyOCR)
+opencv-python-headless>=4.12,<5.0
+easyocr>=1.7,<1.8
+pillow>=11.0,<12.0
+
+# Utilities
+requests>=2.32,<2.33
+tqdm>=4.67,<4.68
+pydantic>=2.11,<2.12
+packaging>=25.0,<26.0
+
+# Additional dependencies for your specific features
+llama-index-core>=0.12.50,<0.13
+llama-index-embeddings-huggingface>=0.5,<0.6
+llama-index-llms-huggingface>=0.5,<0.6