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Update main.py
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main.py
CHANGED
@@ -1,13 +1,11 @@
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import os
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, Field
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from typing import List
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from crawl4ai import AsyncWebCrawler
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from crawl4ai.extraction_strategy import LLMExtractionStrategy
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import json
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from dotenv import load_dotenv
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load_dotenv() # Load environment variables from .env file
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app = FastAPI()
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@@ -30,6 +28,12 @@ async def crawl(input: CrawlerInput):
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if len(input.columns) != len(input.descriptions):
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raise HTTPException(status_code=400, detail="Number of columns must match number of descriptions")
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async with AsyncWebCrawler(verbose=True) as crawler:
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result = await crawler.arun(
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url=input.url,
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@@ -38,7 +42,7 @@ async def crawl(input: CrawlerInput):
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api_token=os.getenv('OPENAI_API_KEY'),
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extraction_type="schema",
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verbose=True,
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instruction=
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)
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)
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@@ -52,4 +56,4 @@ async def test():
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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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import os
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import asyncio
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, Field
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from typing import List, Optional
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from crawl4ai import AsyncWebCrawler
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from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, LLMExtractionStrategy
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import json
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app = FastAPI()
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if len(input.columns) != len(input.descriptions):
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raise HTTPException(status_code=400, detail="Number of columns must match number of descriptions")
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# Create a dictionary with columns as keys and descriptions as values
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extraction_info = {col: desc for col, desc in zip(input.columns, input.descriptions)}
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# Convert the dictionary to a JSON string
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instruction = f"Extract the following information: {json.dumps(extraction_info)}"
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async with AsyncWebCrawler(verbose=True) as crawler:
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result = await crawler.arun(
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url=input.url,
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api_token=os.getenv('OPENAI_API_KEY'),
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extraction_type="schema",
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verbose=True,
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instruction=instruction
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)
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)
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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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