Spaces:

aliroya32
/

ContiAI

Sleeping

File size: 16,120 Bytes

325b94c

# import os
# import json
# import shutil
# from fastapi import APIRouter, HTTPException, UploadFile, Form, Request
# from crewai import Crew, Process
# from agents.design_phase import scraping_built_in_agent, scraping_built_in_task,scraping_bs4_agent,scraping_bs4_task
# from schemas import DNAMetadata, OutlineInput
# from tools import scrape_with_bs4, crawl_parse_url,crawl_bs_url,extract_pdf_content

# router = APIRouter(prefix="/design", tags=["Design"])

# #################################
# #           Built-in            #
# #################################
# @router.post("/scraper_built_in")
# # async def run_training(request: Request, file: UploadFile, data: str = Form(...)):
#     """Uploads keywords JSON + metadata JSON, runs CrewAI search, returns download link."""

#     # ✅ Parse metadata JSON
#     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': {e}")

#     # ✅ Save uploaded file temporarily
#     save_path = f"/tmp/{file.filename}"
#     with open(save_path, "wb") as buffer:
#         shutil.copyfileobj(file.file, buffer)

#     # ✅ Validate file extension
#     if not save_path.lower().endswith(".json"):
#         raise HTTPException(status_code=400, detail="File must be a JSON file")

#     # ✅ Load file content
#     try:
#         with open(save_path, "r", encoding="utf-8") as f:
#             urls_data = json.load(f)
#     except json.JSONDecodeError:
#         raise HTTPException(status_code=400, detail="Invalid JSON file content")

#     # ✅ Initialize Crew
#     crew = Crew(
#         agents=[scraping_built_in_agent],
#         tasks=[scraping_built_in_task],
#         process=Process.sequential,
#     )

#     # ✅ Build static user metadata
#     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 = []

#     # ✅ Iterate through each topic unit and result link
#     for unit in urls_data["results"]:
#         unit_title = unit["unit_title"]
#         subtopic_title = unit["subtopic_title"]
#         query = unit["query"]

#         for result_item in unit["results"]:
#             url = result_item["url"]
#             print(f"🔍 Running scrape for [{subtopic_title}] | URL: {url}")

#             merged_input = {
#                 **user_inputs,
#                 "url": url,
#                 "unit_title": unit_title,
#                 "subtopic_title": subtopic_title,
#                 "query": query,
#             }

#             try:
#                 result = crew.kickoff(inputs=merged_input)
#                 all_results.append(result.dict())

#                 # usage = result.token_usage  # CrewAI بيحسبها جاهز

#                 # total_prompt += usage["prompt_tokens"]
#                 # total_completion += usage["completion_tokens"]
#                 # total_tokens += usage["total_tokens"]
#             except Exception as e:
#                 print(f"⚠️ Error while processing '{url}': {e}")

#     # ✅ Save aggregated results
#     output_data = {"results": all_results}
#     output_file = "/tmp/search_results.json"
#     with open(output_file, "w", encoding="utf-8") as f:
#         json.dump(output_data, f, ensure_ascii=False, indent=2)

#     # ✅ Build download URL
#     base_url = str(request.base_url).rstrip("/")
#     download_link = (
#         f"{base_url}/design/download?filename={os.path.basename(output_file)}"
#     )

#     return {
#         "message": "Scraping process completed successfully 🚀",
#         "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,
#     }

# #################################
# #             BS4               #
# #################################
# @router.post("/scraper_bs4_agent")
# async def run_training(request: Request, file: UploadFile, data: str = Form(...)):
#     """Uploads keywords JSON + metadata JSON, runs CrewAI search, returns download link."""

#     # ✅ Parse metadata JSON
#     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': {e}")

#     # ✅ Save uploaded file temporarily
#     save_path = f"/tmp/{file.filename}"
#     with open(save_path, "wb") as buffer:
#         shutil.copyfileobj(file.file, buffer)

#     # ✅ Validate file extension
#     if not save_path.lower().endswith(".json"):
#         raise HTTPException(status_code=400, detail="File must be a JSON file")

#     # ✅ Load file content
#     try:
#         with open(save_path, "r", encoding="utf-8") as f:
#             urls_data = json.load(f)
#     except json.JSONDecodeError:
#         raise HTTPException(status_code=400, detail="Invalid JSON file content")

#     # ✅ Initialize Crew
#     crew = Crew(
#         agents=[scraping_bs4_agent],
#         tasks=[scraping_bs4_task],
#         process=Process.sequential,
#     )

#     # ✅ Build static user metadata
#     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 = []

#     # ✅ Iterate through each topic unit and result link
#     for unit in urls_data["results"]:
#         unit_title = unit["unit_title"]
#         subtopic_title = unit["subtopic_title"]
#         query = unit["query"]

#         for result_item in unit["results"]:
#             url = result_item["url"]
#             print(f"🔍 Running scrape for [{subtopic_title}] | URL: {url}")

#             merged_input = {
#                 **user_inputs,
#                 "url": url,
#                 "unit_title": unit_title,
#                 "subtopic_title": subtopic_title,
#                 "query": query,
#             }

#             try:
#                 result = crew.kickoff(inputs=merged_input)
#                 all_results.append(result.dict())

#                 # usage = result.token_usage  # CrewAI بيحسبها جاهز

#                 # total_prompt += usage["prompt_tokens"]
#                 # total_completion += usage["completion_tokens"]
#                 # total_tokens += usage["total_tokens"]
#             except Exception as e:
#                 print(f"⚠️ Error while processing '{url}': {e}")

#     # ✅ Save aggregated results
#     output_data = {"results": all_results}
#     output_file = "/tmp/search_results.json"
#     with open(output_file, "w", encoding="utf-8") as f:
#         json.dump(output_data, f, ensure_ascii=False, indent=2)

#     # ✅ Build download URL
#     base_url = str(request.base_url).rstrip("/")
#     download_link = (
#         f"{base_url}/design/download?filename={os.path.basename(output_file)}"
#     )

#     return {
#         "message": "Scraping process completed successfully 🚀",
#         "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,
#     }

# # #################################
# # #           crawlee             #
# # #################################
# # @router.post("/scraper_crawlee_agent")
# # async def run_training(request: Request, file: UploadFile, data: str = Form(...)):
# #     """Uploads keywords JSON + metadata JSON, runs CrewAI search, returns download link."""

# #     # ✅ Parse metadata JSON
# #     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': {e}")

# #     # ✅ Save uploaded file temporarily
# #     save_path = f"/tmp/{file.filename}"
# #     with open(save_path, "wb") as buffer:
# #         shutil.copyfileobj(file.file, buffer)

# #     # ✅ Validate file extension
# #     if not save_path.lower().endswith(".json"):
# #         raise HTTPException(status_code=400, detail="File must be a JSON file")

# #     # ✅ Load file content
# #     try:
# #         with open(save_path, "r", encoding="utf-8") as f:
# #             urls_data = json.load(f)
# #     except json.JSONDecodeError:
# #         raise HTTPException(status_code=400, detail="Invalid JSON file content")

# #     # ✅ Initialize Crew
# #     crew = Crew(
# #         agents=[scraping_built_in_agent],
# #         tasks=[scraping_built_in_task],
# #         process=Process.sequential,
# #     )

# #     # ✅ Build static user metadata
# #     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 = []

# #     # ✅ Iterate through each topic unit and result link
# #     for unit in urls_data["results"]:
# #         unit_title = unit["unit_title"]
# #         subtopic_title = unit["subtopic_title"]
# #         query = unit["query"]

# #         for result_item in unit["results"]:
# #             url = result_item["url"]
# #             print(f"🔍 Running scrape for [{subtopic_title}] | URL: {url}")

# #             merged_input = {
# #                 **user_inputs,
# #                 "url": url,
# #                 "unit_title": unit_title,
# #                 "subtopic_title": subtopic_title,
# #                 "query": query,
# #             }

# #             try:
# #                 result = crew.kickoff(inputs=merged_input)
# #                 all_results.append(result.dict())
# #             except Exception as e:
# #                 print(f"⚠️ Error while processing '{url}': {e}")

# #     # ✅ Save aggregated results
# #     output_data = {"results": all_results}
# #     output_file = "/tmp/search_results.json"
# #     with open(output_file, "w", encoding="utf-8") as f:
# #         json.dump(output_data, f, ensure_ascii=False, indent=2)

# #     # ✅ Build download URL
# #     base_url = str(request.base_url).rstrip("/")
# #     download_link = (
# #         f"{base_url}/design/download?filename={os.path.basename(output_file)}"
# #     )

# #     return {
# #         "message": "Scraping process completed successfully 🚀",
# #         "total_queries": len(all_results),
# #         "download_link": download_link,
# #         "result": all_results,
# #         "json_dict": output_data,
# #     }


# ##############################
# # ===================================================================
# #  SHARED HELPER
# # ===================================================================
# async def process_json_scrape(request: Request, file: UploadFile, data: str, mode: str):
#     """
#     mode = 'bs4' or 'crawlee'
#     """
#     # ---- Parse metadata JSON ----
#     try:
#         metadata = json.loads(data)
#     except Exception as e:
#         raise HTTPException(status_code=400, detail=f"Invalid metadata JSON: {e}")

#     # ---- Save JSON file ----
#     save_path = f"/tmp/{file.filename}"
#     with open(save_path, "wb") as buffer:
#         shutil.copyfileobj(file.file, buffer)

#     if not save_path.endswith(".json"):
#         raise HTTPException(status_code=400, detail="Uploaded file must be .json")

#     # ---- Load JSON content ----
#     try:
#         with open(save_path, "r", encoding="utf-8") as f:
#             urls_data = json.load(f)
#     except Exception:
#         raise HTTPException(status_code=400, detail="Invalid JSON file content")

#     all_results = []

#     # ---- Loop through your structure ----
#     for unit in urls_data["results"]:
#         unit_title = unit["unit_title"]
#         subtopic_title = unit["subtopic_title"]
#         query = unit["query"]

#         for result_item in unit["results"]:
#             url = result_item["url"]

#             print(f"🔍 Scraping | {subtopic_title} | {url}")

#             try:
#                 # ----- PDF case -----
#                 if url.lower().endswith(".pdf"):
#                     scraped = {
#                         "page_url": url,
#                         "title": "",
#                         "content": extract_pdf_content(url),
#                         "img_url": [],
#                         "video_url": [],
#                         "audio_url": [],
#                         "pdf_url": [],
#                         "agent_recommendation_rank": 4.2,
#                         "agent_recommendation_notes": "Scraped successfully using Crawlee + ParselCrawler.",
#                         "header": "Web Scraping Test",
#                         "sub_header": "Crawlee Version",
#                     }
#                 elif mode == "bs4":
#                     scraped = scrape_with_bs4(url)
#                 elif mode == "crawlee bs":
#                     scraped = await crawl_bs_url(url)

#                 elif mode == "crawlee parsel":
#                     scraped = await crawl_parse_url(url)

#                 all_results.append(
#                     {
#                         "unit_title": unit_title,
#                         "subtopic_title": subtopic_title,
#                         "query": query,
#                         "parts": scraped,
#                     }
#                 )

#             except Exception as e:
#                 all_results.append(
#                     {
#                         "unit_title": unit_title,
#                         "subtopic_title": subtopic_title,
#                         "query": query,
#                         "url": url,
#                         "error": str(e),
#                     }
#                 )

#     # ---- Save Output ----
#     output_file = "/tmp/scrape_results.json"
#     with open(output_file, "w", encoding="utf-8") as f:
#         json.dump({"results": all_results}, f, ensure_ascii=False, indent=2)

#     # ---- Download link ----
#     base_url = str(request.base_url).rstrip("/")
#     download_link = f"{base_url}/design/download?filename=scrape_results.json"

#     return {
#         "message": f"Scraping completed using {mode.upper()} ✔",
#         "total_links": len(all_results),
#         "download_link": download_link,
#         "results": all_results,
#     }


# # ===================================================================
# #  ROUTE 1 — BS4 SCRAPER
# # ===================================================================
# @router.post("/scraper_bs4")
# async def scraper_bs4(request: Request, file: UploadFile, data: str = Form(...)):
#     return await process_json_scrape(request, file, data, mode="bs4")


# # ===================================================================
# #  ROUTE 2 — CRAWLEE SCRAPER PARSEL
# # ===================================================================
# @router.post("/scraper_crawlee_parsel")
# async def scraper_crawlee(request: Request, file: UploadFile, data: str = Form(...)):
#     return await process_json_scrape(request, file, data, mode="crawlee parsel")


# # ===================================================================
# #  ROUTE 2 — CRAWLEE SCRAPER BESUTIFULSOUP
# # ===================================================================
# @router.post("/scraper_crawlee_bs")
# async def scraper_crawlee(request: Request, file: UploadFile, data: str = Form(...)):
#     return await process_json_scrape(request, file, data, mode="crawlee bs")