Create main.py

main.py

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+import os
+import re
+import requests
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Optional
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+# Add CORS for Blazor WASM
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class ChatHistoryItem(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    message: str
+    history: List[ChatHistoryItem]
+
+class BookEnrichment(BaseModel):
+    title: Optional[str] = None
+    author: Optional[str] = None
+    cover_url: Optional[str] = None
+    description: Optional[str] = None
+    isbn: Optional[str] = None
+
+class ChatResponse(BaseModel):
+    response: str
+    book_data: Optional[BookEnrichment] = None
+
+# HF Inference API Settings
+# You can get a token from https://huggingface.co/settings/tokens
+HF_TOKEN = os.getenv("HF_TOKEN")
+MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2"
+
+def query_llm(prompt: str):
+    if not HF_TOKEN:
+        return "I'm a local librarian helper. (Note: HF_TOKEN is missing in backend settings)"
+    
+    api_url = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    
+    # Format prompt for Mistral
+    # Instruction: ... 
+    # [INST] user message [/INST]
+    
+    payload = {
+        "inputs": f"[INST] You are a professional and friendly librarian at 'LibraryLuxe'. Keep responses concise and helpful. \\n\\n {prompt} [/INST]",
+        "parameters": {"max_new_tokens": 250, "temperature": 0.7}
+    }
+    
+    try:
+        response = requests.post(api_url, headers=headers, json=payload)
+        res_json = response.json()
+        if isinstance(res_json, list) and len(res_json) > 0:
+            full_text = res_json[0].get("generated_text", "")
+            # Remove the prompt from the response if the model echoes it
+            return full_text.split("[/INST]")[-1].strip()
+        return "I encountered an error while thinking. Please try again."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def get_book_details(isbn: str) -> Optional[BookEnrichment]:
+    try:
+        # Open Library API
+        url = f"https://openlibrary.org/api/books?bibkeys=ISBN:{isbn}&format=json&jscmd=data"
+        response = requests.get(url)
+        data = response.json()
+        
+        key = f"ISBN:{isbn}"
+        if key in data:
+            book_info = data[key]
+            return BookEnrichment(
+                title=book_info.get("title"),
+                author=", ".join([a.get("name") for a in book_info.get("authors", [])]),
+                cover_url=book_info.get("cover", {}).get("large"),
+                description=book_info.get("notes", "No description available."),
+                isbn=isbn
+            )
+    except:
+        pass
+    return None
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    message = request.message
+    
+    # 1. Detect ISBN
+    isbn_match = re.search(r"\b\d{10,13}\b", message)
+    book_data = None
+    
+    if isbn_match:
+        isbn = isbn_match.group(0)
+        book_data = get_book_details(isbn)
+        if book_data:
+            # If we found a book, prepend some context for the LLM
+            message = f"User is asking about a book with ISBN {isbn}. I found: {book_data.title} by {book_data.author}. {message}"
+
+    # 2. Call LLM
+    # In a real app, you'd pass the whole history to the LLM
+    history_context = "\\n".join([f"{h.role}: {h.content}" for h in request.history[-4:]])
+    prompt = f"History:\\n{history_context}\\n\\nUser: {message}"
+    
+    ai_response = query_llm(prompt)
+    
+    return ChatResponse(
+        response=ai_response,
+        book_data=book_data
+    )
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)