Update app.py

app.py

@@ -1,1162 +1,7 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-
-# In[2]:
-
-
-#pip install  evernote-sdk-python3
-# import evernote.edam.notestore.NoteStore as NoteStore
-# import evernote.edam.type.ttypes as Types
-# from evernote.api.client import EvernoteClient
-
-
-# In[3]:
-
-
-import os
-import yaml
-import pandas as pd
-import numpy as np
-
-from datetime import datetime, timedelta
-
-# perspective generation
-import openai
-import os
-from openai import OpenAI
-
-import gradio as gr
-
-import json
-
-import sqlite3
-import uuid
-import socket
-import difflib
-import time
-import shutil
-import requests
-import re
-
-import json
-import markdown
-from fpdf import FPDF
-import hashlib
-
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-from todoist_api_python.api import TodoistAPI
-
-# from flask import Flask, request, jsonify
-from twilio.rest import Client
-
-import asyncio
-import uvicorn
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse
-
-import nest_asyncio
-from twilio.twiml.messaging_response import MessagingResponse
-
-from requests.auth import HTTPBasicAuth
-
-from google.cloud import storage, exceptions  # Import exceptions for error handling
-from google.cloud.exceptions import NotFound
-from google.oauth2 import service_account
-
-from reportlab.pdfgen import canvas
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfbase import pdfmetrics
-from reportlab.lib import colors
-from reportlab.pdfbase.ttfonts import TTFont
-
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger(__name__)
-
-
-# In[4]:
-
-# Access the API keys and other configuration data
-openai_api_key = os.environ["OPENAI_API_KEY"]
-# Access the API keys and other configuration data
-todoist_api_key = os.environ["TODOIST_API_KEY"]
-
-EVERNOTE_API_TOKEN = os.environ["EVERNOTE_API_TOKEN"]
-
-account_sid = os.environ["TWILLO_ACCOUNT_SID"]
-auth_token = os.environ["TWILLO_AUTH_TOKEN"]
-twilio_phone_number = os.environ["TWILLO_PHONE_NUMBER"]
-
-google_credentials_json = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
-twillo_client = Client(account_sid, auth_token)
-
-# Set the GOOGLE_APPLICATION_CREDENTIALS environment variable
-
-# Load Reasoning Graph JSON File
-def load_reasoning_json(filepath):
-    """Load JSON file and return the dictionary."""
-    with open(filepath, "r") as file:
-        data = json.load(file)
-    return data
-
-# Load Action Map
-def load_action_map(filepath):
-    """Load action map JSON file and map strings to actual function objects."""
-    with open(filepath, "r") as file:
-        action_map_raw = json.load(file)
-    # Map string names to actual functions using globals()
-    return {action: globals()[func_name] for action, func_name in action_map_raw.items()}
-
-
-# In[5]:
-
-
-# Define all actions as functions
-
-def find_reference(task_topic):
-    """Finds a reference related to the task topic."""
-    print(f"Finding reference for topic: {task_topic}")
-    return f"Reference found for topic: {task_topic}"
-
-def generate_summary(reference):
-    """Generates a summary of the reference."""
-    print(f"Generating summary for reference: {reference}")
-    return f"Summary of {reference}"
-
-def suggest_relevance(summary):
-    """Suggests how the summary relates to the project."""
-    print(f"Suggesting relevance of summary: {summary}")
-    return f"Relevance of {summary} suggested"
-
-def tool_research(task_topic):
-    """Performs tool research and returns analysis."""
-    print("Performing tool research")
-    return "Tool analysis data"
-
-def generate_comparison_table(tool_analysis):
-    """Generates a comparison table for a competitive tool."""
-    print(f"Generating comparison table for analysis: {tool_analysis}")
-    return f"Comparison table for {tool_analysis}"
-
-def generate_integration_memo(tool_analysis):
-    """Generates an integration memo for a tool."""
-    print(f"Generating integration memo for analysis: {tool_analysis}")
-    return f"Integration memo for {tool_analysis}"
-
-def analyze_issue(task_topic):
-    """Analyzes an issue and returns the analysis."""
-    print("Analyzing issue")
-    return "Issue analysis data"
-
-def generate_issue_memo(issue_analysis):
-    """Generates an issue memo based on the analysis."""
-    print(f"Generating issue memo for analysis: {issue_analysis}")
-    return f"Issue memo for {issue_analysis}"
-
-def list_ideas(task_topic):
-    """Lists potential ideas for brainstorming."""
-    print("Listing ideas")
-    return ["Idea 1", "Idea 2", "Idea 3"]
-
-def construct_matrix(ideas):
-    """Constructs a matrix (e.g., feasibility or impact/effort) for the ideas."""
-    print(f"Constructing matrix for ideas: {ideas}")
-    return {"Idea 1": "High Impact/Low Effort", "Idea 2": "Low Impact/High Effort", "Idea 3": "High Impact/High Effort"}
-
-def prioritize_ideas(matrix):
-    """Prioritizes ideas based on the matrix."""
-    print(f"Prioritizing ideas based on matrix: {matrix}")
-    return ["Idea 3", "Idea 1", "Idea 2"]
-
-def setup_action_plan(prioritized_ideas):
-    """Sets up an action plan based on the prioritized ideas."""
-    print(f"Setting up action plan for ideas: {prioritized_ideas}")
-    return f"Action plan created for {prioritized_ideas}"
-
-def unsupported_task(task_topic):
-    """Handles unsupported tasks."""
-    print("Task not supported")
-    return "Unsupported task"
-
-
-# In[6]:
-
-
-todoist_api = TodoistAPI(todoist_api_key)
-
-# Fetch recent Todoist task
-def fetch_todoist_task():
-    try:
-        tasks = todoist_api.get_tasks()
-        if tasks:
-            recent_task = tasks[0]  # Fetch the most recent task
-            return f"Recent Task: {recent_task.content}"
-        return "No tasks found in Todoist."
-    except Exception as e:
-        return f"Error fetching tasks: {str(e)}"
-
-def add_to_todoist(task_topic, todoist_priority = 3):
-    try:
-        # Create a task in Todoist using the Todoist API
-        # Assuming you have a function `todoist_api.add_task()` that handles the API request
-        todoist_api.add_task(
-            content=task_topic,
-            priority=todoist_priority
-        )
-        msg = f"Task added: {task_topic} with priority {todoist_priority}"
-        logger.debug(msg)
-
-        return msg
-    except Exception as e:
-        # Return an error message if something goes wrong
-        return f"An error occurred: {e}"    
-
-# def save_todo(reasoning_steps):
-#     """
-#     Save reasoning steps to Todoist as tasks.
-
-#     Args:
-#         reasoning_steps (list of dict): A list of steps with "step" and "priority" keys.
-#     """
-#     try:
-#         # Validate that reasoning_steps is a list
-#         if not isinstance(reasoning_steps, list):
-#             raise ValueError("The input reasoning_steps must be a list.")
-
-#         # Iterate over the reasoning steps
-#         for step in reasoning_steps:
-#             # Ensure each step is a dictionary and contains required keys
-#             if not isinstance(step, dict) or "step" not in step or "priority" not in step:
-#                 logger.error(f"Invalid step data: {step}, skipping.")
-#                 continue
-
-#             task_content = step["step"]
-#             priority_level = step["priority"]
-
-#             # Map priority to Todoist's priority levels (1 - low, 4 - high)
-#             priority_mapping = {"Low": 1, "Medium": 2, "High": 4}
-#             todoist_priority = priority_mapping.get(priority_level, 1)  # Default to low if not found
-
-#             # Create a task in Todoist using the Todoist API
-#             # Assuming you have a function `todoist_api.add_task()` that handles the API request
-#             todoist_api.add_task(
-#                 content=task_content,
-#                 priority=todoist_priority
-#             )
-
-#             logger.debug(f"Task added: {task_content} with priority {priority_level}")
-
-#         return "All tasks processed."
-#     except Exception as e:
-#         # Return an error message if something goes wrong
-#         return f"An error occurred: {e}"
-
-
-# In[7]:
-
-
-# evernote_client = EvernoteClient(token=EVERNOTE_API_TOKEN, sandbox=False)
-# note_store = evernote_client.get_note_store()
-
-# def add_to_evernote(task_topic, notebook_title="Inspirations"):
-#     """
-#     Add a task topic to the 'Inspirations' notebook in Evernote. If the notebook doesn't exist, create it.
-
-#     Args:
-#         task_topic (str): The content of the task to be added.
-#         notebook_title (str): The title of the Evernote notebook. Default is 'Inspirations'.
-#     """
-#     try:
-#         # Check if the notebook exists
-#         notebooks = note_store.listNotebooks()
-#         notebook = next((nb for nb in notebooks if nb.name == notebook_title), None)
-
-#         # If the notebook doesn't exist, create it
-#         if not notebook:
-#             notebook = Types.Notebook()
-#             notebook.name = notebook_title
-#             notebook = note_store.createNotebook(notebook)
-
-#         # Search for an existing note with the same title
-#         filter = NoteStore.NoteFilter()
-#         filter.notebookGuid = notebook.guid
-#         filter.words = notebook_title
-#         notes_metadata_result = note_store.findNotesMetadata(filter, 0, 1, NoteStore.NotesMetadataResultSpec(includeTitle=True))
-
-#         # If a note with the title exists, append to it; otherwise, create a new note
-#         if notes_metadata_result.notes:
-#             note_guid = notes_metadata_result.notes[0].guid
-#             existing_note = note_store.getNote(note_guid, True, False, False, False)
-#             existing_note.content = existing_note.content.replace("</en-note>", f"<div>{task_topic}</div></en-note>")
-#             note_store.updateNote(existing_note)
-#         else:
-#             # Create a new note
-#             note = Types.Note()
-#             note.title = notebook_title
-#             note.notebookGuid = notebook.guid
-#             note.content = f'<?xml version="1.0" encoding="UTF-8"?>' \
-#                            f'<!DOCTYPE en-note SYSTEM "http://xml.evernote.com/pub/enml2.dtd">' \
-#                            f'<en-note><div>{task_topic}</div></en-note>'
-#             note_store.createNote(note)
-
-#         print(f"Task '{task_topic}' successfully added to Evernote under '{notebook_title}'.")
-#     except Exception as e:
-#         print(f"Error adding task to Evernote: {e}")
-
-# Mock Functions for Task Actions
-def add_to_evernote(task_topic):
-    return f"Task added to Evernote with title '{task_topic}'."
-
-
-# In[8]:
-
-
-# Access the API keys and other configuration data
-TASK_WORKFLOW_TREE = load_reasoning_json('curify_ideas_reasoning.json')
-action_map = load_action_map('action_map.json')
-
-# In[9]:
-
-
-def generate_task_hash(task_description):
-    try:
-        # Ensure task_description is a string
-        if not isinstance(task_description, str):
-            logger.warning("task_description is not a string, attempting conversion.")
-            task_description = str(task_description)
-        
-        # Safely encode with UTF-8 and ignore errors
-        encoded_description = task_description.encode("utf-8", errors="ignore")
-        task_hash = hashlib.md5(encoded_description).hexdigest()
-
-        logger.debug(f"Generated task hash: {task_hash}")
-        return task_hash
-    except Exception as e:
-        # Log any unexpected issues
-        logger.error(f"Error generating task hash: {e}", exc_info=True)
-        return 'output'
-    
-def save_to_google_storage(bucket_name, file_path, destination_blob_name, expiration_minutes = 1440):
-    credentials_dict = json.loads(google_credentials_json)
-
-    # Step 3: Use `service_account.Credentials.from_service_account_info` to authenticate directly with the JSON
-    credentials = service_account.Credentials.from_service_account_info(credentials_dict)
-    gcs_client = storage.Client(credentials=credentials, project=credentials.project_id)
-
-    # Check if the bucket exists; if not, create it
-    try:
-        bucket = gcs_client.get_bucket(bucket_name)
-    except NotFound:
-        print(f"❌ Bucket '{bucket_name}' not found. Please check the bucket name.")
-        bucket = gcs_client.create_bucket(bucket_name)
-        print(f"✅ Bucket '{bucket_name}' created.")
-    except Exception as e:
-        print(f"❌ An unexpected error occurred: {e}")
-        raise
-    # Get a reference to the blob
-    blob = bucket.blob(destination_blob_name)
-    
-    # Upload the file
-    blob.upload_from_filename(file_path)
-    
-    # Generate a signed URL for the file
-    signed_url = blob.generate_signed_url(
-        version="v4",
-        expiration=timedelta(minutes=expiration_minutes),
-        method="GET"
-    )
-    print(f"✅ File uploaded to Google Cloud Storage. Signed URL: {signed_url}")
-    return signed_url
-
-
-# Function to check if content is Simplified Chinese
-def is_simplified(text):
-    simplified_range = re.compile('[\u4e00-\u9fff]')  # Han characters in general
-    simplified_characters = [char for char in text if simplified_range.match(char)]
-    return len(simplified_characters) > len(text) * 0.5  # Threshold of 50% to be considered simplified
-
-# Function to choose the appropriate font for the content
-def choose_font_for_content(content):
-    return 'NotoSansSC' if is_simplified(content) else 'NotoSansTC'
-
-# Function to generate and save a document using ReportLab
-def generate_document(task_description, md_content, user_name='jayw', bucket_name='curify'):
-    logger.debug("Starting to generate document")
-
-    # Hash the task description to generate a unique filename
-    task_hash = generate_task_hash(task_description)
-
-    # Truncate the hash if needed (64 characters is sufficient for uniqueness)
-    max_hash_length = 64  # Adjust if needed
-    truncated_hash = task_hash[:max_hash_length]
-
-    # Generate PDF file locally
-    local_filename = f"{truncated_hash}.pdf"  # Use the truncated hash as the local file name
-    c = canvas.Canvas(local_filename, pagesize=letter)
-
-    # Paths to the TTF fonts for Simplified and Traditional Chinese
-    sc_font_path = 'NotoSansSC-Regular.ttf'  # Path to Simplified Chinese font
-    tc_font_path = 'NotoSansTC-Regular.ttf'  # Path to Traditional Chinese font
-
-    try:
-        # Register the Simplified Chinese font
-        sc_font = TTFont('NotoSansSC', sc_font_path)
-        pdfmetrics.registerFont(sc_font)
-
-        # Register the Traditional Chinese font
-        tc_font = TTFont('NotoSansTC', tc_font_path)
-        pdfmetrics.registerFont(tc_font)
-        
-        # Set default font (Simplified Chinese or Traditional Chinese depending on content)
-        c.setFont('NotoSansSC', 12)
-    except Exception as e:
-        logger.error(f"Error loading font files: {e}")
-        raise RuntimeError("Failed to load one or more fonts. Ensure the font files are accessible.")
-
-    # Set initial Y position for drawing text
-    y_position = 750  # Starting position for text
-
-    # Process dictionary and render content
-    for key, value in md_content.items():
-        # Choose the font based on the key (header)
-        c.setFont(choose_font_for_content(key), 14)
-        c.drawString(100, y_position, f"# {key}")
-        y_position -= 20
-
-        # Choose the font for the value
-        c.setFont(choose_font_for_content(str(value)), 12)
-
-        # Add value
-        if isinstance(value, list):  # Handle lists
-            for item in value:
-                c.drawString(100, y_position, f"- {item}")
-                y_position -= 15
-        else:  # Handle single strings
-            c.drawString(100, y_position, value)
-            y_position -= 15
-
-        # Check if the page needs to be broken (if Y position is too low)
-        if y_position < 100:
-            c.showPage()  # Create a new page
-            c.setFont('NotoSansSC', 12)  # Reset font
-            y_position = 750  # Reset the Y position for the new page
-
-    # Save the PDF
-    c.save()
-
-    # Organize files into user-specific folders
-    destination_blob_name = f"{user_name}/{truncated_hash}.pdf"
-
-    # Upload to Google Cloud Storage and get the public URL
-    public_url = save_to_google_storage(bucket_name, local_filename, destination_blob_name)
-    logger.debug("Finished generating document")
-    return public_url
-    
-# In[10]:
-
-
-def execute_with_retry(sql, params=(), attempts=5, delay=1, db_name = 'curify_ideas.db'):
-    for attempt in range(attempts):
-        try:
-            with sqlite3.connect(db_name) as conn:
-                cursor = conn.cursor()
-                cursor.execute(sql, params)
-                conn.commit()
-                break
-        except sqlite3.OperationalError as e:
-            if "database is locked" in str(e) and attempt < attempts - 1:
-                time.sleep(delay)
-            else:
-                raise e
-
-# def enable_wal_mode(db_name = 'curify_ideas.db'):
-#     with sqlite3.connect(db_name) as conn:
-#         cursor = conn.cursor()
-#         cursor.execute("PRAGMA journal_mode=WAL;")
-#         conn.commit()
-
-# # Create SQLite DB and table
-# def create_db(db_name = 'curify_ideas.db'):
-#     with sqlite3.connect(db_name, timeout=30) as conn:
-#         c = conn.cursor()
-#         c.execute('''CREATE TABLE IF NOT EXISTS sessions (
-#             session_id TEXT,
-#             ip_address TEXT,
-#             project_desc TEXT,
-#             idea_desc TEXT,
-#             idea_analysis TEXT,
-#             prioritization_steps TEXT,
-#             timestamp DATETIME,
-#             PRIMARY KEY (session_id, timestamp)
-#             )
-#         ''')
-#         conn.commit()
-
-# # Function to insert session data into the SQLite database
-# def insert_session_data(session_id, ip_address, project_desc, idea_desc, idea_analysis, prioritization_steps, db_name = 'curify_ideas.db'):
-#     execute_with_retry('''
-#         INSERT INTO sessions (session_id, ip_address, project_desc, idea_desc, idea_analysis, prioritization_steps, timestamp)
-#         VALUES (?, ?, ?, ?, ?, ?, ?)
-#     ''', (session_id, ip_address, project_desc, idea_desc, json.dumps(idea_analysis), json.dumps(prioritization_steps), datetime.now()), db_name)
-
-
-# In[11]:
-
-
-def convert_to_listed_json(input_string):
-    """
-    Converts a string to a listed JSON object.
-    
-    Parameters:
-        input_string (str): The JSON-like string to be converted.
-    
-    Returns:
-        list: A JSON object parsed into a Python list of dictionaries.
-    """
-    try:
-        # Parse the string into a Python object
-        trimmed_string = input_string[input_string.index('['):input_string.rindex(']') + 1]
-
-        json_object = json.loads(trimmed_string)
-        return json_object
-    except json.JSONDecodeError as e:
-        return None
-    return None
-    #raise ValueError(f"Invalid JSON format: {e}")
-
-def validate_and_extract_json(json_string):
-    """
-    Validates the JSON string, extracts fields with possible variants using fuzzy matching.
-    
-    Args:
-    - json_string (str): The JSON string to validate and extract from.
-    - field_names (list): List of field names to extract, with possible variants.
-    
-    Returns:
-    - dict: Extracted values with the best matched field names.
-    """
-    # Try to parse the JSON string
-    trimmed_string = json_string[json_string.index('{'):json_string.rindex('}') + 1]
-    try:
-        parsed_json = json.loads(trimmed_string)
-        return parsed_json
-    except json.JSONDecodeError as e:
-        return None
-
-    # {"error": "Parsed JSON is not a dictionary."}
-    return None
-
-def json_to_pandas(dat_json, dat_schema = {'name':"", 'description':""}):
-    dat_df = pd.DataFrame([dat_schema])
-    try:
-        dat_df = pd.DataFrame(dat_json)
-
-    except Exception as e:
-        dat_df = pd.DataFrame([dat_schema])
-    # ValueError(f"Failed to parse LLM output as JSON: {e}\nOutput: {res}")
-    return dat_df
-
-
-# In[12]:
-
-
-client = OpenAI(
-    api_key= os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
-)
-
-# Function to call OpenAI API with compact error handling
-def call_openai_api(prompt, model="gpt-4o", max_tokens=5000, retries=3, backoff_factor=2):
-    """
-    Send a prompt to the OpenAI API and handle potential errors robustly.
-
-    Parameters:
-        prompt (str): The user input or task prompt to send to the model.
-        model (str): The OpenAI model to use (default is "gpt-4").
-        max_tokens (int): The maximum number of tokens in the response.
-        retries (int): Number of retry attempts in case of transient errors.
-        backoff_factor (int): Backoff time multiplier for retries.
-
-    Returns:
-        str: The model's response content if successful.
-    """
-    for attempt in range(1, retries + 1):
-        try:
-            response = client.chat.completions.create(
-            model="gpt-4o",
-            messages=[{"role": "user", "content": prompt}],
-            max_tokens=5000,
-            )        
-            return response.choices[0].message.content.strip()
-        
-        except (openai.RateLimitError, openai.APIConnectionError) as e:
-            logging.warning(f"Transient error: {e}. Attempt {attempt} of {retries}. Retrying...")
-        except (openai.BadRequestError, openai.AuthenticationError) as e:
-            logging.error(f"Unrecoverable error: {e}. Check your inputs or API key.")
-            break
-        except Exception as e:
-            logging.error(f"Unexpected error: {e}. Attempt {attempt} of {retries}. Retrying...")
-        
-        # Exponential backoff before retrying
-        if attempt < retries:
-            time.sleep(backoff_factor * attempt)
-    
-    raise RuntimeError(f"Failed to fetch response from OpenAI API after {retries} attempts.")
-
-def fn_analyze_task(project_context, task_description):
-    prompt = (
-            f"You are working in the context of {project_context}. "
-            f"Your task is to analyze the task: {task_description} "
-            "Please analyze the following aspects: "
-            "1) Determine which project this item belongs to. If the idea does not belong to any existing project, categorize it under 'Other'. "
-            "2) Assess whether this idea can be treated as a concrete task. "
-            "3) Evaluate whether a document can be generated as an intermediate result. "
-            "4) Identify the appropriate category of the task. Possible categories are: 'Blogs/Papers', 'Tools', 'Brainstorming', 'Issues', and 'Others'. "
-            "5) Extract the topic of the task. "
-            "Please provide the output in JSON format using the structure below: "
-            "{"
-            "  \"description\": \"\", "
-            "  \"project_association\": \"\", "
-            "  \"is_task\": \"Yes/No\", "
-            "  \"is_document\": \"Yes/No\", "
-            "  \"task_category\": \"\", "
-            "  \"task_topic\": \"\" "
-            "}"
-        )
-    res_task_analysis = call_openai_api(prompt)
-
-    try:
-        json_task_analysis = validate_and_extract_json(res_task_analysis)
-
-        return json_task_analysis
-    except ValueError as e:
-        logger.debug("ValueError occurred: %s", str(e), exc_info=True)  # Log the exception details
-        return None
-
-
-# In[13]:
-
-# Recursive Task Executor
-def fn_process_task(project_desc_table, task_description, bucket_name='curify'):
-    
-    project_context = project_desc_table.to_string(index=False)
-    task_analysis = fn_analyze_task(project_context, task_description)
-
-    if task_analysis:
-        execution_status = []
-        execution_results = task_analysis.copy()
-        execution_results['deliverables'] = ''
-
-        def traverse(node, previous_output=None):
-            if not node:  # If the node is None or invalid
-                return  # Exit if the node is invalid
-
-            # Check if there is a condition to evaluate
-            if "check" in node:
-                # Safely attempt to retrieve the value from execution_results
-                if node["check"] in execution_results:
-                    value = execution_results[node["check"]]  # Evaluate the check condition
-                    traverse(node.get(value, node.get("default")), previous_output)
-                else:
-                    # Log an error and exit, but keep partial results
-                    logger.error(f"Key '{node['check']}' not found in execution_results.")
-                    return
-        
-            # If the node contains an action
-            elif "action" in node:
-                action_name = node["action"]
-                input_key = node.get("input", 'task_topic')
-
-                if input_key in execution_results.keys():
-                    inputs = {input_key: execution_results[input_key]}
-                else:
-                    # Log an error and exit, but keep partial results
-                    logger.error(f"Workflow action {action_name} input key {input_key} not in execution_results.")
-                    return
-
-                logger.debug(f"Executing: {action_name} with inputs: {inputs}")
-                
-                # Execute the action function
-                action_func = action_map.get(action_name, unsupported_task)
-                try:
-                    output = action_func(**inputs)
-                except Exception as e:
-                    # Handle action function failure
-                    logger.error(f"Error executing action '{action_name}': {e}")
-                    return
-
-                # Store execution results or append to previous outputs
-                execution_status.append({"action": action_name, "output": output})
-
-                # Check if 'output' field exists in the node
-                if 'output' in node:
-                    # If 'output' exists, assign the output to execution_results with the key from node['output']
-                    execution_results[node['output']] = output
-                else:
-                    # If 'output' does not exist, append the output to 'deliverables'
-                    execution_results['deliverables'] += output
-                
-                # Traverse to the next node, if it exists
-                if "next" in node and node["next"]:
-                    traverse(node["next"], previous_output)
-
-        try:
-            traverse(TASK_WORKFLOW_TREE["start"])
-            execution_results['doc_url'] = generate_document(task_description, execution_results)
-        except Exception as e:
-            logger.error(f"Traverse Error: {e}")
-        finally:
-            # Always return partial results, even if an error occurs
-            return task_analysis, pd.DataFrame(execution_status), execution_results
-    else:
-        logger.error("Empty task analysis.")
-        return {}, pd.DataFrame(), {}
-
-# In[14]:
-
-
-# Initialize dataframes for the schema
-ideas_df = pd.DataFrame(columns=["Idea ID", "Content", "Tags"])
-
-def extract_ideas(context, text):
-    """
-    Extract project ideas from text, with or without a context, and return in JSON format.
-
-    Parameters:
-        context (str): Context of the extraction. Can be empty.
-        text (str): Text to extract ideas from.
-
-    Returns:
-        list: A list of ideas, each represented as a dictionary with name and description.
-    """
-    if context:
-        # Template when context is provided
-        prompt = (
-            f"You are working in the context of {context}. "
-            "Please extract the ongoing projects with project name and description."
-            "Please only the listed JSON as output string."
-            f"Ongoing projects: {text}"
-        )
-    else:
-        # Template when context is not provided
-        prompt = (
-            "Given the following information about the user."
-            "Please extract the ongoing projects with project name and description."
-            "Please only the listed JSON as output string."
-            f"Ongoing projects: {text}"
-        )
-
-    # return the raw string
-    return call_openai_api(prompt)
-
-def df_to_string(df, empty_message = ''):
-    """
-    Converts a DataFrame to a string if it is not empty. 
-    If the DataFrame is empty, returns an empty string.
-    
-    Parameters:
-        ideas_df (pd.DataFrame): The DataFrame to be converted.
-    
-    Returns:
-        str: A string representation of the DataFrame or an empty string.
-    """
-    if df.empty:
-        return empty_message
-    else:
-        return df.to_string(index=False)
-
-
-# In[15]:
-
-
-# Shared state variables
-shared_state = {"project_desc_table": pd.DataFrame(), "task_analysis_txt": "", "execution_status": pd.DataFrame(), "execution_results": {}}
-
-# Button Action: Fetch State
-def fetch_updated_state():
-    response = requests.get("http://localhost:5000/state")
-    state = response.json()
-    """Fetch the updated shared state from FastAPI."""
-    return pd.DataFrame(state["project_desc_table"]), state["task_analysis_txt"], pd.DataFrame(state["execution_status"]), state["execution_results"]
-
-def update_gradio_state(project_desc_table, task_analysis_txt, execution_status, execution_results):
-    # You can update specific components like Textbox or State
-    shared_state['project_desc_table'] = project_desc_table
-    shared_state['task_analysis_txt'] = task_analysis_txt
-    shared_state['execution_status'] = execution_status
-    shared_state['execution_results'] = execution_results
-    return True
-
-
-# In[16]:
-
-
-# # Initialize the database
-# new_db = 'curify.db'
-
-# # Copy the old database to a new one
-# shutil.copy("curify_idea.db", new_db)
-
-#create_db(new_db)
-#enable_wal_mode(new_db)
-def project_extraction(project_description):
-
-    str_projects = extract_ideas('AI-powered tools for productivity', project_description)
-    json_projects = convert_to_listed_json(str_projects)
-
-    project_desc_table = json_to_pandas(json_projects)
-    update_gradio_state(project_desc_table, "", pd.DataFrame(), {})
-    return project_desc_table
-
-
-# In[17]:
-
-
-# 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.'
-
-# # convert_to_listed_json(extract_ideas('AI-powered tools for productivity', project_description))
-
-# task_description = 'Build an interview bot for the curify digest project.'
-# task_analysis, reasoning_path = generate_reasoning_path(project_description, task_description)
-
-# steps = store_and_execute_task(task_description, reasoning_path)
-
-# def message_back(task_message, execution_status, doc_url, from_whatsapp):
-#     # Convert task steps to a simple numbered list
-#     task_steps_list = "\n".join(
-#         [f"{i + 1}. {step['action']} - {step.get('output', '')}" for i, step in enumerate(execution_status.to_dict(orient="records"))]
-#     )
-
-#     # Format the body message
-#     body_message = (
-#         f"*Task Message:*\n{task_message}\n\n"
-#         f"*Execution Status:*\n{task_steps_list}\n\n"
-#         f"*Doc URL:*\n{doc_url}\n\n"
-#     )
-
-#     # Send response back to WhatsApp
-#     try:
-#         twillo_client.messages.create(
-#             from_=twilio_phone_number,
-#             to=from_whatsapp,
-#             body=body_message
-#         )
-#     except Exception as e:
-#         logger.error(f"Twilio Error: {e}")
-#         raise HTTPException(status_code=500, detail=f"Error sending WhatsApp message: {str(e)}")
-
-#     return {"status": "success"}
-
-# # Initialize the Whisper pipeline
-# whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
-
-# # Function to transcribe audio from a media URL
-# def transcribe_audio_from_media_url(media_url):
-#     try:
-#         media_response = requests.get(media_url, auth=HTTPBasicAuth(account_sid, auth_token))
-#         # Download the media file
-#         media_response.raise_for_status()
-#         audio_data = media_response.content
-
-#         # Save the audio data to a file for processing
-#         audio_file_path = "temp_audio_file.mp3"
-#         with open(audio_file_path, "wb") as audio_file:
-#             audio_file.write(audio_data)
-
-#         # Transcribe the audio using Whisper
-#         transcription = whisper_pipeline(audio_file_path, return_timestamps=True)
-#         logger.debug(f"Transcription: {transcription['text']}")
-#         return transcription["text"]
-
-#     except Exception as e:
-#         logger.error(f"An error occurred: {e}")
-#         return None
-
-
-# In[18]:
-
+from fastapi import FastAPI
 
 app = FastAPI()
 
-# @app.get("/state")
-# async def fetch_state():
-#     return shared_state
-
-# @app.route("/whatsapp-webhook/", methods=["POST"])
-# async def whatsapp_webhook(request: Request):
-#     form_data = await request.form()
-#     # Log the form data to debug
-#     print("Received data:", form_data)
-    
-#     # Extract message and user information
-#     incoming_msg = form_data.get("Body", "").strip()
-#     from_number = form_data.get("From", "")
-#     media_url = form_data.get("MediaUrl0", "")
-#     media_type = form_data.get("MediaContentType0", "")
-
-#     # Initialize response variables
-#     transcription = None
-
-#     if media_type.startswith("audio"):
-#         # If the media is an audio or video file, process it
-#         try:
-#             transcription = transcribe_audio_from_media_url(media_url)
-#         except Exception as e:
-#             return JSONResponse(
-#                 {"error": f"Failed to process voice input: {str(e)}"}, status_code=500
-#             )
-#     # Determine message content: use transcription if available, otherwise use text message
-#     processed_input = transcription if transcription else incoming_msg
-
-#     logger.debug(f"Processed input: {processed_input}")
-#     try:
-#         # Generate response
-#         project_desc_table, _ = fetch_updated_state()
-#         if not project_desc_table.empty:
-#             task_analysis_txt, execution_status, execution_results = fn_process_task(project_desc_table, processed_input)
-#             update_gradio_state(task_analysis_txt, execution_status, execution_results)
-            
-#             doc_url = 'Fail to generate doc'
-#             if 'doc_url' in execution_results:
-#                 doc_url = execution_results['doc_url']
-
-#             # Respond to the user on WhatsApp with the processed idea
-#             response = message_back(processed_input, execution_status, doc_url, from_number)
-#             logger.debug(response)
-
-#             return JSONResponse(content=str(response))
-#     except Exception as e:
-#         logger.error(f"Error during task processing: {e}")
-#         return {"error": str(e)}
-
-
-# # In[19]:
-
-
-# # Mock Gmail Login Function
-# def mock_login(email):
-#     if email.endswith("@gmail.com"):
-#         return f"✅ Logged in as {email}", gr.update(visible=False), gr.update(visible=True)
-#     else:
-#         return "❌ Invalid Gmail address. Please try again.", gr.update(), gr.update()
-
-# # User Onboarding Function
-# def onboarding_survey(role, industry, project_description):
-#     return (project_extraction(project_description),
-#             gr.update(visible=False), gr.update(visible=True))
-
-# # Mock Integration Functions
-# def integrate_todoist():
-#     return "✅ Successfully connected to Todoist!"
-
-# def integrate_evernote():
-#     return "✅ Successfully connected to Evernote!"
-
-# def integrate_calendar():
-#     return "✅ Successfully connected to Google Calendar!"
-
-# def load_svg_with_size(file_path, width="600px", height="400px"):
-#     # Read the SVG content from the file
-#     with open(file_path, "r", encoding="utf-8") as file:
-#         svg_content = file.read()
-    
-#     # Add inline styles to control width and height
-#     styled_svg = f"""
-#     <div style="width: {width}; height: {height}; overflow: auto;">
-#         {svg_content}
-#     </div>
-#     """
-#     return styled_svg
-
-
-# # In[20]:
-
-
-# # Gradio Demo
-# def create_gradio_interface(state=None):
-#     with gr.Blocks(
-#         css="""
-#         .gradio-table td {
-#             white-space: normal !important;
-#             word-wrap: break-word !important;
-#         }
-#         .gradio-table {
-#             width: 100% !important;  /* Adjust to 100% to fit the container */
-#             table-layout: fixed !important;  /* Fixed column widths */
-#             overflow-x: hidden !important;  /* Disable horizontal scrolling */
-#         }
-#         .gradio-container {
-#             overflow-x: hidden !important;  /* Disable horizontal scroll for entire container */
-#             padding: 0 !important;  /* Remove any default padding */
-#         }
-#         .gradio-column {
-#             max-width: 100% !important;  /* Ensure columns take up full width */
-#             overflow: hidden !important;  /* Hide overflow to prevent horizontal scroll */
-#         }
-#         .gradio-row {
-#             overflow-x: hidden !important;  /* Prevent horizontal scroll on rows */
-#         }
-#     """) as demo:
-
-#     # Page 1: Mock Gmail Login
-#         with gr.Group(visible=True) as login_page:
-#             gr.Markdown("### **1️⃣ Login with Gmail**")
-#             email_input = gr.Textbox(label="Enter your Gmail Address", placeholder="[email protected]")
-#             login_button = gr.Button("Login")
-#             login_result = gr.Textbox(label="Login Status", interactive=False, visible=False)
-#         # Page 2: User Onboarding
-#         with gr.Group(visible=False) as onboarding_page:
-#             gr.Markdown("### **2️⃣ Tell Us About Yourself**")
-#             role = gr.Textbox(label="What is your role?", placeholder="e.g. Developer, Designer")
-#             industry = gr.Textbox(label="Which industry are you in?", placeholder="e.g. Software, Finance")
-#             project_description = gr.Textbox(label="Describe your project", placeholder="e.g. A task management app")
-#             submit_survey = gr.Button("Submit")
-
-#         # Page 3: Mock Integrations with Separate Buttons
-#         with gr.Group(visible=False) as integrations_page:
-#             gr.Markdown("### **3️⃣ Connect Integrations**")
-#             gr.Markdown("Click on the buttons below to connect each tool:")
-
-#             # Separate Buttons and Results for Each Integration
-#             todoist_button = gr.Button("Connect to Todoist")
-#             todoist_result = gr.Textbox(label="Todoist Status", interactive=False, visible=False)
-            
-#             evernote_button = gr.Button("Connect to Evernote")
-#             evernote_result = gr.Textbox(label="Evernote Status", interactive=False, visible=False)
-            
-#             calendar_button = gr.Button("Connect to Google Calendar")
-#             calendar_result = gr.Textbox(label="Google Calendar Status", interactive=False, visible=False)
-
-#             # Skip Button to proceed directly to next page
-#             skip_integrations = gr.Button("Skip ➡️")
-#             next_button = gr.Button("Proceed to QR Code")
-
-#         with gr.Group(visible=False) as qr_code_page:
-#             # Page 4: QR Code and Curify Ideas
-#             gr.Markdown("## Curify: Unified AI Tools for Productivity")
-            
-#             with gr.Tab("Curify Idea"):
-#                 with gr.Row():
-#                     with gr.Column():
-#                         gr.Markdown("#### ** QR Code**")
-#                         # Path to your local SVG file
-#                         svg_file_path = "qr.svg"
-#                         # Load the SVG content
-#                         svg_content = load_svg_with_size(svg_file_path, width="200px", height="200px")
-#                         gr.HTML(svg_content)
-
-#                     # Column 1: Webpage rendering
-#                     with gr.Column():
-                        
-#                         gr.Markdown("## Projects Overview")
-#                         project_desc_table = gr.DataFrame(
-#                             type="pandas"
-#                         )
-
-#                         gr.Markdown("## Enter task message.")
-#                         idea_input = gr.Textbox(
-#                             label=None,
-#                             placeholder="Describe the task you want to execute (e.g., Research Paper Review)")
-            
-#                         task_btn = gr.Button("Generate Task Steps")
-#                         fetch_state_btn = gr.Button("Fetch Updated State")
-
-#                     with gr.Column():
-#                         gr.Markdown("## Task analysis")
-#                         task_analysis_txt = gr.Textbox(
-#                             label=None,
-#                             placeholder="Here is the execution status of your task...")
-
-#                         gr.Markdown("## Execution status")
-#                         execution_status = gr.DataFrame(
-#                             type="pandas"
-#                         )
-#                         gr.Markdown("## Execution output")
-#                         execution_results = gr.JSON(
-#                             label=None
-#                         )
-#                         state_output = gr.State()  # Add a state output to hold the state
-
-#                 task_btn.click(
-#                     fn_process_task, 
-#                     inputs=[project_desc_table, idea_input], 
-#                     outputs=[task_analysis_txt, execution_status, execution_results]
-#                 )
-
-#                 fetch_state_btn.click(
-#                     fetch_updated_state, 
-#                     inputs=None, 
-#                     outputs=[project_desc_table, task_analysis_txt, execution_status, execution_results]
-#                 )
-
-#                 # Page 1 -> Page 2 Transition
-#                 login_button.click(
-#                     mock_login, 
-#                     inputs=email_input, 
-#                     outputs=[login_result, login_page, onboarding_page]
-#                 )
-
-#                 # Page 2 -> Page 3 Transition (Submit and Skip)
-#                 submit_survey.click(
-#                     onboarding_survey, 
-#                     inputs=[role, industry, project_description], 
-#                     outputs=[project_desc_table, onboarding_page, integrations_page]
-#                 )
-
-#                 # Integration Buttons
-#                 todoist_button.click(integrate_todoist, outputs=todoist_result)
-#                 evernote_button.click(integrate_evernote, outputs=evernote_result)
-#                 calendar_button.click(integrate_calendar, outputs=calendar_result)
-
-#                 # Skip Integrations and Proceed
-#                 skip_integrations.click(
-#                     lambda: (gr.update(visible=False), gr.update(visible=True)),
-#                     outputs=[integrations_page, qr_code_page]
-#                 )
-
-#         # # Set the load_fn to initialize the state when the page is loaded
-#         # demo.load(
-#         #     curify_ideas, 
-#         #     inputs=[project_input, idea_input], 
-#         #     outputs=[task_steps, task_analysis_txt, state_output]
-#         # )
-#     return demo
-#     # Load function to initialize the state
-#     # demo.load(load_fn, inputs=None, outputs=[state])  # Initialize the state when the page is loaded
-
-@app.on_event("startup")
-async def startup_event():
-    logger.debug("Application startup initiated")
-    # Simulate heavy operation
-    # from transformers import pipeline
-    # global model
-    # model = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
-    logger.debug("Application startup complete")
-
-# In[21]:
-@app.get("/", response_class=JSONResponse)
+@app.get("/")
 async def index():
-    return {"status": "FastAPI is running!"}
-
-
-# demo = create_gradio_interface()
-# # Use Gradio's `server_app` to get an ASGI app for Blocks
-# gradio_asgi_app = gr.routes.App.create_app(demo)
-
-# # Mount the Gradio ASGI app at "/gradio"
-# app.mount("/gradio", gradio_asgi_app)
-
-# # Redirect from the root endpoint to the Gradio app
-# @app.get("/", response_class=RedirectResponse)
-# async def index():
-#     return RedirectResponse(url="/gradio", status_code=307)
-
-# Run the FastAPI server using uvicorn
-if __name__ == "__main__":
-    uvicorn.run("app:app", host="0.0.0.0", port=5000, reload=True, log_level="debug")
-
-# In[ ]:
+    return {"message": "App is running"}