Update app.py

app.py

@@ -1,13 +1,1160 @@
-from fastapi import FastAPI
+#!/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]:
+
 
 app = FastAPI()
 
-@app.get("/")
+# @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)
 async def index():
-    return {"message": "App is running"}
+    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__":
     port = int(os.getenv("PORT", 7860))  # Default to 7860 if PORT is not set
     uvicorn.run(app, host="0.0.0.0", port=port)