Create main.py
Browse files
main.py
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import httpx
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import os
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# --- Configuration ---
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# In a real application, use a proper search API and manage keys securely.
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# For this example, we'll use a placeholder.
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SEARCH_API_URL = "https://rkihacker-brave.hf.space/search"
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# --- System Prompt ---
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SYSTEM_PROMPT = """
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You are Binglity, a helpful and friendly AI assistant with web search capabilities.
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Your goal is to provide accurate and relevant information to users by searching the web.
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When responding, synthesize the information from the search results into a coherent answer.
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Always be polite and informative.
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"""
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# --- FastAPI App ---
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app = FastAPI(
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title="Binglity API",
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description="A web search-powered chatbot alternative to Perplexity.",
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version="1.0.0",
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)
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# --- Pydantic Models ---
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class SearchRequest(BaseModel):
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query: str
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class SearchResult(BaseModel):
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title: str
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url: str
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description: str
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class SearchResponse(BaseModel):
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binglity_response: str
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search_results: list[SearchResult]
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# --- Web Search Function ---
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async def perform_web_search(query: str) -> list[SearchResult]:
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"""
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Performs a web search using an external API and returns a list of search results.
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"""
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async with httpx.AsyncClient() as client:
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try:
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response = await client.get(
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SEARCH_API_URL,
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params={"query": query, "max_results": 5}
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)
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response.raise_for_status()
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results = response.json()
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return [SearchResult(**item) for item in results]
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except httpx.HTTPStatusError as e:
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raise HTTPException(status_code=e.response.status_code, detail=f"Error from search API: {e.response.text}")
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"An unexpected error occurred during web search: {str(e)}")
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# --- API Endpoint ---
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@app.post("/search", response_model=SearchResponse)
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async def search(request: SearchRequest):
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"""
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Takes a user query, performs a web search, and returns a response from Binglity.
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"""
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search_results = await perform_web_search(request.query)
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# --- Simulate Model Response ---
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# In a real application, you would pass the system prompt, query, and search results
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# to a large language model to generate a more nuanced response.
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if not search_results:
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binglity_response = "I couldn't find any relevant information for your query."
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else:
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response_intro = f"Here's what I found for '{request.query}':\n\n"
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formatted_results = "\n\n".join(
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f"Title: {res.title}\nDescription: {res.description}\nURL: {res.url}"
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for res in search_results
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)
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binglity_response = SYSTEM_PROMPT + "\n\n" + response_intro + formatted_results
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return {
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"binglity_response": binglity_response,
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"search_results": search_results,
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}
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@app.get("/")
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def read_root():
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return {"message": "Welcome to the Binglity API"}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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