LocalFoodAI_FerRo988/main.py

import json
import logging
import httpx
import bcrypt
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException, Depends, Header
from database import create_tables, create_user, get_user_by_username, create_session, get_user_from_token, delete_session, search_foods_by_name, save_chat_message, get_user_chat_history, get_user_profile, get_food_by_id
from fastapi.responses import HTMLResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from typing import List, Generator, Optional

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@asynccontextmanager
async def lifespan(app: FastAPI):
    create_tables()
    yield

app = FastAPI(title="LocalFoodAI Chat", lifespan=lifespan)

# Use direct bcrypt for better environment compatibility
def get_password_hash(password: str):
    # Hash requires bytes
    pwd_bytes = password.encode('utf-8')
    salt = bcrypt.gensalt()
    hashed = bcrypt.hashpw(pwd_bytes, salt)
    return hashed.decode('utf-8')

def verify_password(plain_password: str, hashed_password: str):
    # bcrypt.checkpw handles verification
    return bcrypt.checkpw(plain_password.encode('utf-8'), hashed_password.encode('utf-8'))

class UserCreate(BaseModel):
    username: str
    password: str

class UserLogin(BaseModel):
    username: str
    password: str

async def get_current_user(authorization: Optional[str] = Header(None)):
    if not authorization or not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Authentication required")
    
    token = authorization.split(" ")[1]
    user = get_user_from_token(token)
    if not user:
        raise HTTPException(status_code=401, detail="Invalid or expired session")
    return user

OLLAMA_URL = "http://localhost:11434/api/chat"
MODEL_NAME = "llama3.1:8b"

# Common stopwords to strip before searching the food database
_STOPWORDS = {
    'how', 'many', 'much', 'calories', 'does', 'have', 'has', 'is', 'are', 
    'in', 'the', 'a', 'an', 'of', 'for', 'with', 'what', 'tell', 'me',
    'about', 'nutritional', 'value', 'nutrition', 'macro', 'macros',
    'protein', 'fat', 'carbs', 'fiber', 'can', 'you', 'i', 'want', 'need',
    'eat', 'eating', 'food', 'meal', 'diet', 'healthy', 'make', 'cook',
    'recipe', 'per', '100g', 'gram', 'grams', 'serving'
}

def extract_food_context(messages: list) -> str | None:
    """Scan the last user message for food keywords and enrich with local DB data."""
    # Find the last user message
    last_user_msg = None
    for msg in reversed(messages):
        role = msg.get('role', '') if isinstance(msg, dict) else msg.role
        content = msg.get('content', '') if isinstance(msg, dict) else msg.content
        if role == 'user':
            last_user_msg = content
            break
    
    if not last_user_msg:
        return None
    
    # Extract meaningful keywords by removing stopwords
    words = last_user_msg.lower().replace('?', '').replace(',', '').split()
    keywords = [w for w in words if w not in _STOPWORDS and len(w) > 2]
    
    if not keywords:
        return None
    
    # Try each keyword against the local food database, collect unique results
    found_items = {}
    # Optimization: Only use the first 2 most relevant keywords to keep context small on CPU
    for kw in keywords[:2]:
        results = search_foods_by_name(kw, limit=2)
        for item in results:
            # Truncate extremely long USDA names for performance
            short_name = item['name'][:100] + ("..." if len(item['name']) > 100 else "")
            if short_name not in found_items:
                found_items[short_name] = item
        if len(found_items) >= 3:
            break
    
    if not found_items:
        return None
    
    # Build a structured context block for the system prompt
    lines = [
        "[SYSTEM: NUTRITIONAL ANALYST MODE]",
        "You are the LocalFoodAI Analyst. Use ONLY the following verified local data for nutritional values.",
        "Always prioritize this data over your training memory. If a value is 0.0, it means it was not found or is negligible.",
        "If a specific nutrient is not listed below, state that it is not available in the local database.",
        "Format your summary clearly (tables are preferred).",
        ""
    ]
    for name, item in found_items.items():
        # Compact, token-efficient format for the LLM
        line = (
            f"- {name}: {item['calories']}kcal | P:{item['protein_g']}g | F:{item['fat_g']}g | C:{item['carbs_g']}g | "
            f"Fib:{item['fiber_g']}g | Sug:{item['sugar_g']}g | Chol:{item['cholesterol_mg']}mg | "
            f"Na:{item['sodium_mg']}mg | Ca:{item['calcium_mg']}mg | Fe:{item['iron_mg']}mg | "
            f"K:{item['potassium_mg']}mg | VitA:{item['vitamin_a_iu']}IU | VitC:{item['vitamin_c_mg']}mg"
        )
        lines.append(line)
    
    return "\n".join(lines)

# Mount static files to serve the frontend
app.mount("/static", StaticFiles(directory="static"), name="static")

class ChatMessage(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    messages: List[ChatMessage]

@app.get("/", response_class=HTMLResponse)
async def read_root():
    """Serve the chat interface HTML"""
    try:
        with open("static/index.html", "r", encoding="utf-8") as f:
            return HTMLResponse(content=f.read())
    except FileNotFoundError:
        return HTMLResponse(content="<h1>Welcome to LocalFoodAI</h1><p>static/index.html not found. Please create the frontend.</p>")

@app.post("/api/register")
async def register_user(user: UserCreate):
    if len(user.username.strip()) < 3:
        raise HTTPException(status_code=400, detail="Username must be at least 3 characters")
    if len(user.password.strip()) < 6:
        raise HTTPException(status_code=400, detail="Password must be at least 6 characters")
    
    hashed_password = get_password_hash(user.password)
    user_id = create_user(user.username.strip(), hashed_password)
    if not user_id:
        raise HTTPException(status_code=400, detail="Username already exists")
    
    # Auto-login after registration
    token = create_session(user_id)
    return {"message": "User registered successfully", "token": token, "username": user.username.strip()}

@app.post("/api/login")
async def login_user(user: UserLogin):
    db_user = get_user_by_username(user.username.strip())
    if not db_user:
        raise HTTPException(status_code=401, detail="Invalid username or password")
    
    if not verify_password(user.password, db_user["password_hash"]):
        raise HTTPException(status_code=401, detail="Invalid username or password")
    
    token = create_session(db_user["id"])
    return {"status": "success", "username": db_user["username"], "token": token}

@app.post("/api/logout")
async def logout(authorization: Optional[str] = Header(None)):
    if authorization and authorization.startswith("Bearer "):
        token = authorization.split(" ")[1]
        delete_session(token)
    return {"message": "Logged out successfully"}

@app.get("/api/macros/targets")
async def get_macro_targets(current_user: dict = Depends(get_current_user)):
    """API endpoint to securely fetch the user's current macronutrient targets"""
    profile = get_user_profile(current_user['id'])
    
    if not profile:
        # Fallback to defaults in case database insertion failed
        return {
            "calories": 2000,
            "protein_g": 150,
            "carbs_g": 200,
            "fat_g": 65
        }
        
    return {
        "calories": profile.get("target_calories", 2000),
        "protein_g": profile.get("target_protein_g", 150),
        "carbs_g": profile.get("target_carbs_g", 200),
        "fat_g": profile.get("target_fat_g", 65)
    }

@app.post("/chat")
async def chat_endpoint(request: ChatRequest, current_user: dict = Depends(get_current_user)):
    """Proxy chat requests to the local Ollama instance with streaming support.
    Automatically enriches prompts with verified local SQLite nutritional data.
    """
    # Keep only the last 6 messages for context window performance on CPU
    all_messages = [msg.model_dump() for msg in request.messages]
    messages = all_messages[-6:]
    
    # Save the latest user message to DB
    if messages and messages[-1]['role'] == 'user':
        save_chat_message(current_user['id'], 'user', messages[-1]['content'])
    
    # --- TG-35: Local SQL RAG Enrichment ---
    db_context = extract_food_context(messages)
    if db_context:
        # Prepend as a system message so it acts as grounded knowledge
        # We ensure it's a short, concise instruction to prevent context bloat
        messages = [{"role": "system", "content": db_context}] + messages

    logger.info(f"[Chat] User '{current_user['username']}' is chatting. Context items: {'Yes' if db_context else 'No'}. Message count: {len(messages)}")
    
    payload = {
        "model": MODEL_NAME,
        "messages": messages,
        "stream": True
    }
    
    async def generate_response():
        try:
            bot_full_response = ""
            async with httpx.AsyncClient(timeout=300.0) as client:
                # Use a combined timeout for the entire request
                async with client.stream("POST", OLLAMA_URL, json=payload, timeout=300.0) as response:
                    if response.status_code != 200:
                        error_detail = await response.aread()
                        logger.error(f"Ollama returned error {response.status_code}: {error_detail}")
                        yield f"data: {json.dumps({'error': f'LLM Error ({response.status_code})'})}\n\n"
                        return

                    async for line in response.aiter_lines():
                        if line:
                            try:
                                data = json.loads(line)
                                if "message" in data and "content" in data["message"]:
                                    content = data["message"]["content"]
                                    bot_full_response += content
                                    yield f"data: {json.dumps({'content': content})}\n\n"
                                if data.get("done"):
                                    break
                            except json.JSONDecodeError:
                                continue
            
            # Save final bot response to DB
            if bot_full_response.strip():
                save_chat_message(current_user['id'], 'assistant', bot_full_response)
                
        except Exception as e:
            logger.exception(f"Unexpected error during chat stream: {e}")
            yield f"data: {json.dumps({'error': 'A technical error occurred while generating the response.'})}\n\n"

    return StreamingResponse(generate_response(), media_type="text/event-stream")

@app.get("/api/chat/history")
async def get_history(current_user: dict = Depends(get_current_user)):
    """Fetch the chat history for the authenticated user"""
    history = get_user_chat_history(current_user['id'])
    return {"history": history}

@app.get("/api/food/search")
async def search_food(q: str, current_user: dict = Depends(get_current_user)):
    """API endpoint to search for food items securely using token authentication"""
    if not q or len(q.strip()) < 1:
        return {"results": []}
    
    logger.info(f"User {current_user['username']} searched for [{q}]")
    results = search_foods_by_name(q.strip(), limit=15)
    return {"results": results}

@app.get("/api/food/{food_id}")
async def get_food_detail(food_id: int, current_user: dict = Depends(get_current_user)):
    """API endpoint to fetch structured nutritional details for a specific food item"""
    food = get_food_by_id(food_id)
    if not food:
        raise HTTPException(status_code=404, detail="Food item not found")
    
    # Structure the data as proposed in the implementation plan
    structured_data = {
        "id": food["id"],
        "name": food["name"],
        "category": food["category"],
        "base_weight_g": food["base_weight_g"],
        "macros": {
            "calories": food["calories"],
            "protein_g": food["protein_g"],
            "fat_g": food["fat_g"],
            "carbs_g": food["carbs_g"]
        },
        "extended": {
            "fiber_g": food["fiber_g"],
            "sugar_g": food["sugar_g"],
            "cholesterol_mg": food["cholesterol_mg"]
        },
        "vitamins": {
            "vitamin_a_iu": food["vitamin_a_iu"],
            "vitamin_c_mg": food["vitamin_c_mg"]
        },
        "minerals": {
            "calcium_mg": food["calcium_mg"],
            "iron_mg": food["iron_mg"],
            "potassium_mg": food["potassium_mg"],
            "sodium_mg": food["sodium_mg"]
        },
        "source": food["source"]
    }
    
    return structured_data

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True)