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import gradio as gr
import os
import base64
from datetime import datetime
import requests
import trello
from dotenv import load_dotenv
import urllib3
import wave
import audioop
import io
import speech_recognition as sr
# Disable SSL warning
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
# Load environment variables from .env file
load_dotenv()
SAMBANOVA_API_KEY = "34115dcb-baab-4390-ab5c-e501666f9f4e"
SAMBANOVA_URL = "https://api.sambanova.ai/v1/chat/completions"
# Initialize Trello client
trello_client = trello.TrelloClient(
api_key=os.getenv('TRELLO_API_KEY'),
token=os.getenv('TRELLO_TOKEN')
)
def get_trello_members():
"""Get all members from Trello workspace"""
try:
boards = trello_client.list_boards()
if not boards:
raise Exception("No Trello boards found")
board = boards[0]
members = board.get_members()
return {(member.full_name or member.username): member.id for member in members}
except Exception as e:
print(f"Error fetching Trello members: {str(e)}")
return {}
def process_audio_data(audio_path):
"""Process WAV audio file"""
try:
with wave.open(audio_path, 'rb') as wav_file:
# Get audio parameters
n_channels = wav_file.getnchannels()
sampwidth = wav_file.getsampwidth()
framerate = wav_file.getframerate()
n_frames = wav_file.getnframes()
# Read audio data
audio_data = wav_file.readframes(n_frames)
# Convert to mono if stereo
if n_channels == 2:
audio_data = audioop.tomono(audio_data, sampwidth, 1, 1)
# Convert to 16-bit if needed
if sampwidth != 2:
audio_data = audioop.lin2lin(audio_data, sampwidth, 2)
# Resample to 16kHz if needed
if framerate != 16000:
audio_data, _ = audioop.ratecv(audio_data, 2, 1, framerate, 16000, None)
framerate = 16000
return audio_data, framerate
except Exception as e:
print(f"Error processing audio: {str(e)}")
raise
def transcribe_audio(audio_file):
"""Convert audio to text using Speech Recognition"""
try:
# Initialize recognizer
recognizer = sr.Recognizer()
# Handle different audio input formats
if isinstance(audio_file, tuple):
audio_path = audio_file[0]
else:
audio_path = audio_file
print(f"Processing audio file: {audio_path}")
try:
# Load the audio file
with sr.AudioFile(audio_path) as source:
# Adjust for ambient noise
recognizer.adjust_for_ambient_noise(source)
# Read the audio data
audio_data = recognizer.record(source)
# Perform the transcription with increased timeout
text = recognizer.recognize_google(
audio_data,
language='en-US',
show_all=False,
with_confidence=False
)
if not text:
raise Exception("No transcription results returned")
return text.strip()
except sr.UnknownValueError:
raise Exception("Speech could not be understood. Please try speaking more clearly.")
except sr.RequestError as e:
raise Exception(f"Could not request results from Google Speech Recognition service; {e}")
except Exception as e:
print(f"Transcription error details: {str(e)}")
raise Exception(f"Transcription error: {str(e)}")
def analyze_emotion(text):
"""Analyze text emotion using Hugging Face API"""
API_URL = "https://api-inference.huggingface.co/models/SamLowe/roberta-base-go_emotions"
headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
try:
response = requests.post(API_URL, headers=headers, json={"inputs": text})
emotions = response.json()
# Extract emotion scores
if isinstance(emotions, list) and len(emotions) > 0:
emotion_scores = emotions[0]
# Define urgent emotions
urgent_emotions = ['anger', 'fear', 'annoyance', 'disapproval', 'nervousness']
high_priority_emotions = ['desire', 'excitement', 'surprise']
# Calculate urgency based on emotional content
urgent_score = sum(score for emotion, score in emotion_scores.items()
if emotion in urgent_emotions)
high_priority_score = sum(score for emotion, score in emotion_scores.items()
if emotion in high_priority_emotions)
# Determine urgency level based on emotion scores
if urgent_score > 0.5:
return "urgent"
elif high_priority_score > 0.4 or urgent_score > 0.3:
return "high"
return "normal"
return "normal"
except Exception as e:
print(f"Error in emotion analysis: {str(e)}")
return "normal"
def improve_task_description(text):
"""Improve and summarize task description using SambaNova API and emotion analysis"""
try:
# First analyze emotion to get initial urgency assessment
emotion_urgency = analyze_emotion(text)
prompt = f"""Please analyze and structure this task description, including determining its urgency level.
Original task: {text}
Initial emotion-based urgency assessment: {emotion_urgency}
Please provide:
1. A clear, concise task title
2. Key objectives
3. Suggested deadline (if not specified)
4. Any important details or requirements
5. Urgency level assessment (choose one: normal, high, urgent) based on:
- Time-sensitive language (ASAP, immediately, urgent, etc.)
- Deadlines mentioned
- Impact and consequences described
- Business criticality
- Emotional context and tone
Format the response with "URGENCY_LEVEL: [level]" as the first line, followed by the structured description.
Consider the emotion-based urgency assessment provided above when making the final urgency determination.
"""
headers = {
'Authorization': f'Bearer {SAMBANOVA_API_KEY}',
'Content-Type': 'application/json'
}
data = {
'messages': [
{'role': 'user', 'content': prompt}
],
'model': 'Meta-Llama-3.1-8B-Instruct',
'max_tokens': 2000,
'temperature': 0.7
}
response = requests.post(
SAMBANOVA_URL,
headers=headers,
json=data,
verify=False,
timeout=620
)
if response.status_code != 200:
raise Exception(f"SambaNova API request failed: {response.text}")
response_text = response.json()['choices'][0]['message']['content']
# Extract urgency level and description
lines = response_text.split('\n')
urgency_line = lines[0].strip()
# Use emotion-based urgency as fallback
urgency = emotion_urgency
if urgency_line.startswith("URGENCY_LEVEL:"):
level = urgency_line.split(":")[1].strip().lower()
if level in ["normal", "high", "urgent"]:
# Compare with emotion-based urgency and use the higher priority
urgency_levels = {"normal": 0, "high": 1, "urgent": 2}
if urgency_levels[level] > urgency_levels[emotion_urgency]:
urgency = level
description = '\n'.join(lines[1:]).strip()
else:
description = response_text
return description, urgency
except Exception as e:
raise Exception(f"Error improving task description: {str(e)}")
def create_trello_card(task_description, selected_members, location=None, urgency="normal"):
"""Create a Trello card with the improved task description"""
try:
boards = trello_client.list_boards()
if not boards:
raise Exception("No Trello boards found")
board = boards[0]
print(f"Using board: {board.name}")
lists = board.list_lists()
if not lists:
raise Exception("No lists found in the board")
todo_list = lists[0]
print(f"Using list: {todo_list.name}")
# Extract title and add timestamp
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M")
title = task_description.split('\n')[0]
# Add urgency to title
urgency_markers = {
"normal": "📘",
"high": "⚠️",
"urgent": "🔴"
}
urgency_marker = urgency_markers.get(urgency.lower(), "📘")
formatted_title = f"[{timestamp}] {urgency_marker} {title}"
location_text = "Remote/Virtual"
location_coords = None
if location:
location_text = location
# Map urgency to status text
urgency_status = {
"normal": "Normal Priority",
"high": "High Priority",
"urgent": "URGENT"
}
status_text = urgency_status.get(urgency.lower(), "Normal Priority")
formatted_description = f"""🎯 TASK DETAILS
------------------------
{task_description}
📋 METADATA
------------------------
🕒 Created: {timestamp}
🏷️ Source: TaskWhisper AI
⚡ Priority: {status_text}
📍 Location: {location_text}
✅ CHECKLIST
------------------------
- [ ] Task reviewed
- [ ] Requirements clear
- [ ] Timeline confirmed
- [ ] Resources identified
📝 NOTES
------------------------
Add your progress notes here...
"""
card = todo_list.add_card(
name=formatted_title,
desc=formatted_description
)
if location_coords:
card.set_pos(location_coords)
# Add label based on urgency
available_labels = board.get_labels()
urgency_colors = {
"normal": "blue",
"high": "yellow",
"urgent": "red"
}
label_color = urgency_colors.get(urgency.lower(), "blue")
# Find and add the appropriate label
priority_label = next((label for label in available_labels if label.color == label_color), None)
if priority_label:
card.add_label(priority_label)
else:
print(f"Warning: {label_color} label not found on board")
# Assign members to card
if selected_members:
for member_id in selected_members:
try:
member = next((m for m in board.get_members() if m.id == member_id), None)
if member:
card.add_member(member)
else:
print(f"Warning: Member with ID {member_id} not found on board")
except Exception as e:
print(f"Error adding member {member_id}: {str(e)}")
return card.url
except Exception as e:
print(f"Trello card creation error details: {str(e)}")
raise Exception(f"Error creating Trello card: {str(e)}")
def process_input(input_text, selected_members):
"""Process input text and create Trello card"""
try:
# Improve the task description and get urgency
improved_description, urgency = improve_task_description(input_text)
# Create Trello card with detected urgency
card_url = create_trello_card(improved_description, selected_members, urgency=urgency)
# Get member names for display
members_dict = get_trello_members()
member_names = [name for name, mid in members_dict.items()
if mid in selected_members]
urgency_emoji = {"normal": "📘", "high": "⚠️", "urgent": "🔴"}
return f"""
Original Input:
--------------
{input_text}
Improved Task Description:
------------------------
{improved_description}
Task Created in Trello:
----------------------
Priority: {urgency_emoji.get(urgency, "📘")} {urgency.upper()}
Assigned to: {', '.join(member_names) if member_names else 'Not assigned'}
Card URL: {card_url}
"""
except Exception as e:
return f"Error processing input: {str(e)}"
def process_audio(audio_file, selected_members):
"""Process audio input and create Trello card"""
try:
if audio_file is None:
return "Error: No audio file or text provided"
print(f"Audio file type: {type(audio_file)}") # Debug print
print(f"Audio file content: {audio_file}") # Debug print
text = transcribe_audio(audio_file)
return process_input(text, selected_members)
except Exception as e:
print(f"Audio processing error details: {str(e)}") # Debug print
return f"Error processing audio: {str(e)}"
def process_audio_with_members(audio, selected_members):
"""Process audio with selected members"""
try:
if audio is None:
return "Error: Please provide an audio input (record or upload)"
print(f"Received audio input: {type(audio)}")
print(f"Audio content: {audio}")
# Convert selected member names to member IDs
members_dict = get_trello_members()
selected_member_ids = []
for name in (selected_members or []):
if name in members_dict:
selected_member_ids.append(members_dict[name])
else:
print(f"Warning: Member {name} not found in members dictionary")
try:
result = process_audio(audio, selected_member_ids)
return result
except Exception as e:
error_msg = str(e)
if "Speech could not be understood" in error_msg:
return "Could not understand the speech. Please try again with clearer audio."
elif "Could not request results" in error_msg:
return "Network error. Please check your internet connection and try again."
else:
return f"Error processing audio: {error_msg}"
except Exception as e:
print(f"Error in process_audio_with_members: {str(e)}")
return f"Error processing audio with members: {str(e)}"
def process_text_with_members(text, selected_members):
"""Process text with selected members"""
try:
# Convert selected member names to member IDs
members_dict = get_trello_members()
# Debug prints
print(f"Members dict: {members_dict}")
print(f"Selected members: {selected_members}")
selected_member_ids = []
for name in (selected_members or []):
if name in members_dict:
selected_member_ids.append(members_dict[name])
else:
print(f"Warning: Member {name} not found in members dictionary")
return process_input(text, selected_member_ids)
except Exception as e:
print(f"Error in process_text_with_members: {str(e)}")
return f"Error processing text with members: {str(e)}"
# Create Gradio interface
custom_theme = gr.themes.Soft(
primary_hue="teal",
secondary_hue="blue",
neutral_hue="cyan",
font=("Inter", "ui-sans-serif", "system-ui", "sans-serif"),
font_mono=("IBM Plex Mono", "ui-monospace", "Consolas", "monospace"),
)
# Update theme colors with lighter shades
custom_theme.background_fill_primary = "#ffffff"
custom_theme.background_fill_secondary = "#f0fdfa"
custom_theme.border_color_primary = "#99f6e4"
custom_theme.button_primary_background_fill = "#14b8a6"
custom_theme.button_primary_background_fill_hover = "#0d9488"
custom_theme.button_primary_text_color = "white"
custom_theme.block_label_text_color = "#0f766e"
custom_theme.block_title_text_color = "#0f766e"
custom_theme.body_text_color = "#134e4a"
with gr.Blocks(
title="TaskWhisper",
theme=custom_theme,
css="""
/* Reset Gradio defaults */
.gradio-container {
margin: 0 !important;
padding: 0 !important;
max-width: none !important;
}
/* Remove Gradio footer */
footer {
display: none !important;
}
/* Custom container */
#custom-container {
min-height: 100vh;
background: #ffffff;
padding: 2rem;
}
/* Main content wrapper */
.main-content {
max-width: 1200px;
margin: 0 auto;
background: #ffffff;
border-radius: 20px;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
padding: 2rem;
}
/* Header styling */
.header {
text-align: center;
padding: 2rem 0;
border-bottom: 1px solid #eee;
margin-bottom: 2rem;
}
.header h1 {
color: #000000;
font-size: 2.5rem;
margin-bottom: 0.5rem;
font-weight: 600;
}
.header p {
color: #000000;
font-size: 1.1rem;
}
/* Tab styling */
.tabs {
background: none !important;
border: none !important;
}
.tab-nav {
background: none !important;
border-bottom: 1px solid #eee !important;
padding: 0 !important;
margin-bottom: 2rem !important;
}
.tab-nav button {
padding: 1rem 2rem !important;
margin-right: 1rem !important;
border-radius: 8px 8px 0 0 !important;
border: none !important;
background: #f8f8f8 !important;
color: #000000 !important;
font-weight: 500 !important;
}
.tab-nav button.selected {
background: #e8e8e8 !important;
color: #000000 !important;
font-weight: 600 !important;
}
/* Input container styling */
.input-container {
background: #fafafa;
border-radius: 12px;
padding: 2rem;
margin-bottom: 2rem;
border: 1px solid #eee;
}
/* Form elements */
.input-container label {
color: #000000;
font-weight: 500;
margin-bottom: 0.5rem;
}
.textbox textarea {
border: 1px solid #eee !important;
border-radius: 8px !important;
padding: 1rem !important;
font-size: 1rem !important;
color: #000000 !important;
background: #ffffff !important;
}
.textbox textarea:focus {
border-color: #000000 !important;
box-shadow: 0 0 0 1px rgba(0, 0, 0, 0.1) !important;
}
/* Dropdown styling */
.dropdown select {
background-color: white !important;
border: 1px solid #eee !important;
border-radius: 8px !important;
padding: 0.8rem !important;
color: #000000 !important;
}
/* Style the selected options in dropdown */
.selected-options {
color: #000000 !important;
background: white !important;
padding: 4px 8px !important;
border-radius: 4px !important;
margin: 2px !important;
}
/* Style the dropdown options */
.dropdown-option {
color: #ffffff !important;
background: white !important;
padding: 8px !important;
}
/* Style the dropdown container */
.dropdown-container {
background: white !important;
border: 1px solid #eee !important;
border-radius: 8px !important;
}
/* Style the dropdown arrow */
.dropdown-arrow {
color: #000000 !important;
}
/* Button styling */
.primary {
background: #000000 !important;
color: white !important;
padding: 0.8rem 1.5rem !important;
border-radius: 8px !important;
font-weight: 500 !important;
font-size: 1rem !important;
border: none !important;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important;
transition: all 0.2s ease !important;
}
.primary:hover {
background: #333333 !important;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
}
/* Audio component */
.audio-component {
background: #ffffff;
border: 1px solid #eee;
border-radius: 12px;
padding: 1.5rem;
margin-bottom: 1.5rem;
}
/* Output styling */
.output-markdown {
background: #ffffff;
border-radius: 12px;
padding: 2rem;
margin-top: 2rem;
border: 1px solid #eee;
color: #000000;
max-height: 400px !important; /* Fixed height */
overflow-y: auto !important; /* Enable vertical scrolling */
}
/* Style the output content */
.output-markdown p {
margin: 0.5rem 0;
line-height: 1.5;
}
.output-markdown pre {
white-space: pre-wrap;
word-wrap: break-word;
padding: 1rem;
background: #f8f8f8;
border-radius: 8px;
margin: 0.5rem 0;
}
/* Add custom scrollbar for output */
.output-markdown::-webkit-scrollbar {
width: 6px;
}
.output-markdown::-webkit-scrollbar-track {
background: #f8f8f8;
border-radius: 3px;
}
.output-markdown::-webkit-scrollbar-thumb {
background: #000000;
border-radius: 3px;
}
/* Custom scrollbar */
::-webkit-scrollbar {
width: 6px;
}
::-webkit-scrollbar-track {
background: #f8f8f8;
}
::-webkit-scrollbar-thumb {
background: #000000;
border-radius: 3px;
}
/* Remove Gradio branding */
.gr-interface {
background: none !important;
border: none !important;
box-shadow: none !important;
}
.footer {
border-top: 1px solid #eee;
margin-top: 3rem;
padding-top: 1.5rem;
text-align: center;
color: #000000;
}
/* Tips section */
.tips-section {
color: #000000 !important;
font-size: 0.9rem !important;
}
.tips-section ul {
margin-left: 1.2rem;
}
/* Make all text black */
* {
color: #000000;
}
"""
) as interface:
with gr.Column(elem_id="custom-container"):
with gr.Column(elem_classes="main-content"):
gr.Markdown(
"""
<div class='header'>
<h1>🎙️ TaskWhisper</h1>
<p>Transform your voice or text into structured tasks</p>
</div>
"""
)
# Get Trello members for the dropdown
members = get_trello_members()
with gr.Tab("Voice Input"):
with gr.Column(elem_classes="input-container"):
audio_input = gr.Audio(
label="Record or Upload Audio",
sources=["microphone", "upload"],
type="filepath",
format="wav",
interactive=True
)
gr.Markdown(
"""
<div class='tips-section'>
<p><strong>Tips for best results:</strong></p>
<ul>
<li>Speak clearly and at a normal pace</li>
<li>Use a quiet environment</li>
<li>Include key details like deadlines or priority</li>
</ul>
</div>
"""
)
member_dropdown_audio = gr.Dropdown(
choices=list(members.keys()),
multiselect=True,
label="Assign Task To",
info="Select team members",
value=[],
elem_classes="dropdown"
)
audio_button = gr.Button("Create Task", elem_classes="primary")
with gr.Tab("Text Input"):
with gr.Column(elem_classes="input-container"):
text_input = gr.Textbox(
lines=3,
placeholder="Describe your task here (e.g., 'Need to prepare quarterly report with sales data by next Friday')",
label="Task Description",
elem_classes="textbox"
)
member_dropdown_text = gr.Dropdown(
choices=list(members.keys()),
multiselect=True,
label="Assign Task To",
info="Select team members",
value=[],
elem_classes="dropdown"
)
text_button = gr.Button("Create Task", elem_classes="primary")
with gr.Column(elem_classes="output-markdown"):
output = gr.Markdown(
label="Task Details",
value="Task details will appear here..."
)
# Set up event handlers
audio_button.click(
fn=process_audio_with_members,
inputs=[audio_input, member_dropdown_audio],
outputs=output
)
text_button.click(
fn=process_text_with_members,
inputs=[text_input, member_dropdown_text],
outputs=output
)
gr.Markdown(
"""
<div class='footer'>
<p>TaskWhisper © 2024 | Powered by AI</p>
</div>
"""
)
if __name__ == "__main__":
interface.launch(
show_api=False, # Hide API docs
share=True,
favicon_path="🎙️" # Custom favicon
) |