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Update app.py
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app.py
CHANGED
@@ -1,1171 +1,44 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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#pip install evernote-sdk-python3
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# import evernote.edam.notestore.NoteStore as NoteStore
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# import evernote.edam.type.ttypes as Types
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# from evernote.api.client import EvernoteClient
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# In[3]:
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import os
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import yaml
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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# perspective generation
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import openai
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import os
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from openai import OpenAI
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import gradio as gr
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import json
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import sqlite3
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import uuid
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import socket
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import difflib
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import time
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import shutil
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import requests
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import re
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import json
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import markdown
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from fpdf import FPDF
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import hashlib
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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from todoist_api_python.api import TodoistAPI
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# from flask import Flask, request, jsonify
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from twilio.rest import Client
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import asyncio
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import uvicorn
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import fastapi
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from fastapi import FastAPI
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from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse
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from fastapi.staticfiles import StaticFiles
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import nest_asyncio
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from twilio.twiml.messaging_response import MessagingResponse
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from requests.auth import HTTPBasicAuth
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from google.cloud import storage, exceptions # Import exceptions for error handling
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from google.cloud.exceptions import NotFound
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from google.oauth2 import service_account
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from reportlab.pdfgen import canvas
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from reportlab.lib.pagesizes import letter
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from reportlab.pdfbase import pdfmetrics
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from reportlab.lib import colors
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from reportlab.pdfbase.ttfonts import TTFont
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import logging
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logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
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logger = logging.getLogger(__name__)
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# In[4]:
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# Access the API keys and other configuration data
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openai_api_key = os.environ["OPENAI_API_KEY"]
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# Access the API keys and other configuration data
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todoist_api_key = os.environ["TODOIST_API_KEY"]
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EVERNOTE_API_TOKEN = os.environ["EVERNOTE_API_TOKEN"]
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account_sid = os.environ["TWILLO_ACCOUNT_SID"]
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auth_token = os.environ["TWILLO_AUTH_TOKEN"]
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twilio_phone_number = os.environ["TWILLO_PHONE_NUMBER"]
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google_credentials_json = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
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twillo_client = Client(account_sid, auth_token)
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# Set the GOOGLE_APPLICATION_CREDENTIALS environment variable
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# Load Reasoning Graph JSON File
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def load_reasoning_json(filepath):
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"""Load JSON file and return the dictionary."""
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with open(filepath, "r") as file:
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data = json.load(file)
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return data
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# Load Action Map
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def load_action_map(filepath):
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"""Load action map JSON file and map strings to actual function objects."""
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with open(filepath, "r") as file:
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action_map_raw = json.load(file)
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# Map string names to actual functions using globals()
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return {action: globals()[func_name] for action, func_name in action_map_raw.items()}
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# In[5]:
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# Define all actions as functions
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def find_reference(task_topic):
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"""Finds a reference related to the task topic."""
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print(f"Finding reference for topic: {task_topic}")
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return f"Reference found for topic: {task_topic}"
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def generate_summary(reference):
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"""Generates a summary of the reference."""
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print(f"Generating summary for reference: {reference}")
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return f"Summary of {reference}"
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def suggest_relevance(summary):
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"""Suggests how the summary relates to the project."""
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print(f"Suggesting relevance of summary: {summary}")
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return f"Relevance of {summary} suggested"
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def tool_research(task_topic):
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"""Performs tool research and returns analysis."""
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print("Performing tool research")
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return "Tool analysis data"
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def generate_comparison_table(tool_analysis):
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"""Generates a comparison table for a competitive tool."""
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print(f"Generating comparison table for analysis: {tool_analysis}")
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return f"Comparison table for {tool_analysis}"
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def generate_integration_memo(tool_analysis):
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"""Generates an integration memo for a tool."""
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print(f"Generating integration memo for analysis: {tool_analysis}")
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return f"Integration memo for {tool_analysis}"
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def analyze_issue(task_topic):
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"""Analyzes an issue and returns the analysis."""
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print("Analyzing issue")
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return "Issue analysis data"
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def generate_issue_memo(issue_analysis):
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"""Generates an issue memo based on the analysis."""
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print(f"Generating issue memo for analysis: {issue_analysis}")
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return f"Issue memo for {issue_analysis}"
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def list_ideas(task_topic):
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"""Lists potential ideas for brainstorming."""
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print("Listing ideas")
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return ["Idea 1", "Idea 2", "Idea 3"]
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def construct_matrix(ideas):
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"""Constructs a matrix (e.g., feasibility or impact/effort) for the ideas."""
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print(f"Constructing matrix for ideas: {ideas}")
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return {"Idea 1": "High Impact/Low Effort", "Idea 2": "Low Impact/High Effort", "Idea 3": "High Impact/High Effort"}
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def prioritize_ideas(matrix):
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"""Prioritizes ideas based on the matrix."""
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print(f"Prioritizing ideas based on matrix: {matrix}")
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return ["Idea 3", "Idea 1", "Idea 2"]
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def setup_action_plan(prioritized_ideas):
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"""Sets up an action plan based on the prioritized ideas."""
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print(f"Setting up action plan for ideas: {prioritized_ideas}")
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return f"Action plan created for {prioritized_ideas}"
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def unsupported_task(task_topic):
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"""Handles unsupported tasks."""
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print("Task not supported")
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return "Unsupported task"
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# In[6]:
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todoist_api = TodoistAPI(todoist_api_key)
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# Fetch recent Todoist task
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def fetch_todoist_task():
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try:
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tasks = todoist_api.get_tasks()
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if tasks:
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recent_task = tasks[0] # Fetch the most recent task
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return f"Recent Task: {recent_task.content}"
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return "No tasks found in Todoist."
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except Exception as e:
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return f"Error fetching tasks: {str(e)}"
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def add_to_todoist(task_topic, todoist_priority = 3):
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try:
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# Create a task in Todoist using the Todoist API
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# Assuming you have a function `todoist_api.add_task()` that handles the API request
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todoist_api.add_task(
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content=task_topic,
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priority=todoist_priority
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)
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msg = f"Task added: {task_topic} with priority {todoist_priority}"
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logger.debug(msg)
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return msg
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except Exception as e:
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# Return an error message if something goes wrong
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return f"An error occurred: {e}"
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# def save_todo(reasoning_steps):
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# """
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# Save reasoning steps to Todoist as tasks.
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# Args:
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# reasoning_steps (list of dict): A list of steps with "step" and "priority" keys.
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# """
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# try:
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# # Validate that reasoning_steps is a list
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# if not isinstance(reasoning_steps, list):
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# raise ValueError("The input reasoning_steps must be a list.")
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# # Iterate over the reasoning steps
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# for step in reasoning_steps:
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# # Ensure each step is a dictionary and contains required keys
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# if not isinstance(step, dict) or "step" not in step or "priority" not in step:
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# logger.error(f"Invalid step data: {step}, skipping.")
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# continue
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# task_content = step["step"]
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# priority_level = step["priority"]
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# # Map priority to Todoist's priority levels (1 - low, 4 - high)
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# priority_mapping = {"Low": 1, "Medium": 2, "High": 4}
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# todoist_priority = priority_mapping.get(priority_level, 1) # Default to low if not found
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# # Create a task in Todoist using the Todoist API
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# # Assuming you have a function `todoist_api.add_task()` that handles the API request
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# todoist_api.add_task(
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# content=task_content,
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# priority=todoist_priority
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# )
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# logger.debug(f"Task added: {task_content} with priority {priority_level}")
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# return "All tasks processed."
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# except Exception as e:
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# # Return an error message if something goes wrong
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# return f"An error occurred: {e}"
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# In[7]:
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# evernote_client = EvernoteClient(token=EVERNOTE_API_TOKEN, sandbox=False)
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# note_store = evernote_client.get_note_store()
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# def add_to_evernote(task_topic, notebook_title="Inspirations"):
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# """
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# Add a task topic to the 'Inspirations' notebook in Evernote. If the notebook doesn't exist, create it.
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# Args:
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# task_topic (str): The content of the task to be added.
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# notebook_title (str): The title of the Evernote notebook. Default is 'Inspirations'.
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# """
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# try:
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# # Check if the notebook exists
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# notebooks = note_store.listNotebooks()
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# notebook = next((nb for nb in notebooks if nb.name == notebook_title), None)
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# # If the notebook doesn't exist, create it
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# if not notebook:
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# notebook = Types.Notebook()
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# notebook.name = notebook_title
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# notebook = note_store.createNotebook(notebook)
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# # Search for an existing note with the same title
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# filter = NoteStore.NoteFilter()
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# filter.notebookGuid = notebook.guid
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# filter.words = notebook_title
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# notes_metadata_result = note_store.findNotesMetadata(filter, 0, 1, NoteStore.NotesMetadataResultSpec(includeTitle=True))
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# # If a note with the title exists, append to it; otherwise, create a new note
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# if notes_metadata_result.notes:
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# note_guid = notes_metadata_result.notes[0].guid
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# existing_note = note_store.getNote(note_guid, True, False, False, False)
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# existing_note.content = existing_note.content.replace("</en-note>", f"<div>{task_topic}</div></en-note>")
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# note_store.updateNote(existing_note)
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# else:
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# # Create a new note
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# note = Types.Note()
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# note.title = notebook_title
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# note.notebookGuid = notebook.guid
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# note.content = f'<?xml version="1.0" encoding="UTF-8"?>' \
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# f'<!DOCTYPE en-note SYSTEM "http://xml.evernote.com/pub/enml2.dtd">' \
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# f'<en-note><div>{task_topic}</div></en-note>'
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# note_store.createNote(note)
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# print(f"Task '{task_topic}' successfully added to Evernote under '{notebook_title}'.")
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# except Exception as e:
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# print(f"Error adding task to Evernote: {e}")
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# Mock Functions for Task Actions
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def add_to_evernote(task_topic):
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return f"Task added to Evernote with title '{task_topic}'."
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# In[8]:
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# Access the API keys and other configuration data
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TASK_WORKFLOW_TREE = load_reasoning_json('curify_ideas_reasoning.json')
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action_map = load_action_map('action_map.json')
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# In[9]:
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def generate_task_hash(task_description):
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try:
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# Ensure task_description is a string
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if not isinstance(task_description, str):
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logger.warning("task_description is not a string, attempting conversion.")
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task_description = str(task_description)
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# Safely encode with UTF-8 and ignore errors
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encoded_description = task_description.encode("utf-8", errors="ignore")
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task_hash = hashlib.md5(encoded_description).hexdigest()
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logger.debug(f"Generated task hash: {task_hash}")
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return task_hash
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except Exception as e:
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# Log any unexpected issues
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logger.error(f"Error generating task hash: {e}", exc_info=True)
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return 'output'
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def save_to_google_storage(bucket_name, file_path, destination_blob_name, expiration_minutes = 1440):
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credentials_dict = json.loads(google_credentials_json)
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# Step 3: Use `service_account.Credentials.from_service_account_info` to authenticate directly with the JSON
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credentials = service_account.Credentials.from_service_account_info(credentials_dict)
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gcs_client = storage.Client(credentials=credentials, project=credentials.project_id)
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# Check if the bucket exists; if not, create it
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try:
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bucket = gcs_client.get_bucket(bucket_name)
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except NotFound:
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print(f"❌ Bucket '{bucket_name}' not found. Please check the bucket name.")
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bucket = gcs_client.create_bucket(bucket_name)
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print(f"✅ Bucket '{bucket_name}' created.")
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except Exception as e:
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print(f"❌ An unexpected error occurred: {e}")
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raise
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# Get a reference to the blob
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blob = bucket.blob(destination_blob_name)
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# Upload the file
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blob.upload_from_filename(file_path)
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# Generate a signed URL for the file
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signed_url = blob.generate_signed_url(
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version="v4",
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expiration=timedelta(minutes=expiration_minutes),
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method="GET"
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)
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print(f"✅ File uploaded to Google Cloud Storage. Signed URL: {signed_url}")
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return signed_url
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# Function to check if content is Simplified Chinese
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def is_simplified(text):
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simplified_range = re.compile('[\u4e00-\u9fff]') # Han characters in general
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simplified_characters = [char for char in text if simplified_range.match(char)]
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return len(simplified_characters) > len(text) * 0.5 # Threshold of 50% to be considered simplified
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# Function to choose the appropriate font for the content
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def choose_font_for_content(content):
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return 'NotoSansSC' if is_simplified(content) else 'NotoSansTC'
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# Function to generate and save a document using ReportLab
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def generate_document(task_description, md_content, user_name='jayw', bucket_name='curify'):
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logger.debug("Starting to generate document")
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# Hash the task description to generate a unique filename
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task_hash = generate_task_hash(task_description)
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# Truncate the hash if needed (64 characters is sufficient for uniqueness)
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max_hash_length = 64 # Adjust if needed
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truncated_hash = task_hash[:max_hash_length]
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# Generate PDF file locally
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local_filename = f"{truncated_hash}.pdf" # Use the truncated hash as the local file name
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c = canvas.Canvas(local_filename, pagesize=letter)
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# Paths to the TTF fonts for Simplified and Traditional Chinese
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406 |
-
sc_font_path = 'NotoSansSC-Regular.ttf' # Path to Simplified Chinese font
|
407 |
-
tc_font_path = 'NotoSansTC-Regular.ttf' # Path to Traditional Chinese font
|
408 |
-
|
409 |
-
try:
|
410 |
-
# Register the Simplified Chinese font
|
411 |
-
sc_font = TTFont('NotoSansSC', sc_font_path)
|
412 |
-
pdfmetrics.registerFont(sc_font)
|
413 |
-
|
414 |
-
# Register the Traditional Chinese font
|
415 |
-
tc_font = TTFont('NotoSansTC', tc_font_path)
|
416 |
-
pdfmetrics.registerFont(tc_font)
|
417 |
-
|
418 |
-
# Set default font (Simplified Chinese or Traditional Chinese depending on content)
|
419 |
-
c.setFont('NotoSansSC', 12)
|
420 |
-
except Exception as e:
|
421 |
-
logger.error(f"Error loading font files: {e}")
|
422 |
-
raise RuntimeError("Failed to load one or more fonts. Ensure the font files are accessible.")
|
423 |
-
|
424 |
-
# Set initial Y position for drawing text
|
425 |
-
y_position = 750 # Starting position for text
|
426 |
-
|
427 |
-
# Process dictionary and render content
|
428 |
-
for key, value in md_content.items():
|
429 |
-
# Choose the font based on the key (header)
|
430 |
-
c.setFont(choose_font_for_content(key), 14)
|
431 |
-
c.drawString(100, y_position, f"# {key}")
|
432 |
-
y_position -= 20
|
433 |
-
|
434 |
-
# Choose the font for the value
|
435 |
-
c.setFont(choose_font_for_content(str(value)), 12)
|
436 |
-
|
437 |
-
# Add value
|
438 |
-
if isinstance(value, list): # Handle lists
|
439 |
-
for item in value:
|
440 |
-
c.drawString(100, y_position, f"- {item}")
|
441 |
-
y_position -= 15
|
442 |
-
else: # Handle single strings
|
443 |
-
c.drawString(100, y_position, value)
|
444 |
-
y_position -= 15
|
445 |
-
|
446 |
-
# Check if the page needs to be broken (if Y position is too low)
|
447 |
-
if y_position < 100:
|
448 |
-
c.showPage() # Create a new page
|
449 |
-
c.setFont('NotoSansSC', 12) # Reset font
|
450 |
-
y_position = 750 # Reset the Y position for the new page
|
451 |
-
|
452 |
-
# Save the PDF
|
453 |
-
c.save()
|
454 |
-
|
455 |
-
# Organize files into user-specific folders
|
456 |
-
destination_blob_name = f"{user_name}/{truncated_hash}.pdf"
|
457 |
-
|
458 |
-
# Upload to Google Cloud Storage and get the public URL
|
459 |
-
public_url = save_to_google_storage(bucket_name, local_filename, destination_blob_name)
|
460 |
-
logger.debug("Finished generating document")
|
461 |
-
return public_url
|
462 |
-
|
463 |
-
# In[10]:
|
464 |
-
|
465 |
-
|
466 |
-
def execute_with_retry(sql, params=(), attempts=5, delay=1, db_name = 'curify_ideas.db'):
|
467 |
-
for attempt in range(attempts):
|
468 |
-
try:
|
469 |
-
with sqlite3.connect(db_name) as conn:
|
470 |
-
cursor = conn.cursor()
|
471 |
-
cursor.execute(sql, params)
|
472 |
-
conn.commit()
|
473 |
-
break
|
474 |
-
except sqlite3.OperationalError as e:
|
475 |
-
if "database is locked" in str(e) and attempt < attempts - 1:
|
476 |
-
time.sleep(delay)
|
477 |
-
else:
|
478 |
-
raise e
|
479 |
-
|
480 |
-
# def enable_wal_mode(db_name = 'curify_ideas.db'):
|
481 |
-
# with sqlite3.connect(db_name) as conn:
|
482 |
-
# cursor = conn.cursor()
|
483 |
-
# cursor.execute("PRAGMA journal_mode=WAL;")
|
484 |
-
# conn.commit()
|
485 |
-
|
486 |
-
# # Create SQLite DB and table
|
487 |
-
# def create_db(db_name = 'curify_ideas.db'):
|
488 |
-
# with sqlite3.connect(db_name, timeout=30) as conn:
|
489 |
-
# c = conn.cursor()
|
490 |
-
# c.execute('''CREATE TABLE IF NOT EXISTS sessions (
|
491 |
-
# session_id TEXT,
|
492 |
-
# ip_address TEXT,
|
493 |
-
# project_desc TEXT,
|
494 |
-
# idea_desc TEXT,
|
495 |
-
# idea_analysis TEXT,
|
496 |
-
# prioritization_steps TEXT,
|
497 |
-
# timestamp DATETIME,
|
498 |
-
# PRIMARY KEY (session_id, timestamp)
|
499 |
-
# )
|
500 |
-
# ''')
|
501 |
-
# conn.commit()
|
502 |
-
|
503 |
-
# # Function to insert session data into the SQLite database
|
504 |
-
# def insert_session_data(session_id, ip_address, project_desc, idea_desc, idea_analysis, prioritization_steps, db_name = 'curify_ideas.db'):
|
505 |
-
# execute_with_retry('''
|
506 |
-
# INSERT INTO sessions (session_id, ip_address, project_desc, idea_desc, idea_analysis, prioritization_steps, timestamp)
|
507 |
-
# VALUES (?, ?, ?, ?, ?, ?, ?)
|
508 |
-
# ''', (session_id, ip_address, project_desc, idea_desc, json.dumps(idea_analysis), json.dumps(prioritization_steps), datetime.now()), db_name)
|
509 |
-
|
510 |
-
|
511 |
-
# In[11]:
|
512 |
-
|
513 |
-
|
514 |
-
def convert_to_listed_json(input_string):
|
515 |
-
"""
|
516 |
-
Converts a string to a listed JSON object.
|
517 |
-
|
518 |
-
Parameters:
|
519 |
-
input_string (str): The JSON-like string to be converted.
|
520 |
-
|
521 |
-
Returns:
|
522 |
-
list: A JSON object parsed into a Python list of dictionaries.
|
523 |
-
"""
|
524 |
-
try:
|
525 |
-
# Parse the string into a Python object
|
526 |
-
trimmed_string = input_string[input_string.index('['):input_string.rindex(']') + 1]
|
527 |
-
|
528 |
-
json_object = json.loads(trimmed_string)
|
529 |
-
return json_object
|
530 |
-
except json.JSONDecodeError as e:
|
531 |
-
return None
|
532 |
-
return None
|
533 |
-
#raise ValueError(f"Invalid JSON format: {e}")
|
534 |
-
|
535 |
-
def validate_and_extract_json(json_string):
|
536 |
-
"""
|
537 |
-
Validates the JSON string, extracts fields with possible variants using fuzzy matching.
|
538 |
-
|
539 |
-
Args:
|
540 |
-
- json_string (str): The JSON string to validate and extract from.
|
541 |
-
- field_names (list): List of field names to extract, with possible variants.
|
542 |
-
|
543 |
-
Returns:
|
544 |
-
- dict: Extracted values with the best matched field names.
|
545 |
-
"""
|
546 |
-
# Try to parse the JSON string
|
547 |
-
trimmed_string = json_string[json_string.index('{'):json_string.rindex('}') + 1]
|
548 |
-
try:
|
549 |
-
parsed_json = json.loads(trimmed_string)
|
550 |
-
return parsed_json
|
551 |
-
except json.JSONDecodeError as e:
|
552 |
-
return None
|
553 |
-
|
554 |
-
# {"error": "Parsed JSON is not a dictionary."}
|
555 |
-
return None
|
556 |
-
|
557 |
-
def json_to_pandas(dat_json, dat_schema = {'name':"", 'description':""}):
|
558 |
-
dat_df = pd.DataFrame([dat_schema])
|
559 |
-
try:
|
560 |
-
dat_df = pd.DataFrame(dat_json)
|
561 |
-
|
562 |
-
except Exception as e:
|
563 |
-
dat_df = pd.DataFrame([dat_schema])
|
564 |
-
# ValueError(f"Failed to parse LLM output as JSON: {e}\nOutput: {res}")
|
565 |
-
return dat_df
|
566 |
-
|
567 |
-
|
568 |
-
# In[12]:
|
569 |
-
|
570 |
-
|
571 |
-
client = OpenAI(
|
572 |
-
api_key= os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted
|
573 |
-
)
|
574 |
-
|
575 |
-
# Function to call OpenAI API with compact error handling
|
576 |
-
def call_openai_api(prompt, model="gpt-4o", max_tokens=5000, retries=3, backoff_factor=2):
|
577 |
-
"""
|
578 |
-
Send a prompt to the OpenAI API and handle potential errors robustly.
|
579 |
-
|
580 |
-
Parameters:
|
581 |
-
prompt (str): The user input or task prompt to send to the model.
|
582 |
-
model (str): The OpenAI model to use (default is "gpt-4").
|
583 |
-
max_tokens (int): The maximum number of tokens in the response.
|
584 |
-
retries (int): Number of retry attempts in case of transient errors.
|
585 |
-
backoff_factor (int): Backoff time multiplier for retries.
|
586 |
-
|
587 |
-
Returns:
|
588 |
-
str: The model's response content if successful.
|
589 |
-
"""
|
590 |
-
for attempt in range(1, retries + 1):
|
591 |
-
try:
|
592 |
-
response = client.chat.completions.create(
|
593 |
-
model="gpt-4o",
|
594 |
-
messages=[{"role": "user", "content": prompt}],
|
595 |
-
max_tokens=5000,
|
596 |
-
)
|
597 |
-
return response.choices[0].message.content.strip()
|
598 |
-
|
599 |
-
except (openai.RateLimitError, openai.APIConnectionError) as e:
|
600 |
-
logging.warning(f"Transient error: {e}. Attempt {attempt} of {retries}. Retrying...")
|
601 |
-
except (openai.BadRequestError, openai.AuthenticationError) as e:
|
602 |
-
logging.error(f"Unrecoverable error: {e}. Check your inputs or API key.")
|
603 |
-
break
|
604 |
-
except Exception as e:
|
605 |
-
logging.error(f"Unexpected error: {e}. Attempt {attempt} of {retries}. Retrying...")
|
606 |
-
|
607 |
-
# Exponential backoff before retrying
|
608 |
-
if attempt < retries:
|
609 |
-
time.sleep(backoff_factor * attempt)
|
610 |
-
|
611 |
-
raise RuntimeError(f"Failed to fetch response from OpenAI API after {retries} attempts.")
|
612 |
-
|
613 |
-
def fn_analyze_task(project_context, task_description):
|
614 |
-
prompt = (
|
615 |
-
f"You are working in the context of {project_context}. "
|
616 |
-
f"Your task is to analyze the task: {task_description} "
|
617 |
-
"Please analyze the following aspects: "
|
618 |
-
"1) Determine which project this item belongs to. If the idea does not belong to any existing project, categorize it under 'Other'. "
|
619 |
-
"2) Assess whether this idea can be treated as a concrete task. "
|
620 |
-
"3) Evaluate whether a document can be generated as an intermediate result. "
|
621 |
-
"4) Identify the appropriate category of the task. Possible categories are: 'Blogs/Papers', 'Tools', 'Brainstorming', 'Issues', and 'Others'. "
|
622 |
-
"5) Extract the topic of the task. "
|
623 |
-
"Please provide the output in JSON format using the structure below: "
|
624 |
-
"{"
|
625 |
-
" \"description\": \"\", "
|
626 |
-
" \"project_association\": \"\", "
|
627 |
-
" \"is_task\": \"Yes/No\", "
|
628 |
-
" \"is_document\": \"Yes/No\", "
|
629 |
-
" \"task_category\": \"\", "
|
630 |
-
" \"task_topic\": \"\" "
|
631 |
-
"}"
|
632 |
-
)
|
633 |
-
res_task_analysis = call_openai_api(prompt)
|
634 |
-
|
635 |
-
try:
|
636 |
-
json_task_analysis = validate_and_extract_json(res_task_analysis)
|
637 |
-
|
638 |
-
return json_task_analysis
|
639 |
-
except ValueError as e:
|
640 |
-
logger.debug("ValueError occurred: %s", str(e), exc_info=True) # Log the exception details
|
641 |
-
return None
|
642 |
-
|
643 |
-
|
644 |
-
# In[13]:
|
645 |
-
|
646 |
-
# Recursive Task Executor
|
647 |
-
def fn_process_task(project_desc_table, task_description, bucket_name='curify'):
|
648 |
-
|
649 |
-
project_context = project_desc_table.to_string(index=False)
|
650 |
-
task_analysis = fn_analyze_task(project_context, task_description)
|
651 |
-
|
652 |
-
if task_analysis:
|
653 |
-
execution_status = []
|
654 |
-
execution_results = task_analysis.copy()
|
655 |
-
execution_results['deliverables'] = ''
|
656 |
-
|
657 |
-
def traverse(node, previous_output=None):
|
658 |
-
if not node: # If the node is None or invalid
|
659 |
-
return # Exit if the node is invalid
|
660 |
-
|
661 |
-
# Check if there is a condition to evaluate
|
662 |
-
if "check" in node:
|
663 |
-
# Safely attempt to retrieve the value from execution_results
|
664 |
-
if node["check"] in execution_results:
|
665 |
-
value = execution_results[node["check"]] # Evaluate the check condition
|
666 |
-
traverse(node.get(value, node.get("default")), previous_output)
|
667 |
-
else:
|
668 |
-
# Log an error and exit, but keep partial results
|
669 |
-
logger.error(f"Key '{node['check']}' not found in execution_results.")
|
670 |
-
return
|
671 |
-
|
672 |
-
# If the node contains an action
|
673 |
-
elif "action" in node:
|
674 |
-
action_name = node["action"]
|
675 |
-
input_key = node.get("input", 'task_topic')
|
676 |
-
|
677 |
-
if input_key in execution_results.keys():
|
678 |
-
inputs = {input_key: execution_results[input_key]}
|
679 |
-
else:
|
680 |
-
# Log an error and exit, but keep partial results
|
681 |
-
logger.error(f"Workflow action {action_name} input key {input_key} not in execution_results.")
|
682 |
-
return
|
683 |
-
|
684 |
-
logger.debug(f"Executing: {action_name} with inputs: {inputs}")
|
685 |
-
|
686 |
-
# Execute the action function
|
687 |
-
action_func = action_map.get(action_name, unsupported_task)
|
688 |
-
try:
|
689 |
-
output = action_func(**inputs)
|
690 |
-
except Exception as e:
|
691 |
-
# Handle action function failure
|
692 |
-
logger.error(f"Error executing action '{action_name}': {e}")
|
693 |
-
return
|
694 |
-
|
695 |
-
# Store execution results or append to previous outputs
|
696 |
-
execution_status.append({"action": action_name, "output": output})
|
697 |
-
|
698 |
-
# Check if 'output' field exists in the node
|
699 |
-
if 'output' in node:
|
700 |
-
# If 'output' exists, assign the output to execution_results with the key from node['output']
|
701 |
-
execution_results[node['output']] = output
|
702 |
-
else:
|
703 |
-
# If 'output' does not exist, append the output to 'deliverables'
|
704 |
-
execution_results['deliverables'] += output
|
705 |
-
|
706 |
-
# Traverse to the next node, if it exists
|
707 |
-
if "next" in node and node["next"]:
|
708 |
-
traverse(node["next"], previous_output)
|
709 |
-
|
710 |
-
try:
|
711 |
-
traverse(TASK_WORKFLOW_TREE["start"])
|
712 |
-
execution_results['doc_url'] = generate_document(task_description, execution_results)
|
713 |
-
except Exception as e:
|
714 |
-
logger.error(f"Traverse Error: {e}")
|
715 |
-
finally:
|
716 |
-
# Always return partial results, even if an error occurs
|
717 |
-
return task_analysis, pd.DataFrame(execution_status), execution_results
|
718 |
-
else:
|
719 |
-
logger.error("Empty task analysis.")
|
720 |
-
return {}, pd.DataFrame(), {}
|
721 |
-
|
722 |
-
# In[14]:
|
723 |
-
|
724 |
-
|
725 |
-
# Initialize dataframes for the schema
|
726 |
-
ideas_df = pd.DataFrame(columns=["Idea ID", "Content", "Tags"])
|
727 |
-
|
728 |
-
def extract_ideas(context, text):
|
729 |
-
"""
|
730 |
-
Extract project ideas from text, with or without a context, and return in JSON format.
|
731 |
-
|
732 |
-
Parameters:
|
733 |
-
context (str): Context of the extraction. Can be empty.
|
734 |
-
text (str): Text to extract ideas from.
|
735 |
-
|
736 |
-
Returns:
|
737 |
-
list: A list of ideas, each represented as a dictionary with name and description.
|
738 |
-
"""
|
739 |
-
if context:
|
740 |
-
# Template when context is provided
|
741 |
-
prompt = (
|
742 |
-
f"You are working in the context of {context}. "
|
743 |
-
"Please extract the ongoing projects with project name and description."
|
744 |
-
"Please only the listed JSON as output string."
|
745 |
-
f"Ongoing projects: {text}"
|
746 |
-
)
|
747 |
-
else:
|
748 |
-
# Template when context is not provided
|
749 |
-
prompt = (
|
750 |
-
"Given the following information about the user."
|
751 |
-
"Please extract the ongoing projects with project name and description."
|
752 |
-
"Please only the listed JSON as output string."
|
753 |
-
f"Ongoing projects: {text}"
|
754 |
-
)
|
755 |
-
|
756 |
-
# return the raw string
|
757 |
-
return call_openai_api(prompt)
|
758 |
-
|
759 |
-
def df_to_string(df, empty_message = ''):
|
760 |
-
"""
|
761 |
-
Converts a DataFrame to a string if it is not empty.
|
762 |
-
If the DataFrame is empty, returns an empty string.
|
763 |
-
|
764 |
-
Parameters:
|
765 |
-
ideas_df (pd.DataFrame): The DataFrame to be converted.
|
766 |
-
|
767 |
-
Returns:
|
768 |
-
str: A string representation of the DataFrame or an empty string.
|
769 |
-
"""
|
770 |
-
if df.empty:
|
771 |
-
return empty_message
|
772 |
-
else:
|
773 |
-
return df.to_string(index=False)
|
774 |
-
|
775 |
-
|
776 |
-
# In[15]:
|
777 |
-
|
778 |
-
|
779 |
-
# Shared state variables
|
780 |
-
shared_state = {"project_desc_table": pd.DataFrame(), "task_analysis_txt": "", "execution_status": pd.DataFrame(), "execution_results": {}}
|
781 |
-
|
782 |
-
# Button Action: Fetch State
|
783 |
-
def fetch_updated_state():
|
784 |
-
response = requests.get("http://localhost:5000/state")
|
785 |
-
state = response.json()
|
786 |
-
"""Fetch the updated shared state from FastAPI."""
|
787 |
-
return pd.DataFrame(state["project_desc_table"]), state["task_analysis_txt"], pd.DataFrame(state["execution_status"]), state["execution_results"]
|
788 |
-
|
789 |
-
def update_gradio_state(project_desc_table, task_analysis_txt, execution_status, execution_results):
|
790 |
-
# You can update specific components like Textbox or State
|
791 |
-
shared_state['project_desc_table'] = project_desc_table
|
792 |
-
shared_state['task_analysis_txt'] = task_analysis_txt
|
793 |
-
shared_state['execution_status'] = execution_status
|
794 |
-
shared_state['execution_results'] = execution_results
|
795 |
-
return True
|
796 |
-
|
797 |
-
|
798 |
-
# In[16]:
|
799 |
-
|
800 |
-
|
801 |
-
# # Initialize the database
|
802 |
-
# new_db = 'curify.db'
|
803 |
-
|
804 |
-
# # Copy the old database to a new one
|
805 |
-
# shutil.copy("curify_idea.db", new_db)
|
806 |
-
|
807 |
-
#create_db(new_db)
|
808 |
-
#enable_wal_mode(new_db)
|
809 |
-
def project_extraction(project_description):
|
810 |
-
|
811 |
-
str_projects = extract_ideas('AI-powered tools for productivity', project_description)
|
812 |
-
json_projects = convert_to_listed_json(str_projects)
|
813 |
-
|
814 |
-
project_desc_table = json_to_pandas(json_projects)
|
815 |
-
update_gradio_state(project_desc_table, "", pd.DataFrame(), {})
|
816 |
-
return project_desc_table
|
817 |
-
|
818 |
-
|
819 |
-
# In[17]:
|
820 |
-
|
821 |
-
|
822 |
-
# project_description = 'work on a number of projects including curify (digest, ideas, careers, projects etc), and writing a book on LLM for recommendation system, educating my 3.5-year-old boy and working on a paper for LLM reasoning.'
|
823 |
-
|
824 |
-
# # convert_to_listed_json(extract_ideas('AI-powered tools for productivity', project_description))
|
825 |
-
|
826 |
-
# task_description = 'Build an interview bot for the curify digest project.'
|
827 |
-
# task_analysis, reasoning_path = generate_reasoning_path(project_description, task_description)
|
828 |
-
|
829 |
-
# steps = store_and_execute_task(task_description, reasoning_path)
|
830 |
-
|
831 |
-
def message_back(task_message, execution_status, doc_url, from_whatsapp):
|
832 |
-
# Convert task steps to a simple numbered list
|
833 |
-
task_steps_list = "\n".join(
|
834 |
-
[f"{i + 1}. {step['action']} - {step.get('output', '')}" for i, step in enumerate(execution_status.to_dict(orient="records"))]
|
835 |
-
)
|
836 |
-
|
837 |
-
# Format the body message
|
838 |
-
body_message = (
|
839 |
-
f"*Task Message:*\n{task_message}\n\n"
|
840 |
-
f"*Execution Status:*\n{task_steps_list}\n\n"
|
841 |
-
f"*Doc URL:*\n{doc_url}\n\n"
|
842 |
-
)
|
843 |
-
|
844 |
-
# Send response back to WhatsApp
|
845 |
-
try:
|
846 |
-
twillo_client.messages.create(
|
847 |
-
from_=twilio_phone_number,
|
848 |
-
to=from_whatsapp,
|
849 |
-
body=body_message
|
850 |
-
)
|
851 |
-
except Exception as e:
|
852 |
-
logger.error(f"Twilio Error: {e}")
|
853 |
-
raise HTTPException(status_code=500, detail=f"Error sending WhatsApp message: {str(e)}")
|
854 |
-
|
855 |
-
return {"status": "success"}
|
856 |
-
|
857 |
-
# Initialize the Whisper pipeline
|
858 |
-
whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
|
859 |
-
|
860 |
-
# Function to transcribe audio from a media URL
|
861 |
-
def transcribe_audio_from_media_url(media_url):
|
862 |
-
try:
|
863 |
-
media_response = requests.get(media_url, auth=HTTPBasicAuth(account_sid, auth_token))
|
864 |
-
# Download the media file
|
865 |
-
media_response.raise_for_status()
|
866 |
-
audio_data = media_response.content
|
867 |
-
|
868 |
-
# Save the audio data to a file for processing
|
869 |
-
audio_file_path = "temp_audio_file.mp3"
|
870 |
-
with open(audio_file_path, "wb") as audio_file:
|
871 |
-
audio_file.write(audio_data)
|
872 |
-
|
873 |
-
# Transcribe the audio using Whisper
|
874 |
-
transcription = whisper_pipeline(audio_file_path, return_timestamps=True)
|
875 |
-
logger.debug(f"Transcription: {transcription['text']}")
|
876 |
-
return transcription["text"]
|
877 |
-
|
878 |
-
except Exception as e:
|
879 |
-
logger.error(f"An error occurred: {e}")
|
880 |
-
return None
|
881 |
-
|
882 |
-
|
883 |
-
# In[18]:
|
884 |
-
|
885 |
|
|
|
886 |
app = FastAPI()
|
887 |
|
888 |
-
|
889 |
-
|
890 |
-
return
|
891 |
-
|
892 |
-
@app.route("/whatsapp-webhook/", methods=["POST"])
|
893 |
-
async def whatsapp_webhook(request: Request):
|
894 |
-
form_data = await request.form()
|
895 |
-
# Log the form data to debug
|
896 |
-
print("Received data:", form_data)
|
897 |
-
|
898 |
-
# Extract message and user information
|
899 |
-
incoming_msg = form_data.get("Body", "").strip()
|
900 |
-
from_number = form_data.get("From", "")
|
901 |
-
media_url = form_data.get("MediaUrl0", "")
|
902 |
-
media_type = form_data.get("MediaContentType0", "")
|
903 |
-
|
904 |
-
# Initialize response variables
|
905 |
-
transcription = None
|
906 |
-
|
907 |
-
if media_type.startswith("audio"):
|
908 |
-
# If the media is an audio or video file, process it
|
909 |
-
try:
|
910 |
-
transcription = transcribe_audio_from_media_url(media_url)
|
911 |
-
except Exception as e:
|
912 |
-
return JSONResponse(
|
913 |
-
{"error": f"Failed to process voice input: {str(e)}"}, status_code=500
|
914 |
-
)
|
915 |
-
# Determine message content: use transcription if available, otherwise use text message
|
916 |
-
processed_input = transcription if transcription else incoming_msg
|
917 |
-
|
918 |
-
logger.debug(f"Processed input: {processed_input}")
|
919 |
-
try:
|
920 |
-
# Generate response
|
921 |
-
project_desc_table, _ = fetch_updated_state()
|
922 |
-
if not project_desc_table.empty:
|
923 |
-
task_analysis_txt, execution_status, execution_results = fn_process_task(project_desc_table, processed_input)
|
924 |
-
update_gradio_state(task_analysis_txt, execution_status, execution_results)
|
925 |
-
|
926 |
-
doc_url = 'Fail to generate doc'
|
927 |
-
if 'doc_url' in execution_results:
|
928 |
-
doc_url = execution_results['doc_url']
|
929 |
-
|
930 |
-
# Respond to the user on WhatsApp with the processed idea
|
931 |
-
response = message_back(processed_input, execution_status, doc_url, from_number)
|
932 |
-
logger.debug(response)
|
933 |
-
|
934 |
-
return JSONResponse(content=str(response))
|
935 |
-
except Exception as e:
|
936 |
-
logger.error(f"Error during task processing: {e}")
|
937 |
-
return {"error": str(e)}
|
938 |
-
|
939 |
-
|
940 |
-
# In[19]:
|
941 |
-
|
942 |
-
|
943 |
-
# Mock Gmail Login Function
|
944 |
-
def mock_login(email):
|
945 |
-
if email.endswith("@gmail.com"):
|
946 |
-
return f"✅ Logged in as {email}", gr.update(visible=False), gr.update(visible=True)
|
947 |
-
else:
|
948 |
-
return "❌ Invalid Gmail address. Please try again.", gr.update(), gr.update()
|
949 |
-
|
950 |
-
# User Onboarding Function
|
951 |
-
def onboarding_survey(role, industry, project_description):
|
952 |
-
return (project_extraction(project_description),
|
953 |
-
gr.update(visible=False), gr.update(visible=True))
|
954 |
-
|
955 |
-
# Mock Integration Functions
|
956 |
-
def integrate_todoist():
|
957 |
-
return "✅ Successfully connected to Todoist!"
|
958 |
-
|
959 |
-
def integrate_evernote():
|
960 |
-
return "✅ Successfully connected to Evernote!"
|
961 |
|
962 |
-
|
963 |
-
|
964 |
-
|
965 |
-
|
966 |
-
# Read the SVG content from the file
|
967 |
-
with open(file_path, "r", encoding="utf-8") as file:
|
968 |
-
svg_content = file.read()
|
969 |
|
970 |
-
|
971 |
-
styled_svg = f"""
|
972 |
-
<div style="width: {width}; height: {height}; overflow: auto;">
|
973 |
-
{svg_content}
|
974 |
-
</div>
|
975 |
-
"""
|
976 |
-
return styled_svg
|
977 |
-
|
978 |
-
|
979 |
-
# In[20]:
|
980 |
-
|
981 |
-
|
982 |
-
# Gradio Demo
|
983 |
-
def create_gradio_interface(state=None):
|
984 |
-
with gr.Blocks(
|
985 |
-
css="""
|
986 |
-
.gradio-table td {
|
987 |
-
white-space: normal !important;
|
988 |
-
word-wrap: break-word !important;
|
989 |
-
}
|
990 |
-
.gradio-table {
|
991 |
-
width: 100% !important; /* Adjust to 100% to fit the container */
|
992 |
-
table-layout: fixed !important; /* Fixed column widths */
|
993 |
-
overflow-x: hidden !important; /* Disable horizontal scrolling */
|
994 |
-
}
|
995 |
-
.gradio-container {
|
996 |
-
overflow-x: hidden !important; /* Disable horizontal scroll for entire container */
|
997 |
-
padding: 0 !important; /* Remove any default padding */
|
998 |
-
}
|
999 |
-
.gradio-column {
|
1000 |
-
max-width: 100% !important; /* Ensure columns take up full width */
|
1001 |
-
overflow: hidden !important; /* Hide overflow to prevent horizontal scroll */
|
1002 |
-
}
|
1003 |
-
.gradio-row {
|
1004 |
-
overflow-x: hidden !important; /* Prevent horizontal scroll on rows */
|
1005 |
-
}
|
1006 |
-
""") as demo:
|
1007 |
-
|
1008 |
-
# Page 1: Mock Gmail Login
|
1009 |
-
with gr.Group(visible=True) as login_page:
|
1010 |
-
gr.Markdown("### **1️⃣ Login with Gmail**")
|
1011 |
-
email_input = gr.Textbox(label="Enter your Gmail Address", placeholder="[email protected]")
|
1012 |
-
login_button = gr.Button("Login")
|
1013 |
-
login_result = gr.Textbox(label="Login Status", interactive=False, visible=False)
|
1014 |
-
# Page 2: User Onboarding
|
1015 |
-
with gr.Group(visible=False) as onboarding_page:
|
1016 |
-
gr.Markdown("### **2️⃣ Tell Us About Yourself**")
|
1017 |
-
role = gr.Textbox(label="What is your role?", placeholder="e.g. Developer, Designer")
|
1018 |
-
industry = gr.Textbox(label="Which industry are you in?", placeholder="e.g. Software, Finance")
|
1019 |
-
project_description = gr.Textbox(label="Describe your project", placeholder="e.g. A task management app")
|
1020 |
-
submit_survey = gr.Button("Submit")
|
1021 |
-
|
1022 |
-
# Page 3: Mock Integrations with Separate Buttons
|
1023 |
-
with gr.Group(visible=False) as integrations_page:
|
1024 |
-
gr.Markdown("### **3️⃣ Connect Integrations**")
|
1025 |
-
gr.Markdown("Click on the buttons below to connect each tool:")
|
1026 |
-
|
1027 |
-
# Separate Buttons and Results for Each Integration
|
1028 |
-
todoist_button = gr.Button("Connect to Todoist")
|
1029 |
-
todoist_result = gr.Textbox(label="Todoist Status", interactive=False, visible=False)
|
1030 |
-
|
1031 |
-
evernote_button = gr.Button("Connect to Evernote")
|
1032 |
-
evernote_result = gr.Textbox(label="Evernote Status", interactive=False, visible=False)
|
1033 |
-
|
1034 |
-
calendar_button = gr.Button("Connect to Google Calendar")
|
1035 |
-
calendar_result = gr.Textbox(label="Google Calendar Status", interactive=False, visible=False)
|
1036 |
-
|
1037 |
-
# Skip Button to proceed directly to next page
|
1038 |
-
skip_integrations = gr.Button("Skip ➡️")
|
1039 |
-
next_button = gr.Button("Proceed to QR Code")
|
1040 |
-
|
1041 |
-
with gr.Group(visible=False) as qr_code_page:
|
1042 |
-
# Page 4: QR Code and Curify Ideas
|
1043 |
-
gr.Markdown("## Curify: Unified AI Tools for Productivity")
|
1044 |
-
|
1045 |
-
with gr.Tab("Curify Idea"):
|
1046 |
-
with gr.Row():
|
1047 |
-
with gr.Column():
|
1048 |
-
gr.Markdown("#### ** QR Code**")
|
1049 |
-
# Path to your local SVG file
|
1050 |
-
svg_file_path = "qr.svg"
|
1051 |
-
# Load the SVG content
|
1052 |
-
svg_content = load_svg_with_size(svg_file_path, width="200px", height="200px")
|
1053 |
-
gr.HTML(svg_content)
|
1054 |
-
|
1055 |
-
# Column 1: Webpage rendering
|
1056 |
-
with gr.Column():
|
1057 |
-
|
1058 |
-
gr.Markdown("## Projects Overview")
|
1059 |
-
project_desc_table = gr.DataFrame(
|
1060 |
-
type="pandas"
|
1061 |
-
)
|
1062 |
-
|
1063 |
-
gr.Markdown("## Enter task message.")
|
1064 |
-
idea_input = gr.Textbox(
|
1065 |
-
label=None,
|
1066 |
-
placeholder="Describe the task you want to execute (e.g., Research Paper Review)")
|
1067 |
-
|
1068 |
-
task_btn = gr.Button("Generate Task Steps")
|
1069 |
-
fetch_state_btn = gr.Button("Fetch Updated State")
|
1070 |
-
|
1071 |
-
with gr.Column():
|
1072 |
-
gr.Markdown("## Task analysis")
|
1073 |
-
task_analysis_txt = gr.Textbox(
|
1074 |
-
label=None,
|
1075 |
-
placeholder="Here is the execution status of your task...")
|
1076 |
-
|
1077 |
-
gr.Markdown("## Execution status")
|
1078 |
-
execution_status = gr.DataFrame(
|
1079 |
-
type="pandas"
|
1080 |
-
)
|
1081 |
-
gr.Markdown("## Execution output")
|
1082 |
-
execution_results = gr.JSON(
|
1083 |
-
label=None
|
1084 |
-
)
|
1085 |
-
state_output = gr.State() # Add a state output to hold the state
|
1086 |
-
|
1087 |
-
task_btn.click(
|
1088 |
-
fn_process_task,
|
1089 |
-
inputs=[project_desc_table, idea_input],
|
1090 |
-
outputs=[task_analysis_txt, execution_status, execution_results]
|
1091 |
-
)
|
1092 |
|
1093 |
-
|
1094 |
-
|
1095 |
-
inputs=None,
|
1096 |
-
outputs=[project_desc_table, task_analysis_txt, execution_status, execution_results]
|
1097 |
-
)
|
1098 |
|
1099 |
-
|
1100 |
-
|
1101 |
-
mock_login,
|
1102 |
-
inputs=email_input,
|
1103 |
-
outputs=[login_result, login_page, onboarding_page]
|
1104 |
-
)
|
1105 |
|
1106 |
-
|
1107 |
-
submit_survey.click(
|
1108 |
-
onboarding_survey,
|
1109 |
-
inputs=[role, industry, project_description],
|
1110 |
-
outputs=[project_desc_table, onboarding_page, integrations_page]
|
1111 |
-
)
|
1112 |
-
|
1113 |
-
# Integration Buttons
|
1114 |
-
todoist_button.click(integrate_todoist, outputs=todoist_result)
|
1115 |
-
evernote_button.click(integrate_evernote, outputs=evernote_result)
|
1116 |
-
calendar_button.click(integrate_calendar, outputs=calendar_result)
|
1117 |
-
|
1118 |
-
# Skip Integrations and Proceed
|
1119 |
-
skip_integrations.click(
|
1120 |
-
lambda: (gr.update(visible=False), gr.update(visible=True)),
|
1121 |
-
outputs=[integrations_page, qr_code_page]
|
1122 |
-
)
|
1123 |
-
|
1124 |
-
# # Set the load_fn to initialize the state when the page is loaded
|
1125 |
-
# demo.load(
|
1126 |
-
# curify_ideas,
|
1127 |
-
# inputs=[project_input, idea_input],
|
1128 |
-
# outputs=[task_steps, task_analysis_txt, state_output]
|
1129 |
-
# )
|
1130 |
-
return demo
|
1131 |
-
# Load function to initialize the state
|
1132 |
-
# demo.load(load_fn, inputs=None, outputs=[state]) # Initialize the state when the page is loaded
|
1133 |
-
|
1134 |
-
# In[21]:
|
1135 |
-
demo = create_gradio_interface()
|
1136 |
-
# Use Gradio's `server_app` to get an ASGI app for Blocks
|
1137 |
-
gradio_asgi_app = gr.routes.App.create_app(demo)
|
1138 |
-
|
1139 |
-
logging.debug(f"Gradio version: {gr.__version__}")
|
1140 |
-
logging.debug(f"FastAPI version: {fastapi.__version__}")
|
1141 |
-
|
1142 |
-
# Mount the Gradio ASGI app at "/gradio"
|
1143 |
-
app.mount("/", gradio_asgi_app)
|
1144 |
-
|
1145 |
-
# create a static directory to store the static files
|
1146 |
static_dir = Path('./static')
|
1147 |
static_dir.mkdir(parents=True, exist_ok=True)
|
1148 |
-
|
1149 |
-
# mount FastAPI StaticFiles server
|
1150 |
app.mount("/static", StaticFiles(directory=static_dir), name="static")
|
1151 |
|
1152 |
-
#
|
1153 |
-
|
1154 |
-
|
1155 |
-
# if os.path.exists(gradio_assets_path):
|
1156 |
-
# # If assets exist, mount them
|
1157 |
-
# app.mount("/assets", StaticFiles(directory=gradio_assets_path), name="assets")
|
1158 |
-
# else:
|
1159 |
-
# logging.error(f"Gradio assets directory not found at: {gradio_assets_path}")
|
1160 |
-
|
1161 |
-
# # Redirect from the root endpoint to the Gradio app
|
1162 |
-
# @app.get("/", response_class=RedirectResponse)
|
1163 |
-
# async def index():
|
1164 |
-
# return {"message": "FastAPI is running. Visit /gradio for the Gradio interface."}
|
1165 |
-
|
1166 |
-
# return RedirectResponse(url="/gradio", status_code=307)
|
1167 |
|
1168 |
# Run the FastAPI server using uvicorn
|
1169 |
if __name__ == "__main__":
|
1170 |
port = int(os.getenv("PORT", 7860)) # Default to 7860 if PORT is not set
|
1171 |
-
uvicorn.run(app, host="0.0.0.0", port=port)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
1 |
import gradio as gr
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2 |
import fastapi
|
3 |
+
from fastapi import FastAPI
|
|
|
4 |
from fastapi.staticfiles import StaticFiles
|
5 |
+
import uvicorn
|
6 |
+
import os
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
7 |
import logging
|
8 |
+
from pathlib import Path
|
9 |
|
10 |
+
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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11 |
|
12 |
+
# Create FastAPI app
|
13 |
app = FastAPI()
|
14 |
|
15 |
+
# Function for Gradio button
|
16 |
+
def on_button_click():
|
17 |
+
return "Button clicked!"
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18 |
|
19 |
+
# Create Gradio Blocks app
|
20 |
+
with gr.Blocks() as demo:
|
21 |
+
button = gr.Button("Click Me")
|
22 |
+
output = gr.Textbox()
|
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|
23 |
|
24 |
+
button.click(on_button_click, inputs=[], outputs=[output])
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25 |
|
26 |
+
# Create ASGI app for Gradio
|
27 |
+
gradio_asgi_app = demo.launch(share=False, inbrowser=False, server_name="0.0.0.0", server_port=7860, inline=False)
|
|
|
|
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|
28 |
|
29 |
+
# Mount the Gradio ASGI app onto FastAPI at "/gradio"
|
30 |
+
app.mount("/gradio", gradio_asgi_app)
|
|
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|
31 |
|
32 |
+
# Static files directory for FastAPI
|
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|
33 |
static_dir = Path('./static')
|
34 |
static_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
35 |
app.mount("/static", StaticFiles(directory=static_dir), name="static")
|
36 |
|
37 |
+
# Check Gradio and FastAPI versions
|
38 |
+
logging.debug(f"Gradio version: {gr.__version__}")
|
39 |
+
logging.debug(f"FastAPI version: {fastapi.__version__}")
|
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|
40 |
|
41 |
# Run the FastAPI server using uvicorn
|
42 |
if __name__ == "__main__":
|
43 |
port = int(os.getenv("PORT", 7860)) # Default to 7860 if PORT is not set
|
44 |
+
uvicorn.run(app, host="0.0.0.0", port=port)
|