Initial commit for AI-service

backend/Dockerfile

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+# Use official Python 3.11 image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements first for caching
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# Copy backend source code
+COPY . .
+
+# Expose FastAPI port
+EXPOSE 8089
+
+# Run FastAPI with uvicorn
+CMD ["uvicorn", "backend:app", "--host", "0.0.0.0", "--port", "8089", "--reload"]

backend/backend.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from datetime import datetime
+import re
+import requests
+
+app = FastAPI(title="PC Budget Assistant API")
+
+# Allow React frontend to call API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # or ["http://localhost:5173"] to restrict
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --------------------------
+# Knowledge base
+# --------------------------
+KNOWLEDGE_BASE = {
+    "budget_ranges": [
+        {
+            "range": "$300-$500",
+            "cpu": "Intel Pentium Gold or AMD Athlon",
+            "gpu": "Integrated Graphics",
+            "ram": "8GB DDR4",
+            "storage": "256GB SSD",
+            "use_case": "Basic computing, web browsing, office work"
+        },
+        {
+            "range": "$600-$900",
+            "cpu": "Intel Core i3-12100F or AMD Ryzen 5 5600",
+            "gpu": "GTX 1650 or RX 6500 XT",
+            "ram": "16GB DDR4",
+            "storage": "500GB NVMe SSD",
+            "use_case": "Gaming at 1080p, content creation, multitasking"
+        },
+        {
+            "range": "$1000-$1500",
+            "cpu": "Intel Core i5-13400F or AMD Ryzen 5 7600",
+            "gpu": "RTX 4060 or RX 7600",
+            "ram": "16GB DDR4/DDR5",
+            "storage": "1TB NVMe SSD",
+            "use_case": "High-end gaming, streaming, video editing"
+        },
+        {
+            "range": "$1500-$2500",
+            "cpu": "Intel Core i7-14700K or AMD Ryzen 7 7800X3D",
+            "gpu": "RTX 4070 Super or RX 7800 XT",
+            "ram": "32GB DDR5",
+            "storage": "2TB NVMe Gen4 SSD",
+            "use_case": "4K gaming, professional work, heavy multitasking"
+        }
+    ],
+    "components": {
+        "cpu": "The processor is the brain of your computer.",
+        "gpu": "Graphics card handles visuals and gaming.",
+        "ram": "Memory for running programs.",
+        "storage": "SSD (Solid State Drive) is faster than HDD.",
+        "motherboard": "Connects all components. Must match CPU socket.",
+        "psu": "Power Supply Unit. Bronze/Gold/Platinum ratings show efficiency.",
+        "case": "Houses all components. Ensure good airflow."
+    },
+    "tips": [
+        "Check component compatibility before buying",
+        "Invest more in GPU if gaming is priority",
+        "Don't cheap out on PSU - it powers everything",
+        "SSD for OS is essential",
+        "Consider future upgradability"
+    ]
+}
+
+# --------------------------
+# Pydantic models
+# --------------------------
+class QueryRequest(BaseModel):
+    query: str
+
+class QueryResponse(BaseModel):
+    query: str
+    context: str
+    response: str
+    timestamp: str
+
+# --------------------------
+# RAG System
+# --------------------------
+class RAGSystem:
+    def __init__(self):
+        self.knowledge_base = KNOWLEDGE_BASE
+
+    def retrieve_context(self, query: str) -> str:
+        """Retrieve context for a query"""
+        query_lower = query.lower()
+        context = []
+
+        # Match budget
+        budget_match = re.search(r'\$?(\d+)', query_lower)
+        if budget_match:
+            budget = int(budget_match.group(1))
+            for b in self.knowledge_base["budget_ranges"]:
+                min_b, max_b = map(int, b["range"].replace("$", "").split("-"))
+                if min_b <= budget <= max_b:
+                    context.append(f"Budget: {b['range']}\nCPU: {b['cpu']}\nGPU: {b['gpu']}\nRAM: {b['ram']}\nStorage: {b['storage']}\nUse Case: {b['use_case']}")
+
+        # Match components
+        for comp, desc in self.knowledge_base["components"].items():
+            if comp in query_lower:
+                context.append(f"{comp.upper()}: {desc}")
+
+        # Tips
+        if any(w in query_lower for w in ["tip", "advice", "recommend", "help"]):
+            context.append("Tips:\n" + "\n".join(f"- {t}" for t in self.knowledge_base["tips"][:3]))
+
+        if any(w in query_lower for w in ["option", "choice", "range", "budget"]):
+            for b in self.knowledge_base["budget_ranges"]:
+                context.append(f"{b['range']}: {b['use_case']}")
+
+        return "\n\n".join(context) if context else "General PC building info available."
+
+    def generate_response(self, query: str, context: str) -> str:
+        """Simple fallback response for React frontend"""
+        if "General PC building info available." in context:
+            return "I can help you build PCs! Ask me about budgets like '$800' or components like 'GPU'."
+        return f"Based on your question about '{query}':\n{context}"
+
+# Initialize RAG system
+rag_system = RAGSystem()
+
+# --------------------------
+# API Endpoints
+# --------------------------
+@app.get("/")
+async def root():
+    return {"message": "PC Budget Assistant API", "version": "1.0"}
+
+@app.post("/query", response_model=QueryResponse)
+async def query_pc(request: QueryRequest):
+    if not request.query.strip():
+        raise HTTPException(status_code=400, detail="Query cannot be empty")
+
+    context = rag_system.retrieve_context(request.query)
+    response = rag_system.generate_response(request.query, context)
+
+    return QueryResponse(
+        query=request.query,
+        context=context,
+        response=response,
+        timestamp=datetime.now().isoformat()
+    )
+
+@app.get("/budgets")
+async def get_budgets():
+    return {"budgets": KNOWLEDGE_BASE["budget_ranges"]}
+
+@app.get("/components")
+async def get_components():
+    return {"components": KNOWLEDGE_BASE["components"]}
+
+@app.get("/tips")
+async def get_tips():
+    return {"tips": KNOWLEDGE_BASE["tips"]}

backend/requirements.txt

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+fastapi
+uvicorn
+tkinter