Remove deprecated README and startup script; update FastAPI server port to 7860

README copy.md

@@ -1,162 +0,0 @@
-# OutFitly API
-
-A FastAPI-based web application for outfit management and recommendations.
-
-## Features
-
-- Fast and modern API built with FastAPI
-- Image processing capabilities with Pillow
-- Data validation with Pydantic
-- HTTP client functionality with requests
-- Automatic API documentation
-
-## Prerequisites
-
-- Python 3.8 or higher
-- pip (Python package installer)
-
-## Installation
-
-1. **Clone the repository**
-
-   ```bash
-   git clone <repository-url>
-   cd OutFitly
-   ```
-
-2. **Create a virtual environment**
-
-   ```bash
-   python -m venv ollama
-
-   # On Windows
-   ollama\Scripts\activate
-
-   # On macOS/Linux
-   source ollama/bin/activate
-   ```
-
-3. **Install dependencies**
-   ```bash
-   pip install -r fastapi_requirements.txt
-   ```
-
-## Usage
-
-### Running the Development Server
-
-Start the FastAPI development server:
-
-```bash
-uvicorn main:app --reload
-```
-
-The API will be available at:
-
-- **API**: http://localhost:8000
-- **Interactive API docs (Swagger UI)**: http://localhost:8000/docs
-- **Alternative API docs (ReDoc)**: http://localhost:8000/redoc
-
-### Running in Production
-
-For production deployment:
-
-```bash
-uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-## API Documentation
-
-Once the server is running, you can access the interactive API documentation at:
-
-- Swagger UI: http://localhost:8000/docs
-- ReDoc: http://localhost:8000/redoc
-
-## Project Structure
-
-```
-OutFitly/
-├── AI/
-│   ├── fastapi_requirements.txt
-│   └── [other AI-related files]
-├── main.py                 # FastAPI application entry point
-├── requirements.txt        # Python dependencies
-├── .gitignore             # Git ignore rules
-└── README.md              # This file
-```
-
-## Dependencies
-
-- **FastAPI** (0.104.1): Modern, fast web framework for building APIs
-- **Uvicorn** (0.24.0): ASGI server for running FastAPI applications
-- **Pillow** (10.0.1): Python Imaging Library for image processing
-- **Requests** (2.31.0): HTTP library for making API calls
-- **Pydantic** (2.4.2): Data validation and settings management
-
-## Development
-
-### Setting up Development Environment
-
-1. Follow the installation steps above
-2. Install additional development dependencies (if any):
-   ```bash
-   pip install pytest pytest-asyncio httpx
-   ```
-
-### Running Tests
-
-```bash
-pytest
-```
-
-### Code Style
-
-This project follows PEP 8 style guidelines. You can check code style with:
-
-```bash
-flake8 .
-```
-
-## Environment Variables
-
-Create a `.env` file in the root directory for environment-specific configurations:
-
-```env
-# Example environment variables
-DEBUG=True
-DATABASE_URL=sqlite:///./app.db
-SECRET_KEY=your-secret-key-here
-```
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## Support
-
-If you encounter any issues or have questions, please:
-
-1. Check the [documentation](http://localhost:8000/docs) when the server is running
-2. Search existing [issues](../../issues)
-3. Create a new issue if needed
-
-## Roadmap
-
-- [ ] Add authentication and authorization
-- [ ] Implement outfit recommendation algorithms
-- [ ] Add image upload and processing features
-- [ ] Create user management system
-- [ ] Add database integration
-- [ ] Implement caching layer
-
----
-
-**Built with ❤️ using FastAPI**

REFINED_PROMPT_README.md

@@ -1,143 +0,0 @@
-# Refined Clothing Analysis Prompt
-
-This document describes the refined prompt for analyzing clothing in images, providing a structured and comprehensive approach to fashion analysis.
-
-## Overview
-
-The refined prompt has been integrated into the Fashion Analyzer API to provide structured clothing analysis that follows a consistent format. This ensures detailed, professional-quality fashion analysis suitable for various applications.
-
-## API Endpoints
-
-### 1. Structured Analysis Endpoint
-- **URL**: `POST /analyze-structured`
-- **Description**: Analyzes uploaded images using the refined structured format
-- **Response**: Returns analysis in the exact format specified by the refined prompt
-
-### 2. Refined Prompt Endpoint
-- **URL**: `GET /refined-prompt`
-- **Description**: Returns the complete refined prompt specification
-- **Response**: Plain text containing the detailed prompt instructions
-
-### 3. Traditional Analysis Endpoint
-- **URL**: `POST /analyze-image`
-- **Description**: Provides comprehensive fashion analysis with detailed insights
-- **Response**: Returns traditional detailed fashion analysis
-
-## Refined Prompt Structure
-
-The refined prompt ensures analysis follows this exact structure:
-
-### 1. UPPER GARMENT
-- **Type**: Specific garment classification (shirt, blouse, sweater, dress, etc.)
-- **Color**: Primary and secondary colors with color theory insights
-- **Material**: Fabric type with intelligent inference
-- **Features**: Distinguishing characteristics (patterns, buttons, sleeves, etc.)
-
-### 2. LOWER GARMENT
-- **Type**: Bottom wear classification (jeans, pants, skirt, etc.)
-- **Color**: Color analysis with fashion context
-- **Material**: Fabric identification or intelligent inference
-- **Features**: Design elements (pockets, fit, length, etc.)
-
-### 3. FOOTWEAR
-- **Type**: Shoe classification (sneakers, heels, boots, etc.)
-- **Color**: Color description and analysis
-- **Material**: Material identification
-- **Features**: Design details (laces, buckles, heel height, etc.)
-
-### 4. OUTFIT SUMMARY
-A comprehensive 3-5 sentence paragraph that:
-- Describes overall aesthetic and style
-- Explains how pieces complement each other
-- Assesses occasion appropriateness and versatility
-- Includes color harmony analysis
-- Provides styling insights
-
-## Key Features
-
-### Intelligent Analysis
-- Uses yainage90 fashion-specific AI models for object detection
-- Provides intelligent inference when details aren't clearly visible
-- Incorporates color theory and fashion expertise
-- Considers fit, proportion, and styling principles
-
-### Professional Terminology
-- Uses appropriate fashion vocabulary
-- Includes technical terms (silhouette, drape, texture)
-- Provides industry-standard assessments
-- Offers styling recommendations
-
-### Comprehensive Coverage
-- Analyzes all visible clothing elements
-- Considers occasion appropriateness
-- Assesses versatility and styling potential
-- Provides color harmony insights
-
-## Usage Examples
-
-### Web Interface
-1. Visit `http://localhost:7861`
-2. Upload an image of clothing
-3. Choose "Analyze Fashion (Structured)" for refined format
-4. Choose "Analyze Fashion (Detailed)" for comprehensive analysis
-
-### API Usage
-```bash
-# Structured analysis
-curl -X POST "http://localhost:7861/analyze-structured" \
-     -H "accept: application/json" \
-     -H "Content-Type: multipart/form-data" \
-     -F "file=@your_image.jpg"
-
-# Get refined prompt
-curl -X GET "http://localhost:7861/refined-prompt"
-```
-
-## Technical Implementation
-
-### AI Models Used
-- **yainage90/fashion-object-detection**: Specialized fashion item detection
-- **yainage90/fashion-image-feature-extractor**: Fashion feature extraction
-- **Salesforce/blip-image-captioning-base**: Fallback image description
-
-### Analysis Pipeline
-1. Image preprocessing and validation
-2. Fashion object detection using specialized models
-3. Feature extraction and analysis
-4. Intelligent inference for missing details
-5. Structured output generation following refined prompt format
-
-## Benefits
-
-### Consistency
-- Standardized output format across all analyses
-- Predictable structure for integration with other systems
-- Professional-quality descriptions
-
-### Accuracy
-- Specialized fashion AI models for better detection
-- Intelligent inference based on garment types
-- Color theory and fashion expertise integration
-
-### Versatility
-- Suitable for e-commerce applications
-- Fashion blogging and content creation
-- Personal styling recommendations
-- Wardrobe analysis tools
-
-## Future Enhancements
-
-- Integration with fashion trend databases
-- Seasonal styling recommendations
-- Brand and style recognition
-- Price point estimation
-- Sustainability assessments
-
-## Getting Started
-
-1. Start the server: `python fast.py`
-2. Open browser to `http://localhost:7861`
-3. Upload an image and select analysis type
-4. View the refined prompt at `/refined-prompt`
-
-The refined prompt ensures consistent, professional fashion analysis suitable for various applications while maintaining the flexibility to provide detailed insights based on image content.

fast.py

@@ -2752,4 +2752,4 @@ async def deepfashion2_setup_instructions():
     }
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7861)
+    uvicorn.run(app, host="0.0.0.0", port=7860)

fastapi_startup_script.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-
-# Debug: Check current user and permissions
-echo "Current user: $(whoami)"
-echo "Home directory: $HOME"
-
-# Note: This script is deprecated as the application now uses transformers instead of Ollama
-echo "Starting FastAPI server with transformers-based fashion analysis..."
-
-# Start FastAPI on port 7860 (HF Spaces requirement)
-echo "Starting FastAPI server on port 7860..."
-cd /app
-python3 -m uvicorn fast:app --host 0.0.0.0 --port 7860