Implement FastAPI endpoint with fitness input validation and AI response generation

.env.example

@@ -0,0 +1 @@
+OPENAI_API_KEY=your_openai_api_key_here

.gitignore

@@ -0,0 +1,42 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+env/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Environment variables
+.env
+.env.local
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/

IMPLEMENTATION.md

@@ -0,0 +1,156 @@
+# Implementation Summary
+
+## Problem Statement (Turkish)
+The requirement was to create a FastAPI endpoint with the following specifications:
+
+1. **Input Parameters:**
+   - user adı (user name)
+   - user hedefleri ama liste şeklinde (user goals as a list)
+   - user vücut bilgileri (dict olarak, örneğin {weight: 76.8,..}) (user body info as dict)
+   - user input'u (user input)
+
+2. **Two-Step Process:**
+
+   **Step 1:** Use the user input to ask the model if it's fitness-related, returning a boolean or score value.
+
+   **Step 2:** Based on the value:
+   - If failed: return error response
+   - If successful: include user information in prompt and send to AI, return AI response
+
+## Implementation
+
+### Files Created
+
+1. **main.py** - Main FastAPI application
+   - `/process-fitness-input` endpoint (POST) - Main endpoint for processing user input
+   - `/` endpoint (GET) - Root endpoint with API info
+   - `/health` endpoint (GET) - Health check
+   - `validate_fitness_input()` - Step 1: Validates if input is fitness-related
+   - `generate_ai_response()` - Step 2: Generates personalized AI response
+
+2. **models.py** - Pydantic data models
+   - `UserInput` - Request model with user_name, user_goals, user_body_info, user_input
+   - `FitnessValidationResponse` - Internal model for validation results
+   - `ErrorResponse` - Error response model
+   - `SuccessResponse` - Success response model with AI response and fitness score
+
+3. **requirements.txt** - Project dependencies
+   - fastapi==0.104.1
+   - uvicorn[standard]==0.24.0
+   - pydantic==2.5.0
+   - openai==1.3.0
+   - python-dotenv==1.0.0
+
+4. **test_app.py** - Test script to validate implementation
+5. **example_usage.py** - Example script showing API usage
+6. **.gitignore** - Python gitignore file
+7. **.env.example** - Example environment variables file
+8. **README.md** - Comprehensive documentation
+
+### Implementation Details
+
+#### Step 1: Fitness Validation
+Uses OpenAI's **structured output** feature (gpt-4o-2024-08-06 model) with the `response_format` parameter:
+
+```python
+completion = client.beta.chat.completions.parse(
+    model="gpt-4o-2024-08-06",
+    messages=[...],
+    response_format=FitnessValidationResponse,  # Structured output
+)
+```
+
+Returns a `FitnessValidationResponse` object with:
+- `is_fitness_related`: Boolean
+- `score`: Float (0-1) confidence score
+
+#### Step 2: Conditional Processing
+
+**If validation fails (score < 0.5 or is_fitness_related = False):**
+```json
+HTTP 400 Bad Request
+{
+  "detail": {
+    "error": "Input is not fitness-related",
+    "details": "The provided input does not appear to be related to fitness..."
+  }
+}
+```
+
+**If validation succeeds:**
+- User information (name, goals, body info) is included in the prompt
+- OpenAI generates a personalized fitness response
+- Returns success response:
+```json
+HTTP 200 OK
+{
+  "response": "AI-generated personalized fitness advice...",
+  "fitness_score": 0.95
+}
+```
+
+### API Usage
+
+**Request:**
+```bash
+curl -X POST "http://localhost:8000/process-fitness-input" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "user_name": "John Doe",
+    "user_goals": ["lose weight", "build muscle"],
+    "user_body_info": {"weight": 76.8, "height": 180},
+    "user_input": "I want to start a workout routine"
+  }'
+```
+
+**Success Response (200):**
+```json
+{
+  "response": "Based on your goals of losing weight and building muscle, and your current weight of 76.8kg and height of 180cm...",
+  "fitness_score": 0.95
+}
+```
+
+**Error Response (400):**
+```json
+{
+  "detail": {
+    "error": "Input is not fitness-related",
+    "details": "The provided input does not appear to be related to fitness, health, or wellness. Confidence score: 0.25"
+  }
+}
+```
+
+## Configuration
+
+1. Copy `.env.example` to `.env`
+2. Add your OpenAI API key:
+   ```
+   OPENAI_API_KEY=sk-...
+   ```
+
+## Testing
+
+Run tests:
+```bash
+python test_app.py
+```
+
+Start the server:
+```bash
+python main.py
+# or
+uvicorn main:app --reload
+```
+
+## Features Implemented
+
+✅ FastAPI endpoint accepting user name, goals (list), body info (dict), and input (string)
+✅ Step 1: Fitness validation using OpenAI structured output (boolean + score)
+✅ Step 2: Conditional processing based on validation
+✅ Error response for non-fitness inputs
+✅ Success response with personalized AI-generated content
+✅ Comprehensive documentation
+✅ Example scripts and tests
+✅ Proper error handling
+✅ OpenAPI/Swagger documentation (auto-generated at /docs)

README.md

@@ -1 +1,134 @@
-# demo-structured-output
+# demo-structured-output
+
+FastAPI application for validating fitness-related user inputs and generating personalized AI responses.
+
+## Features
+
+- **Fitness Input Validation**: Uses OpenAI's structured output to determine if user input is fitness-related
+- **Personalized AI Responses**: Generates tailored fitness advice based on user information
+- **Two-step Processing**: 
+  1. Validates input relevance (fitness-related or not)
+  2. Generates personalized response using user data
+
+## Requirements
+
+- Python 3.8+
+- OpenAI API key
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/lightningcell/demo-structured-output.git
+cd demo-structured-output
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Set up environment variables:
+```bash
+cp .env.example .env
+# Edit .env and add your OpenAI API key
+```
+
+## Usage
+
+### Start the server
+
+```bash
+python main.py
+```
+
+Or with uvicorn:
+```bash
+uvicorn main:app --reload
+```
+
+The API will be available at `http://localhost:8000`
+
+### API Endpoints
+
+#### POST /process-fitness-input
+
+Processes user fitness input with validation.
+
+**Request Body:**
+```json
+{
+  "user_name": "John Doe",
+  "user_goals": ["lose weight", "build muscle", "improve stamina"],
+  "user_body_info": {
+    "weight": 76.8,
+    "height": 180,
+    "age": 30
+  },
+  "user_input": "I want to start a workout routine"
+}
+```
+
+**Success Response (200):**
+```json
+{
+  "response": "Based on your goals and current metrics...",
+  "fitness_score": 0.95
+}
+```
+
+**Error Response (400):**
+```json
+{
+  "detail": {
+    "error": "Input is not fitness-related",
+    "details": "The provided input does not appear to be related to fitness, health, or wellness. Confidence score: 0.25"
+  }
+}
+```
+
+### Example with curl
+
+```bash
+curl -X POST "http://localhost:8000/process-fitness-input" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "user_name": "John Doe",
+    "user_goals": ["lose weight", "build muscle"],
+    "user_body_info": {"weight": 76.8, "height": 180},
+    "user_input": "What exercises should I do to lose weight?"
+  }'
+```
+
+## API Documentation
+
+Once the server is running, visit:
+- Swagger UI: `http://localhost:8000/docs`
+- ReDoc: `http://localhost:8000/redoc`
+
+## Project Structure
+
+```
+.
+├── main.py           # FastAPI application with endpoints
+├── models.py         # Pydantic models for request/response
+├── requirements.txt  # Python dependencies
+├── .env.example      # Example environment variables
+└── README.md         # This file
+```
+
+## Implementation Details
+
+### Step 1: Fitness Validation
+Uses OpenAI's structured output feature to parse responses into a `FitnessValidationResponse` model with:
+- `is_fitness_related`: Boolean indicating if input is fitness-related
+- `score`: Confidence score (0-1) for the classification
+
+### Step 2: Response Generation
+If validation passes (score >= 0.5):
+- User information (name, goals, body info) is included in the prompt
+- OpenAI generates a personalized fitness response
+- Response is returned to the client
+
+If validation fails:
+- HTTP 400 error with details about why the input was rejected