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# backend/app.py
from flask import Flask, request, jsonify
from flask_cors import CORS
from datetime import datetime
from io import BytesIO
import mammoth
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import FileReadTool, MDXSearchTool
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
app = Flask(__name__)
CORS(app)
load_dotenv()
openai_api_key = os.getenv("openai_api_key")
os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'
os.environ["OPENAI_API_KEY"] = openai_api_key
with open('./brd-template/brd-template.md', 'r', encoding='utf-8') as file:
brd_template_content = file.read()
cleaned_brd_template = brd_template_content.replace('\ufeff', '')
def call_crew_kickoff(str_current_datetime):
mt_tool = FileReadTool(txt=f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md')
semantic_search_resume = MDXSearchTool(mdx=f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md')
with open(f'./meeting-transcription/meeting-transcript_{str_current_datetime}.md', 'r', encoding='utf-8') as file:
transcript_content = file.read()
cleaned_transcript_content = transcript_content.replace('\ufeff', '')
business_analyst = Agent(
role="Business Analyst",
goal="Translate the meeting transcript into a BRD using the provided template.",
tools=[mt_tool, semantic_search_resume],
allow_delegation=False,
verbose=True,
backstory="Background in business analysis."
)
subject_matter_expert = Agent(
role="Subject Matter Expert",
goal="Ensure the BRD reflects technical feasibility.",
tools=[mt_tool, semantic_search_resume],
allow_delegation=False,
verbose=True,
backstory="Expert in the project's domain."
)
analyze_meeting_for_brd = Task(
description="Analyze the meeting transcript and create a BRD.",
expected_output="A well-structured BRD.",
agent=business_analyst,
)
sme_technical_review = Task(
description="Review the BRD for technical accuracy.",
expected_output="A refined BRD document.",
agent=subject_matter_expert,
)
crew = Crew(
agents=[business_analyst, subject_matter_expert],
tasks=[analyze_meeting_for_brd, sme_technical_review],
verbose=2,
manager_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
process=Process.hierarchical,
memory=True,
)
result = crew.kickoff(inputs={'datetime': str_current_datetime})
return result
@app.route('/upload', methods=['POST'])
def upload_file():
if 'file' not in request.files:
return jsonify({"error": "No file provided"}), 400
file = request.files['file']
# Validate the file type
if not file.filename.endswith('.docx'):
return jsonify({"error": "Invalid file type. Only .docx files are supported."}), 400
current_datetime = datetime.now().strftime("%Y-%m-%d %H-%M-%S")
filename = f'./meeting-transcription/meeting-transcript_{current_datetime}.md'
# Use BytesIO to handle the file content properly
file_content = file.read()
file_stream = BytesIO(file_content)
try:
result = mammoth.convert_to_markdown(file_stream)
with open(filename, 'w', encoding='utf-8') as f:
f.write(result.value)
except Exception as e:
return jsonify({"error": str(e)}), 500
response = call_crew_kickoff(current_datetime)
output_filename = f"./generated-brd/generated-brd_{current_datetime}.md"
with open(output_filename, 'w', encoding='utf-8') as f:
f.write(response)
return jsonify({"file_url": output_filename, "brd_content": response})
if __name__ == '__main__':
if not os.path.exists('./meeting-transcription'):
os.makedirs('./meeting-transcription')
if not os.path.exists('./generated-brd'):
os.makedirs('./generated-brd')
app.run(debug=True, port=5000)
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