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import uvicorn |
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import base64 |
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from fastapi import FastAPI, File, UploadFile |
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from fastapi.responses import JSONResponse |
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from datetime import datetime |
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import mammoth |
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import os |
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from crewai import Agent, Task, Crew, Process |
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from crewai_tools import FileReadTool, MDXSearchTool |
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from langchain_openai import ChatOpenAI |
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from dotenv import load_dotenv |
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app = FastAPI() |
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load_dotenv() |
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openai_api_key = os.getenv("openai_api_key") |
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os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo' |
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os.environ["OPENAI_API_KEY"] = openai_api_key |
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@app.post("/upload") |
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async def upload_file(file: dict): |
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current_datetime = datetime.now().strftime("%Y-%m-%d %H-%M-%S") |
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filename = f'meeting-transcription/meeting-transcript_{current_datetime}.md' |
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print(filename) |
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content = base64.b64decode(file.get("document")) |
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src_filename = file.get("filename") |
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with open(f"{src_filename}", "wb") as docx_file: |
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docx_file.write(content) |
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with open(src_filename, "rb") as docx_file: |
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result = mammoth.convert_to_markdown(docx_file) |
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with open(filename, 'w', encoding='utf-8') as f: |
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f.write(result.value) |
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response = call_crew_kickoff(current_datetime) |
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output_filename = f"generated-brd/generated-brd_{current_datetime}.md" |
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with open(output_filename, 'w', encoding='utf-8') as f: |
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f.write(response) |
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return JSONResponse(content={"file_url": output_filename, "brd_content": response}) |
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def call_crew_kickoff(str_current_datetime): |
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mt_tool = FileReadTool(txt=f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md') |
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semantic_search_resume = MDXSearchTool(mdx=f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md') |
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with open(f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md', 'r', encoding='utf-8') as file: |
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transcript_content = file.read() |
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cleaned_transcript_content = transcript_content.replace('\ufeff', '') |
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with open('./brd-template/brd-template.md', 'r', encoding='utf-8') as file: |
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brd_template_content = file.read() |
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cleaned_brd_template = brd_template_content.replace('\ufeff', '') |
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business_analyst = Agent( |
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role="Business Analyst", |
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goal="Effectively translate the meeting transcript and discussions into a well-structured BRD...", |
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tools=[mt_tool, semantic_search_resume], |
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allow_delegation=False, |
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verbose=True, |
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backstory="You come from a background in business analysis..." |
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) |
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subject_matter_expert = Agent( |
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role="Subject Matter Expert", |
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goal="Ensure the BRD accurately reflects the project's technical feasibility...", |
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tools=[mt_tool, semantic_search_resume], |
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allow_delegation=False, |
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verbose=True, |
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backstory="You possess in-depth knowledge and experience specific to the project's domain..." |
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) |
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analyze_meeting_for_brd = Task( |
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description="Analyze the meeting transcript and create a BRD...", |
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expected_output="A well-structured BRD...", |
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agent=business_analyst, |
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) |
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sme_technical_review = Task( |
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description="Review the BRD for technical accuracy...", |
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expected_output="Comprehensive and refined BRD document...", |
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agent=subject_matter_expert, |
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) |
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crew = Crew( |
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agents=[business_analyst, subject_matter_expert], |
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tasks=[analyze_meeting_for_brd, sme_technical_review], |
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verbose=2, |
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manager_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"), |
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process=Process.hierarchical, |
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memory=True, |
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) |
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result = crew.kickoff(inputs={'datetime': str_current_datetime}) |
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return result |
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