File size: 4,401 Bytes
325b94c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | # import os
# import json
# import shutil
# from fastapi import APIRouter, HTTPException, UploadFile, Form, Request
# from crewai import Crew, Process
# from agents.design_phase import search_engine_agent, search_engine_task
# from schemas import DNAMetadata, OutlineInput
# from modules import TRUSTED_SITES
# router = APIRouter(prefix="/design", tags=["Design"])
# @router.post("/source_finder")
# async def run_training(request: Request, file: UploadFile, data: str = Form(...)):
# """Uploads keywords JSON + metadata JSON, runs CrewAI search, returns download link"""
# # โ
Parse metadata JSON manually
# try:
# parsed_data = json.loads(data)
# metadata = OutlineInput(**parsed_data)
# except Exception as e:
# raise HTTPException(
# status_code=400, detail=f"Invalid JSON in 'data' field: {str(e)}"
# )
# # โ
Save uploaded file
# save_path = f"/tmp/{file.filename}"
# with open(save_path, "wb") as buffer:
# shutil.copyfileobj(file.file, buffer)
# # โ
Validate extension
# ext = save_path.lower().split(".")[-1]
# if ext != "json":
# raise HTTPException(status_code=400, detail="File must be a JSON file")
# # โ
Load keywords file
# with open(save_path, "r", encoding="utf-8") as f:
# try:
# units_data = json.load(f)
# except json.JSONDecodeError:
# raise HTTPException(status_code=400, detail="Invalid JSON file content")
# # โ
Initialize Crew
# crew = Crew(
# agents=[search_engine_agent],
# tasks=[search_engine_task],
# process=Process.sequential,
# )
# # โ
Prepare static user input
# user_inputs = DNAMetadata(
# topic=metadata.topic,
# domain=metadata.domain,
# content_type=metadata.content_type,
# audience=metadata.audience,
# material_type=metadata.material_type,
# ).dict()
# all_results = []
# # total_prompt = 0
# # total_completion = 0
# # โ
Loop through each subtopic and query
# for unit in units_data:
# unit_title = unit["unit_title"]
# subtopic_title = unit["subtopic_title"]
# queries = unit["queries"]
# for query in queries:
# print(f"๐ Running search for [{subtopic_title}] | Query: {query}")
# merged_input = {
# **user_inputs,
# "score_th": 0.6,
# "no_links": 3,
# "queries": query,
# "unit_title": unit_title,
# "subtopic_title": subtopic_title,
# "TRUSTED_SITES": TRUSTED_SITES,
# }
# try:
# result = crew.kickoff(inputs=merged_input)
# all_results.append(result.json_dict)
# # usage = result.token_usage # CrewAI ุจูุญุณุจูุง ุฌุงูุฒ
# # total_prompt += usage["prompt_tokens"]
# # total_completion += usage["completion_tokens"]
# # total_tokens += usage["total_tokens"]
# except Exception as e:
# print(f"โ ๏ธ Error while running query '{query}': {e}")
# output_data = {"results": all_results}
# # โ
Save results to file
# output_file = f"/tmp/search_results"
# with open(output_file, "w", encoding="utf-8") as f:
# json.dump(output_data, f, ensure_ascii=False, indent=2)
# # โ
Create downloadable link
# base_url = str(request.base_url).rstrip("/")
# download_link = (
# f"{base_url}/design/download?filename={os.path.basename(output_file)}"
# )
# return {
# "message": "Search process completed successfully ๐",
# "total_queries": len(all_results),
# # "total_prompt": total_prompt,
# # "total_completion": total_completion,
# # "total_tokens": total_tokens,
# "download_link": download_link,
# "result": all_results,
# "json_dict": output_data,
# }
# # โ
New endpoint for downloading results
# @router.get("/download")
# async def download_file(filename: str):
# file_path = f"/tmp/{filename}"
# if not os.path.exists(file_path):
# raise HTTPException(status_code=404, detail="File not found")
# with open(file_path, "r", encoding="utf-8") as f:
# data = json.load(f)
# return data
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