Add FastAPI application with WebSocket support and authentication

.gitignore

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+.venv

Dockerfile

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+# Use the official Python 3.9 image
+FROM python:3.9
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

auth.py

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+from fastapi import Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordBearer
+from jose import JWTError, jwt
+from passlib.context import CryptContext
+from datetime import datetime, timedelta
+import os
+
+# Load secrets from environment variables
+SECRET_KEY = os.getenv("SECRET_KEY")
+ALGORITHM = "HS512"
+ACCESS_TOKEN_EXPIRE_MINUTES = 30
+
+# Password hashing
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+# OAuth2 scheme
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+# Mock user database (replace with a real database in production)
+fake_users_db = {
+    "admin": {
+        "username": "admin",
+        "hashed_password": pwd_context.hash(os.getenv("ADMIN_PASSWORD")),
+    }
+}
+
+def verify_password(plain_password, hashed_password):
+    return pwd_context.verify(plain_password, hashed_password)
+
+def authenticate_user(username: str, password: str):
+    user = fake_users_db.get(username)
+    if not user or not verify_password(password, user["hashed_password"]):
+        return False
+    return user
+
+def create_access_token(data: dict, expires_delta: timedelta = None):
+    to_encode = data.copy()
+    if expires_delta:
+        expire = datetime.utcnow() + expires_delta
+    else:
+        expire = datetime.utcnow() + timedelta(minutes=15)
+    to_encode.update({"exp": expire})
+    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
+    return encoded_jwt
+
+async def get_current_user(token: str = Depends(oauth2_scheme)):
+    credentials_exception = HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Could not validate credentials",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    try:
+        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
+        username: str = payload.get("sub")
+        if username is None:
+            raise credentials_exception
+    except JWTError:
+        raise credentials_exception
+
+    user = fake_users_db.get(username)
+    if user is None:
+        raise credentials_exception
+    return user

docs/diagrams/system.md

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+```mermaid
+graph TD
+    A[Client] -->|HTTP/WebSocket| B[FastAPI Server]
+    B -->|Authentication| C[Auth Module]
+    B -->|Text Generation| D[Generation Module]
+    B -->|Rate Limiting| E[Rate Limiter]
+    B -->|Logging| F[Logging Middleware]
+    C -->|JWT Token| G[Secrets Manager]
+    D -->|Model Inference| H[Hugging Face Model]
+    E -->|Rate Limit Check| I[Redis/Database]
+    F -->|Logs| J[Log Storage]
+    H -->|Streaming| A
+
+    subgraph FastAPI Server
+        B
+        C
+        D
+        E
+        F
+    end
+
+    subgraph External Services
+        G[Secrets Manager]
+        H[Hugging Face Model]
+        I[Redis/Database]
+        J[Log Storage]
+    end
+```

gen.py

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+from fastapi import WebSocket
+from transformers import pipeline
+import asyncio
+
+# Load the model
+model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
+generator = pipeline("text-generation", model=model_name)
+
+async def generate_text_stream(prompt: str, websocket: WebSocket):
+    for i in range(10):  # Simulate streaming (replace with actual model inference)
+        chunk = generator(prompt, max_length=i + 10, do_sample=True)[0]["generated_text"]
+        await websocket.send_text(chunk)
+        await asyncio.sleep(0.1)  # Simulate delay

main.py

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+from fastapi import FastAPI, WebSocket, Depends, HTTPException
+from auth import get_current_user, authenticate_user, create_access_token
+from gen import generate_text_stream
+from fastapi.security import OAuth2PasswordRequestForm
+from fastapi.middleware.cors import CORSMiddleware
+from middleware import setup_rate_limiter
+import os
+
+app = FastAPI()
+
+
+# Apply rate limiting middleware
+setup_rate_limiter(app)
+
+# CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Login endpoint
+@app.post("/token")
+async def login(form_data: OAuth2PasswordRequestForm = Depends()):
+    user = authenticate_user(form_data.username, form_data.password)
+    if not user:
+        raise HTTPException(status_code=400, detail="Incorrect username or password")
+    access_token = create_access_token(data={"sub": user["username"]})
+    return {"access_token": access_token, "token_type": "bearer"}
+
+# WebSocket endpoint for streaming
+@app.websocket("/generate")
+async def websocket_generate(websocket: WebSocket, token: str):
+    await websocket.accept()
+    try:
+        user = get_current_user(token)
+        prompt = await websocket.receive_text()
+        await generate_text_stream(prompt, websocket)
+    except Exception as e:
+        await websocket.send_text(f"Error: {str(e)}")
+    finally:
+        await websocket.close()

middleware.py

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+from fastapi import Request
+from slowapi import Limiter
+from slowapi.util import get_remote_address
+from slowapi.middleware import SlowAPIMiddleware
+
+
+limiter = Limiter(key_func=get_remote_address)
+
+async def log_requests(request: Request, call_next):
+    print(f"Request: {request.method} {request.url}")
+    response = await call_next(request)
+    print(f"Response: {response.status_code}")
+    return response
+
+# Apply the rate limiter middleware
+def setup_rate_limiter(app):
+    app.state.limiter = limiter
+    app.add_middleware(SlowAPIMiddleware)

requirements.txt

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+# FastAPI and ASGI server
+fastapi
+uvicorn
+
+# Hugging Face Transformers for LLM inference
+transformers
+torch  # or tensorflow if preferred
+
+# Authentication and security
+python-jose[cryptography]  # JWT support
+passlib  # Password hashing
+
+# Rate limiting
+slowapi
+
+# WebSocket support
+websockets
+
+# Logging and middleware
+loguru  # Optional: for better logging
+
+# Testing (optional for development)
+pytest
+httpx  # For async HTTP requests in tests
+
+# Docker and deployment
+gunicorn  # Optional: for production deployment

schemas.py

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+from pydantic import BaseModel, EmailStr
+
+# Authentication
+class Token(BaseModel):
+    access_token: str
+    token_type: str
+
+class TokenData(BaseModel):
+    username: str | None = None
+
+class User(BaseModel):
+    username: str
+    email: EmailStr | None = None
+
+class UserInDB(User):
+    hashed_password: str
+
+class LoginRequest(BaseModel):
+    username: str
+    password: str
+
+# Generation
+class GenerationRequest(BaseModel):
+    prompt: str
+    max_length: int = 100
+    temperature: float = 0.7
+    top_k: int = 50
+    top_p: float = 0.95
+
+class GenerationResponse(BaseModel):
+    generated_text: str
+
+# WebSocket
+class WebSocketMessage(BaseModel):
+    prompt: str
+
+# Error Handling
+class HTTPError(BaseModel):
+    detail: str
+
+    class Config:
+        schema_extra = {
+            "example": {"detail": "Error message"},
+        }
+
+# Rate Limiting
+class RateLimitResponse(BaseModel):
+    message: str
+    retry_after: int

static/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>LLM Host API Documentation</title>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/prismjs/1.29.0/themes/prism-tomorrow.min.css" rel="stylesheet" />
+    <link href="/home/style.css" rel="stylesheet" />
+</head>
+<body>
+    <div class="container">
+        <nav class="sidebar">
+            <div class="logo">
+                🔗 LLM Host API
+            </div>
+            <div class="nav-items">
+                <!-- Navigation items will be populated by JavaScript -->
+            </div>
+        </nav>
+        <main class="content">
+            <h1>LLM Host API</h1>
+            
+            <section id="endpoints">
+                <h2>Endpoints</h2>
+                
+                <div class="endpoint" id="login">
+                    <h3>1. Login Endpoint</h3>
+                    <p><span class="method">POST</span><span class="endpoint-url">/login</span></p>
+                    <p>This endpoint is used to authenticate the user and issue an access token and a refresh token.</p>
+                    
+                    <h4>cURL Request:</h4>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/login \
+     -H "Content-Type: application/x-www-form-urlencoded" \
+     -d 'username=user123&password=password123'</code></pre>
+
+                    <h4>Flutter Implementation:</h4>
+                    <pre><code class="language-dart">Future<LoginResponse> login(String username, String password) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/login');
+  
+  try {
+    final response = await http.post(
+      url,
+      headers: {'Content-Type': 'application/x-www-form-urlencoded'},
+      body: {
+        'username': username,
+        'password': password,
+      },
+    );
+
+    if (response.statusCode == 200) {
+      return LoginResponse.fromJson(jsonDecode(response.body));
+    } else {
+      throw Exception('Failed to login: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}
+
+// Model class
+class LoginResponse {
+  final String accessToken;
+  final String refreshToken;
+  final String tokenType;
+  final int expiresIn;
+
+  LoginResponse({
+    required this.accessToken,
+    required this.refreshToken,
+    required this.tokenType,
+    required this.expiresIn,
+  });
+
+  factory LoginResponse.fromJson(Map<String, dynamic> json) {
+    return LoginResponse(
+      accessToken: json['access_token'],
+      refreshToken: json['refresh_token'],
+      tokenType: json['token_type'],
+      expiresIn: json['expires_in'],
+    );
+  }
+}</code></pre>
+                    
+                    <h4>Response:</h4>
+                    <pre><code class="language-json">{
+    "access_token": "your-access-token",
+    "refresh_token": "your-refresh-token",
+    "token_type": "bearer",
+    "expires_in": 1800
+}</code></pre>
+                </div>
+
+                <div class="endpoint" id="refresh">
+                    <h3>2. Refresh Token Endpoint</h3>
+                    <p><span class="method">POST</span><span class="endpoint-url">/refresh</span></p>
+                    <p>This endpoint is used to refresh the access token using a valid refresh token.</p>
+                    
+                    <h4>cURL Request:</h4>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/refresh \
+     -H "Content-Type: application/json" \
+     -d '{"refresh_token": "your-refresh-token"}'</code></pre>
+
+                    <h4>Flutter Implementation:</h4>
+                    <pre><code class="language-dart">Future<LoginResponse> refreshToken(String refreshToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/refresh');
+  
+  try {
+    final response = await http.post(
+      url,
+      headers: {'Content-Type': 'application/json'},
+      body: jsonEncode({
+        'refresh_token': refreshToken,
+      }),
+    );
+
+    if (response.statusCode == 200) {
+      return LoginResponse.fromJson(jsonDecode(response.body));
+    } else {
+      throw Exception('Failed to refresh token: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}</code></pre>
+                    
+                    <h4>Response:</h4>
+                    <pre><code class="language-json">{
+    "access_token": "new-access-token",
+    "refresh_token": "your-refresh-token",
+    "token_type": "bearer",
+    "expires_in": 1800
+}</code></pre>
+                </div>
+
+                <div class="endpoint" id="search">
+                    <h3>3. Search Endpoint</h3>
+                    <p><span class="method">POST</span><span class="endpoint-url">/search</span></p>
+                    <p>This endpoint is used to send a search query and retrieve results. It requires a valid access token.</p>
+                    
+                    <h4>cURL Request:</h4>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/search \
+     -H "Content-Type: application/json" \
+     -H "Authorization: Bearer your-access-token" \
+     -d '{"query": "test query"}'</code></pre>
+
+                    <h4>Flutter Implementation:</h4>
+                    <pre><code class="language-dart">class SearchResult {
+  final String text;
+  final double similarity;
+  final String modelType;
+
+  SearchResult({
+    required this.text,
+    required this.similarity,
+    required this.modelType,
+  });
+
+  factory SearchResult.fromJson(Map<String, dynamic> json) {
+    return SearchResult(
+      text: json['text'],
+      similarity: json['similarity'].toDouble(),
+      modelType: json['model_type'],
+    );
+  }
+}
+
+Future<List<SearchResult>> search(String query, String accessToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/search');
+  
+  try {
+    final response = await http.post(
+      url,
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': 'Bearer $accessToken',
+      },
+      body: jsonEncode({
+        'query': query,
+      }),
+    );
+
+    if (response.statusCode == 200) {
+      List<dynamic> jsonList = jsonDecode(response.body);
+      return jsonList.map((json) => SearchResult.fromJson(json)).toList();
+    } else {
+      throw Exception('Search failed: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}</code></pre>
+                    
+                    <h4>Response:</h4>
+                    <pre><code class="language-json">[
+    {
+        "text": "Result 1 text",
+        "similarity": 0.95,
+        "model_type": "all-mpnet-base-v2"
+    },
+    {
+        "text": "Result 2 text",
+        "similarity": 0.92,
+        "model_type": "openai"
+    }
+]</code></pre>
+                </div>
+
+                <div class="endpoint" id="save">
+                    <h3>4. Save Data Endpoint</h3>
+                    <p><span class="method">POST</span><span class="endpoint-url">/save</span></p>
+                    <p>This endpoint is used to save user feedback and search results to the Hugging Face dataset. It requires a valid access token.</p>
+                    
+                    <h4>cURL Request:</h4>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/save \
+     -H "Content-Type: application/json" \
+     -H "Authorization: Bearer your-access-token" \
+     -d '{
+           "items": [
+               {
+                   "user_type": "user",
+                   "username": "user123",
+                   "query": "test query",
+                   "retrieved_text": "Result 1 text",
+                   "model_type": "all-mpnet-base-v2",
+                   "reaction": "positive"
+               }
+           ]
+         }'</code></pre>
+
+                    <h4>Flutter Implementation:</h4>
+                    <pre><code class="language-dart">class SaveInput {
+  final String userType;
+  final String username;
+  final String query;
+  final String retrievedText;
+  final String modelType;
+  final String reaction;
+
+  SaveInput({
+    required this.userType,
+    required this.username,
+    required this.query,
+    required this.retrievedText,
+    required this.modelType,
+    required this.reaction,
+  });
+
+  Map<String, dynamic> toJson() {
+    return {
+      'user_type': userType,
+      'username': username,
+      'query': query,
+      'retrieved_text': retrievedText,
+      'model_type': modelType,
+      'reaction': reaction,
+    };
+  }
+}
+
+Future<void> saveData(List<SaveInput> items, String accessToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/save');
+  
+  try {
+    final response = await http.post(
+      url,
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': 'Bearer $accessToken',
+      },
+      body: jsonEncode({
+        'items': items.map((item) => item.toJson()).toList(),
+      }),
+    );
+
+    if (response.statusCode != 200) {
+      throw Exception('Failed to save data: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}</code></pre>
+                    
+                    <h4>Response:</h4>
+                    <pre><code class="language-json">{
+    "message": "Data saved successfully"
+}</code></pre>
+                </div>
+            </section>
+
+            <section id="workflow">
+                <h2>Workflow Example</h2>
+                <p>Here's a complete workflow demonstrating how to use the API:</p>
+                
+                <h3>cURL Implementation</h3>
+                <div class="endpoint">
+                    <h3>Step 1: Login</h3>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/login \
+     -H "Content-Type: application/x-www-form-urlencoded" \
+     -d 'username=user123&password=password123'</code></pre>
+                </div>
+
+                <div class="endpoint">
+                    <h3>Step 2: Search</h3>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/search \
+     -H "Content-Type: application/json" \
+     -H "Authorization: Bearer your-access-token" \
+     -d '{"query": "test query"}'</code></pre>
+                </div>
+
+                <div class="endpoint">
+                    <h3>Step 3: Save Data</h3>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/save \
+     -H "Content-Type: application/json" \
+     -H "Authorization: Bearer your-access-token" \
+     -d '{
+           "items": [
+               {
+                   "user_type": "user",
+                   "username": "user123",
+                   "query": "test query",
+                   "retrieved_text": "Result 1 text",
+                   "model_type": "all-mpnet-base-v2",
+                   "reaction": "positive"
+               }
+           ]
+         }'</code></pre>
+                </div>
+
+                <div class="endpoint">
+                    <h3>Step 4: Refresh Token</h3>
+                    <pre><code class="language-bash">curl -X POST https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/refresh \
+     -H "Content-Type: application/json" \
+     -d '{"refresh_token": "your-refresh-token"}'</code></pre>
+                </div>
+
+                <h3>Flutter Implementation</h3>
+                <div class="endpoint">
+                    <h4>Complete Flutter Example</h4>
+                    <pre><code class="language-dart">import 'dart:convert';
+import 'package:flutter/material.dart';
+import 'package:http/http.dart' as http;
+
+void main() {
+  runApp(MyApp());
+}
+
+class MyApp extends StatelessWidget {
+  @override
+  Widget build(BuildContext context) {
+    return MaterialApp(
+      title: 'Al Ghazali RAG API Example',
+      home: ApiWorkflowExample(),
+    );
+  }
+}
+
+class ApiWorkflowExample extends StatefulWidget {
+  @override
+  _ApiWorkflowExampleState createState() => _ApiWorkflowExampleState();
+}
+
+class _ApiWorkflowExampleState extends State<ApiWorkflowExample> {
+  String _accessToken = '';
+  String _refreshToken = '';
+  List<SearchResult> _searchResults = [];
+
+  Future<void> _login() async {
+    final username = 'user123';
+    final password = 'password123';
+
+    try {
+      final loginResponse = await login(username, password);
+      setState(() {
+        _accessToken = loginResponse.accessToken;
+        _refreshToken = loginResponse.refreshToken;
+      });
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Login successful!')),
+      );
+    } catch (e) {
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Login failed: $e')),
+      );
+    }
+  }
+
+  Future<void> _search() async {
+    final query = 'test query';
+
+    try {
+      final results = await search(query, _accessToken);
+      setState(() {
+        _searchResults = results;
+      });
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Search successful!')),
+      );
+    } catch (e) {
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Search failed: $e')),
+      );
+    }
+  }
+
+  Future<void> _saveData() async {
+    final items = [
+      SaveInput(
+        userType: 'user',
+        username: 'user123',
+        query: 'test query',
+        retrievedText: _searchResults[0].text,
+        modelType: _searchResults[0].modelType,
+        reaction: 'positive',
+      ),
+    ];
+
+    try {
+      await saveData(items, _accessToken);
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Data saved successfully!')),
+      );
+    } catch (e) {
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Failed to save data: $e')),
+      );
+    }
+  }
+
+  Future<void> _refreshToken() async {
+    try {
+      final loginResponse = await refreshToken(_refreshToken);
+      setState(() {
+        _accessToken = loginResponse.accessToken;
+      });
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Token refreshed successfully!')),
+      );
+    } catch (e) {
+      ScaffoldMessenger.of(context).showSnackBar(
+        SnackBar(content: Text('Token refresh failed: $e')),
+      );
+    }
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return Scaffold(
+      appBar: AppBar(
+        title: Text('Al Ghazali RAG API Workflow'),
+      ),
+      body: Padding(
+        padding: const EdgeInsets.all(16.0),
+        child: Column(
+          crossAxisAlignment: CrossAxisAlignment.stretch,
+          children: [
+            ElevatedButton(
+              onPressed: _login,
+              child: Text('Login'),
+            ),
+            ElevatedButton(
+              onPressed: _search,
+              child: Text('Search'),
+            ),
+            ElevatedButton(
+              onPressed: _saveData,
+              child: Text('Save Data'),
+            ),
+            ElevatedButton(
+              onPressed: _refreshToken,
+              child: Text('Refresh Token'),
+            ),
+            Expanded(
+              child: ListView.builder(
+                itemCount: _searchResults.length,
+                itemBuilder: (context, index) {
+                  final result = _searchResults[index];
+                  return ListTile(
+                    title: Text(result.text),
+                    subtitle: Text('Similarity: ${result.similarity}, Model: ${result.modelType}'),
+                  );
+                },
+              ),
+            ),
+          ],
+        ),
+      ),
+    );
+  }
+}
+
+Future<LoginResponse> login(String username, String password) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/login');
+
+  try {
+    final response = await http.post(
+      url,
+      headers: {'Content-Type': 'application/x-www-form-urlencoded'},
+      body: {
+        'username': username,
+        'password': password,
+      },
+    );
+
+    if (response.statusCode == 200) {
+      return LoginResponse.fromJson(jsonDecode(response.body));
+    } else {
+      throw Exception('Failed to login: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}
+
+Future<LoginResponse> refreshToken(String refreshToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/refresh');
+
+  try {
+    final response = await http.post(
+      url,
+      headers: {'Content-Type': 'application/json'},
+      body: jsonEncode({
+        'refresh_token': refreshToken,
+      }),
+    );
+
+    if (response.statusCode == 200) {
+      return LoginResponse.fromJson(jsonDecode(response.body));
+    } else {
+      throw Exception('Failed to refresh token: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}
+
+Future<List<SearchResult>> search(String query, String accessToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/search');
+
+  try {
+    final response = await http.post(
+      url,
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': 'Bearer $accessToken',
+      },
+      body: jsonEncode({
+        'query': query,
+      }),
+    );
+
+    if (response.statusCode == 200) {
+      List<dynamic> jsonList = jsonDecode(response.body);
+      return jsonList.map((json) => SearchResult.fromJson(json)).toList();
+    } else {
+      throw Exception('Search failed: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}
+
+Future<void> saveData(List<SaveInput> items, String accessToken) async {
+  final url = Uri.parse('https://humblebeeai-al-ghazali-rag-retrieval-api.hf.space/save');
+
+  try {
+    final response = await http.post(
+      url,
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': 'Bearer $accessToken',
+      },
+      body: jsonEncode({
+        'items': items.map((item) => item.toJson()).toList(),
+      }),
+    );
+
+    if (response.statusCode != 200) {
+      throw Exception('Failed to save data: ${response.body}');
+    }
+  } catch (e) {
+    throw Exception('Network error: $e');
+  }
+}
+
+class LoginResponse {
+  final String accessToken;
+  final String refreshToken;
+  final String tokenType;
+  final int expiresIn;
+
+  LoginResponse({
+    required this.accessToken,
+    required this.refreshToken,
+    required this.tokenType,
+    required this.expiresIn,
+  });
+
+  factory LoginResponse.fromJson(Map<String, dynamic> json) {
+    return LoginResponse(
+      accessToken: json['access_token'],
+      refreshToken: json['refresh_token'],
+      tokenType: json['token_type'],
+      expiresIn: json['expires_in'],
+    );
+  }
+}
+
+class SearchResult {
+  final String text;
+  final double similarity;
+  final String modelType;
+
+  SearchResult({
+    required this.text,
+    required this.similarity,
+    required this.modelType,
+  });
+
+  factory SearchResult.fromJson(Map<String, dynamic> json) {
+    return SearchResult(
+      text: json['text'],
+      similarity: json['similarity'].toDouble(),
+      modelType: json['model_type'],
+    );
+  }
+}
+
+class SaveInput {
+  final String userType;
+  final String username;
+  final String query;
+  final String retrievedText;
+  final String modelType;
+  final String reaction;
+
+  SaveInput({
+    required this.userType,
+    required this.username,
+    required this.query,
+    required this.retrievedText,
+    required this.modelType,
+    required this.reaction,
+  });
+
+  Map<String, dynamic> toJson() {
+    return {
+      'user_type': userType,
+      'username': username,
+      'query': query,
+      'retrieved_text': retrievedText,
+      'model_type': modelType,
+      'reaction': reaction,
+    };
+  }
+}</code></pre>
+                </div>
+            </section>
+        </main>
+    </div>
+
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prismjs/1.29.0/prism.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prismjs/1.29.0/components/prism-bash.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prismjs/1.29.0/components/prism-json.min.js"></script>
+    <script src="/home/script.js"></script>
+</body>
+</html>

static/script.js

@@ -0,0 +1,44 @@
+// Generate navigation items
+const sections = [
+    { id: 'endpoints', title: 'Endpoints' },
+    { id: 'login', title: 'Login' },
+    { id: 'refresh', title: 'Refresh Token' },
+    { id: 'search', title: 'Search' },
+    { id: 'save', title: 'Save'},
+    { id: 'workflow', title: 'Workflow Example' }
+];
+
+const navItems = document.querySelector('.nav-items');
+sections.forEach(section => {
+    const item = document.createElement('div');
+    item.className = 'nav-item';
+    item.textContent = section.title;
+    item.setAttribute('data-section', section.id);
+    item.addEventListener('click', () => {
+        document.getElementById(section.id).scrollIntoView({ behavior: 'smooth' });
+    });
+    navItems.appendChild(item);
+});
+
+// Highlight current section in navigation
+const observerCallback = (entries) => {
+    entries.forEach(entry => {
+        if (entry.isIntersecting) {
+            const currentNav = document.querySelector(`.nav-item[data-section="${entry.target.id}"]`);
+            if (currentNav) {
+                document.querySelectorAll('.nav-item').forEach(item => {
+                    item.style.backgroundColor = '';
+                });
+                currentNav.style.backgroundColor = '#f3f4f6';
+            }
+        }
+    });
+};
+
+const observer = new IntersectionObserver(observerCallback, {
+    threshold: 0.2
+});
+
+document.querySelectorAll('section').forEach(section => {
+    observer.observe(section);
+});

static/style.css

@@ -0,0 +1,172 @@
+:root {
+    --primary-color: #4f46e5;
+    --secondary-color: #1e40af;
+    --text-color: #1f2937;
+    --code-bg: #1e1e1e;
+    --border-color: #e5e7eb;
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
+    line-height: 1.6;
+    color: var(--text-color);
+    background-color: #f9fafb;
+}
+
+.container {
+    display: flex;
+    min-height: 100vh;
+}
+
+.sidebar {
+    width: 280px;
+    background-color: white;
+    border-right: 1px solid var(--border-color);
+    padding: 2rem;
+    position: fixed;
+    height: 100vh;
+    overflow-y: auto;
+}
+
+.content {
+    flex: 1;
+    padding: 2rem;
+    margin-left: 280px;
+    max-width: 1200px;
+}
+
+.logo {
+    font-size: 1.5rem;
+    font-weight: bold;
+    color: var(--primary-color);
+    margin-bottom: 2rem;
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+}
+
+.nav-item {
+    margin: 0.5rem 0;
+    cursor: pointer;
+    padding: 0.5rem;
+    border-radius: 0.375rem;
+    transition: background-color 0.2s;
+}
+
+.nav-item:hover {
+    background-color: #f3f4f6;
+}
+
+h1 {
+    font-size: 2.5rem;
+    margin-bottom: 1.5rem;
+    color: var(--primary-color);
+}
+
+h2 {
+    font-size: 1.8rem;
+    margin: 2rem 0 1rem;
+    padding-top: 2rem;
+    border-top: 1px solid var(--border-color);
+}
+
+h3 {
+    font-size: 1.4rem;
+    margin: 1.5rem 0 1rem;
+    color: var(--secondary-color);
+}
+
+p {
+    margin-bottom: 1rem;
+}
+
+pre {
+    margin: 1rem 0;
+    padding: 1rem;
+    border-radius: 0.5rem;
+    background-color: #2d2d2d !important;
+    overflow-x: auto;
+    border: 1px solid #3f3f3f;
+}
+
+code {
+    font-family: 'Fira Code', monospace;
+    font-size: 0.9rem;
+    color: #e6e6e6;
+}
+
+/* Specific syntax highlighting overrides */
+.language-bash {
+    color: #e6e6e6 !important;
+}
+
+.language-bash .token.function {
+    color: #87ceeb !important;
+}
+
+.language-bash .token.operator {
+    color: #87ceeb !important;
+}
+
+.language-bash .token.string {
+    color: #98c379 !important;
+}
+
+.language-json {
+    color: #e6e6e6 !important;
+}
+
+.language-json .token.property {
+    color: #87ceeb !important;
+}
+
+.language-json .token.string {
+    color: #98c379 !important;
+}
+
+.language-json .token.number {
+    color: #d19a66 !important;
+}
+
+/* Add a subtle glow effect to code blocks */
+pre code {
+    text-shadow: 0 1px 1px rgba(0, 0, 0, 0.1);
+}
+
+.endpoint {
+    background-color: white;
+    border: 1px solid var(--border-color);
+    border-radius: 0.5rem;
+    padding: 1.5rem;
+    margin: 1.5rem 0;
+}
+
+.method {
+    display: inline-block;
+    padding: 0.25rem 0.5rem;
+    border-radius: 0.25rem;
+    background-color: var(--primary-color);
+    color: white;
+    font-weight: 500;
+    margin-right: 0.5rem;
+}
+
+.endpoint-url {
+    font-family: 'Fira Code', monospace;
+    color: var(--secondary-color);
+}
+
+@media (max-width: 768px) {
+    .sidebar {
+        display: none;
+    }
+    .content {
+        margin-left: 0;
+    }
+}