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Gradio ChatInterface for BeatDebate Music Recommendation System
This module provides a ChatGPT-style interface that showcases the 4-agent
planning system with real-time progress indicators and planning visualization.
"""
import logging
from typing import Dict, List, Optional, Tuple, Any
import gradio as gr
import requests
from .response_formatter import ResponseFormatter
from .planning_display import PlanningDisplay
# Import fallback service components
from ..services.llm_fallback_service import (
LLMFallbackService,
FallbackRequest,
FallbackTrigger
)
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Query examples from intent-aware recommendation system design document
QUERY_EXAMPLES = {
"By Artist": [
"Songs by Mk.gee",
"Give me tracks by Radiohead",
"Play some Beatles songs"
],
"Artist Similarity": [
"Songs like Mk.gee",
"Similar artists to BROCKHAMPTON",
"Songs that sound like Radiohead"
],
"Discovery": [
"Find me underground electronic music",
"Something completely new and different",
"Discover underground tracks by Kendrick Lamar"
],
"Genre/Mood": [
"Upbeat electronic music",
"Sad indie songs",
"Chill lo-fi hip hop"
],
"Contextual": [
"Music for studying",
"Workout playlist songs",
"Background music for coding"
],
"Hybrid": [
"Songs like Kendrick Lamar but jazzy",
"Songs by Michael Jackson that are R&B",
"Electronic music similar to Aphex Twin"
],
"Follow-ups": [
"More tracks",
"More like that",
"Similar to these",
"More from this artist",
"More underground like these",
"More for studying like these"
]
}
class BeatDebateChatInterface:
"""
ChatGPT-style interface for BeatDebate music recommendations.
Features:
- Real-time agent progress indicators
- Planning strategy visualization
- Audio preview integration
- Conversation history management
- Last.fm player embeds
- LLM fallback for unknown intents
"""
def __init__(self, backend_url: str = "http://localhost:8000"):
"""
Initialize the chat interface.
Args:
backend_url: URL of the FastAPI backend
"""
self.backend_url = backend_url
self.response_formatter = ResponseFormatter()
self.planning_display = PlanningDisplay()
# ✅ REMOVED: Global session management - now handled per-user via gr.State
# ✅ REMOVED: Global conversation history - now managed by backend session store
# Initialize fallback service
self.fallback_service = None
self._initialize_fallback_service()
logger.info(
f"BeatDebate Chat Interface initialized (multi-user safe), "
f"fallback_available: {self.fallback_service is not None}"
)
def _initialize_fallback_service(self) -> None:
"""Initialize the LLM fallback service."""
try:
# Import Gemini client creation function
from ..services.enhanced_recommendation_service import create_gemini_client
from ..api.rate_limiter import UnifiedRateLimiter
import os
# Get Gemini API key
gemini_api_key = os.getenv('GEMINI_API_KEY', 'demo_gemini_key')
if gemini_api_key and gemini_api_key != 'demo_gemini_key':
# Create Gemini client
gemini_client = create_gemini_client(gemini_api_key)
if gemini_client:
# Create rate limiter for fallback service
rate_limiter = UnifiedRateLimiter.for_gemini(calls_per_minute=8)
# Initialize fallback service
self.fallback_service = LLMFallbackService(
gemini_client=gemini_client,
rate_limiter=rate_limiter
)
logger.info("LLM fallback service initialized successfully")
else:
logger.warning("Failed to create Gemini client for fallback service")
else:
logger.warning("No valid Gemini API key found, fallback service disabled")
except Exception as e:
logger.error(f"Failed to initialize fallback service: {e}")
self.fallback_service = None
async def process_message(
self,
message: str,
history: List[Tuple[str, str]],
session_id: str
) -> Tuple[str, List[Tuple[str, str]], str, str]:
"""
Process user message and return response with track info.
Enhanced with fallback support and per-user session management.
Args:
message: User input message
history: Chat history as list of (user, assistant) tuples
session_id: User-specific session identifier
Returns:
Tuple of (response, updated_history, lastfm_player_html, updated_session_id)
"""
if not message.strip():
return "", history, "", session_id
logger.info(f"Processing message: {message} for session: {session_id}")
try:
# Primary: Get recommendations from 4-agent system with session ID
recommendations_response = await self._get_recommendations(message, session_id)
# Check if fallback is needed
should_fallback, trigger_reason = self._should_use_fallback(
recommendations_response
)
if should_fallback:
logger.info(f"Using LLM fallback for session {session_id} due to: {trigger_reason.value}")
recommendations_response = await self._get_fallback_recommendations(
message, trigger_reason, session_id
)
if recommendations_response:
# Get potentially updated session_id from backend
updated_session_id = recommendations_response.get("session_id", session_id)
# Format the response
formatted_response = (
self.response_formatter.format_recommendations(
recommendations_response
)
)
# Add to history using tuple format
history.append((message, formatted_response))
# Create Last.fm player HTML for latest recommendations
lastfm_player_html = self._create_lastfm_player_html(
recommendations_response.get("recommendations", [])
)
return "", history, lastfm_player_html, updated_session_id
else:
# Final emergency fallback
error_response = self._create_emergency_response(message)
history.append((message, error_response))
return "", history, "", session_id
except Exception as e:
logger.error(f"Error processing message for session {session_id}: {e}")
error_response = f"An error occurred: {str(e)}"
history.append((message, error_response))
return "", history, "", session_id
def _should_use_fallback(
self,
response: Optional[Dict]
) -> Tuple[bool, FallbackTrigger]:
"""
Determine if fallback should be used based on backend response.
Args:
response: Response from backend recommendation system
Returns:
Tuple of (should_fallback, trigger_reason)
"""
if response is None:
return True, FallbackTrigger.API_ERROR
# Check for explicit unknown intent
intent = response.get("intent", "").lower()
if intent in ["unknown", "unsupported", "fallback"]:
return True, FallbackTrigger.UNKNOWN_INTENT
# Check for empty recommendations
recommendations = response.get("recommendations", [])
if not recommendations or len(recommendations) == 0:
return True, FallbackTrigger.NO_RECOMMENDATIONS
# Check for error indicators
if response.get("error") or response.get("detail"):
return True, FallbackTrigger.API_ERROR
return False, None
async def _get_fallback_recommendations(
self,
query: str,
trigger_reason: FallbackTrigger,
session_id: str
) -> Optional[Dict[str, Any]]:
"""
Get fallback recommendations from LLM service.
Args:
query: User query
trigger_reason: Reason fallback was triggered
session_id: User-specific session identifier
Returns:
Fallback recommendations response or None if unavailable
"""
if not self.fallback_service:
logger.warning("Fallback service not available")
return None
try:
# Prepare fallback request
fallback_request = FallbackRequest(
query=query,
session_id=session_id,
chat_context=self._get_chat_context(),
trigger_reason=trigger_reason,
max_recommendations=10
)
# Get fallback recommendations
fallback_response = await self.fallback_service.get_fallback_recommendations(
fallback_request
)
# Add fallback disclaimer to explanation
if fallback_response and fallback_response.get("fallback_used"):
original_explanation = fallback_response.get("explanation", "")
fallback_explanation = (
f"**⚠️ DEFAULTING TO REGULAR LLM** - This query is outside our "
f"specialized 4-agent system's scope.\n\n{original_explanation}"
)
fallback_response["explanation"] = fallback_explanation
return fallback_response
except Exception as e:
logger.error(f"Fallback service failed: {e}")
return None
def _get_chat_context(self) -> Optional[Dict]:
"""Get chat context for fallback requests."""
# ✅ UPDATED: Chat context now managed entirely by backend session store
# The backend will retrieve session history based on session_id
# Frontend no longer maintains global conversation history
return None
def _create_emergency_response(self, query: str) -> str:
"""Create emergency response when all systems fail."""
return (
"**🚨 SYSTEM TEMPORARILY UNAVAILABLE**\n\n"
f"I apologize, but I'm unable to process your request for '{query}' "
"at the moment. Our recommendation systems are experiencing issues.\n\n"
"**Please try:**\n"
"- Waiting a few moments and trying again\n"
"- Simplifying your query (e.g., 'music like [artist name]')\n"
"- Checking your internet connection\n\n"
"We're working to restore full functionality. Thank you for your patience! 🎵"
)
async def _get_planning_strategy(self, query: str) -> Optional[Dict]:
"""Get planning strategy from backend."""
try:
response = requests.post(
f"{self.backend_url}/planning",
json={
"query": query,
"session_id": "planning-session" # Planning doesn't need user session
},
timeout=60
)
if response.status_code == 200:
return response.json()
else:
logger.error(
f"Planning request failed: {response.status_code}"
)
return None
except Exception as e:
logger.error(f"Error getting planning strategy: {e}")
return None
async def _get_recommendations(self, query: str, session_id: str) -> Optional[Dict]:
"""Get recommendations from backend with user-specific session context."""
try:
# Prepare request with user-specific session ID
request_data = {
"query": query,
"session_id": session_id,
"max_recommendations": 10,
"include_previews": True
}
# ✅ UPDATED: Chat context now retrieved by backend from session store
# No need to pass chat_context explicitly - backend handles it
response = requests.post(
f"{self.backend_url}/recommendations",
json=request_data,
timeout=120
)
if response.status_code == 200:
response_data = response.json()
return response_data
else:
logger.error(
f"Recommendations request failed: {response.status_code} for session {session_id}"
)
return None
except Exception as e:
logger.error(f"Error getting recommendations for session {session_id}: {e}")
return None
def _create_lastfm_player_html(self, recommendations: List[Dict]) -> str:
"""Create HTML for track preview links and info."""
if not recommendations:
return """
<div style="
padding: 20px;
text-align: center;
color: #cbd5e1;
background: rgba(30, 41, 59, 0.5);
border-radius: 0 0 12px 12px;
">
<p><em>No tracks yet!</em></p>
<p>Ask for music recommendations to see track info here</p>
</div>
"""
player_html = []
# Container with scrolling for all tracks
player_html.append("""
<div style="
max-height: 400px;
overflow-y: auto;
border-radius: 0 0 12px 12px;
background: rgba(30, 41, 59, 0.5);
">
""")
# Show all tracks with rank numbers
for i, rec in enumerate(recommendations):
rank = i + 1
title = rec.get("title", "Unknown Title")
artist = rec.get("artist", "Unknown Artist")
confidence = rec.get("confidence", 0.0)
confidence_pct = int(confidence * 100)
# Create search queries
search_query = f"{artist} {title}".replace(" ", "+")
lastfm_url = f"https://www.last.fm/search?q={search_query}"
spotify_url = f"https://open.spotify.com/search/{search_query}"
youtube_url = f"https://www.youtube.com/results?search_query={search_query}"
# Dark mode compatible colors based on confidence
if confidence_pct >= 70:
border_color = "#10b981" # emerald
bg_gradient = "linear-gradient(135deg, rgba(16, 185, 129, 0.15) 0%, rgba(5, 150, 105, 0.15) 100%)"
elif confidence_pct >= 50:
border_color = "#f59e0b" # amber
bg_gradient = "linear-gradient(135deg, rgba(245, 158, 11, 0.15) 0%, rgba(217, 119, 6, 0.15) 100%)"
else:
border_color = "#ef4444" # red
bg_gradient = "linear-gradient(135deg, rgba(239, 68, 68, 0.15) 0%, rgba(220, 38, 38, 0.15) 100%)"
player_html.append(f"""
<div style="
margin: 12px 20px;
padding: 15px;
background: {bg_gradient};
border-left: 4px solid {border_color};
border-radius: 8px;
box-shadow: 0 2px 8px rgba(0,0,0,0.3);
transition: all 0.2s ease;
border: 1px solid rgba(75, 85, 99, 0.3);
position: relative;
">
<!-- Rank Number -->
<div style="
position: absolute;
top: -8px;
left: 15px;
background: {border_color};
color: white;
width: 24px;
height: 24px;
border-radius: 50%;
display: flex;
align-items: center;
justify-content: center;
font-size: 12px;
font-weight: bold;
box-shadow: 0 2px 4px rgba(0,0,0,0.3);
">
{rank}
</div>
<div style="
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 8px;
margin-top: 8px;
">
<div style="
font-weight: 700;
color: #ffffff;
font-size: 15px;
text-shadow:
0 1px 3px rgba(0,0,0,0.8),
0 2px 6px rgba(0,0,0,0.6),
1px 1px 2px rgba(0,0,0,0.9);
">
{artist}
</div>
<div style="
background: {border_color};
color: white;
padding: 3px 10px;
border-radius: 12px;
font-size: 12px;
font-weight: bold;
text-shadow: 0 1px 2px rgba(0,0,0,0.3);
">
{confidence_pct}%
</div>
</div>
<div style="
color: #ffffff;
margin-bottom: 10px;
font-size: 14px;
font-weight: 600;
text-shadow:
0 1px 3px rgba(0,0,0,0.8),
0 2px 6px rgba(0,0,0,0.6),
1px 1px 2px rgba(0,0,0,0.9);
">
{title}
</div>
<div style="
display: flex;
gap: 8px;
font-size: 12px;
">
<a href="{lastfm_url}" target="_blank" style="
color: #fca5a5;
text-decoration: none;
padding: 4px 8px;
border-radius: 4px;
background: rgba(0, 0, 0, 0.3);
border: 1px solid rgba(239, 68, 68, 0.5);
transition: all 0.2s ease;
font-weight: 500;
">🎵 Last.fm</a>
<a href="{spotify_url}" target="_blank" style="
color: #86efac;
text-decoration: none;
padding: 4px 8px;
border-radius: 4px;
background: rgba(0, 0, 0, 0.3);
border: 1px solid rgba(34, 197, 94, 0.5);
transition: all 0.2s ease;
font-weight: 500;
">🎧 Spotify</a>
<a href="{youtube_url}" target="_blank" style="
color: #fda4af;
text-decoration: none;
padding: 4px 8px;
border-radius: 4px;
background: rgba(0, 0, 0, 0.3);
border: 1px solid rgba(244, 63, 94, 0.5);
transition: all 0.2s ease;
font-weight: 500;
">📺 YouTube</a>
</div>
</div>
""")
# Close the scrollable container
player_html.append("</div>")
return ''.join(player_html)
def create_interface(self) -> gr.Blocks:
"""
Create the Gradio interface.
Returns:
Gradio Blocks interface
"""
with gr.Blocks(
title="🎵 BeatDebate",
theme=gr.themes.Soft(
primary_hue="violet",
secondary_hue="blue",
neutral_hue="slate"
),
css="""
.main-container {
max-width: 1400px;
margin: 0 auto;
padding: 20px;
}
.header-section {
text-align: center;
margin-bottom: 30px;
padding: 20px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: 15px;
box-shadow: 0 4px 15px rgba(0,0,0,0.3);
}
.examples-section {
margin: 20px 0;
padding: 20px;
background: rgba(55, 65, 81, 0.8);
border-radius: 12px;
border: 1px solid rgba(75, 85, 99, 0.5);
}
.examples-section h2 {
color: #f8fafc !important;
margin-bottom: 15px;
}
.examples-section h3 {
color: #e2e8f0 !important;
margin-bottom: 10px;
}
.example-chip {
display: inline-block;
margin: 4px;
padding: 10px 16px;
background: rgba(30, 41, 59, 0.9) !important;
border: 1px solid rgba(100, 116, 139, 0.5) !important;
border-radius: 20px;
font-size: 14px;
font-weight: 500;
color: #f1f5f9 !important;
cursor: pointer;
transition: all 0.2s ease;
letter-spacing: 0.025em;
}
.example-chip:hover {
background: rgba(51, 65, 85, 0.9) !important;
border-color: rgba(139, 92, 246, 0.7) !important;
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(139, 92, 246, 0.2);
font-weight: 600;
}
.chat-container {
background: rgba(55, 65, 81, 0.8) !important;
border: 1px solid rgba(75, 85, 99, 0.5) !important;
border-radius: 15px 15px 0 0 !important;
border-bottom: none !important;
min-height: 400px !important;
}
.chat-header {
padding: 15px 20px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
text-align: center;
font-weight: 600;
font-size: 16px;
border-radius: 15px 15px 0 0;
margin: 0;
pointer-events: none;
}
.input-section {
margin-top: 0;
border-radius: 0 0 15px 15px;
background: rgba(55, 65, 81, 0.8);
padding: 20px;
border-top: 1px solid rgba(75, 85, 99, 0.5);
}
.info-container {
background: rgba(30, 41, 59, 0.6);
border-radius: 12px;
border: 1px solid rgba(75, 85, 99, 0.3);
margin-left: 20px;
}
.agent-info {
background: rgba(55, 65, 81, 0.8);
padding: 15px;
border-radius: 10px;
margin-bottom: 15px;
border: 1px solid rgba(75, 85, 99, 0.5);
color: #f1f5f9;
}
.agent-info h3 {
color: #f8fafc !important;
}
.agent-info ul li {
color: #e2e8f0 !important;
}
/* Global dark mode overrides */
.gradio-container {
background: #0f172a !important;
color: #f1f5f9 !important;
}
/* Input styling */
.gr-textbox input {
background: rgba(30, 41, 59, 0.9) !important;
border: 1px solid rgba(100, 116, 139, 0.5) !important;
color: #f1f5f9 !important;
}
.gr-textbox input::placeholder {
color: #94a3b8 !important;
}
/* Button styling */
.gr-button {
background: linear-gradient(135deg, #8b5cf6 0%, #7c3aed 100%) !important;
border: none !important;
color: white !important;
}
.gr-button:hover {
background: linear-gradient(135deg, #7c3aed 0%, #6d28d9 100%) !important;
box-shadow: 0 4px 12px rgba(139, 92, 246, 0.3) !important;
}
"""
) as interface:
# ✅ ADD: Per-user session state management
# NOTE: gr.State() without value generates unique session per user
import uuid
session_id_state = gr.State()
with gr.Column(elem_classes=["main-container"]):
# Header with gradient background
with gr.Column(elem_classes=["header-section"]):
gr.Markdown("""
# 🎵 BeatDebate
### AI Music Discovery with Intent-Aware 4-Agent Recommendation System
**🏆 AgentX Competition Entry** | **🚀 Advanced Agentic Planning System**
Discover perfect tracks using our sophisticated 4-agent AI system that understands your musical intent and collaborates intelligently!
**🔗 Competition Links:** [AgentX Submission](https://rdi.berkeley.edu/agentx/) • [GitHub Repository](https://github.com/SulmanK/BeatDebate)
""")
# Query examples prominently displayed
with gr.Column(elem_classes=["examples-section"]):
gr.Markdown("## 💡 **Try These Examples** - Click any to get started!")
gr.Markdown("*Our system recognizes all these intent types and optimizes agent coordination accordingly*")
example_buttons = []
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("**🎯 By Artist**")
for example in QUERY_EXAMPLES["By Artist"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
with gr.Column(scale=1):
gr.Markdown("**🎯 Artist Similarity**")
for example in QUERY_EXAMPLES["Artist Similarity"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
with gr.Column(scale=1):
gr.Markdown("**🎯 Discovery**")
for example in QUERY_EXAMPLES["Discovery"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("**🎵 Genre/Mood**")
for example in QUERY_EXAMPLES["Genre/Mood"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
with gr.Column(scale=1):
gr.Markdown("**📍 Contextual**")
for example in QUERY_EXAMPLES["Contextual"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
with gr.Column(scale=1):
gr.Markdown("**🎭 Hybrid Queries**")
for example in QUERY_EXAMPLES["Hybrid"]:
btn = gr.Button(
example,
elem_classes=["example-chip"],
size="sm",
variant="secondary"
)
example_buttons.append((btn, example))
# Main content area
with gr.Row():
with gr.Column(scale=7):
# Fixed chat header
gr.HTML(
"""<div class="chat-header">🎵 Music Recommendations</div>""",
elem_classes=[]
)
# Chat interface
chatbot = gr.Chatbot(
label="",
height=500,
elem_classes=["chat-container"],
show_label=False,
container=False,
render_markdown=True
)
# Input area connected to chat
with gr.Column(elem_classes=["input-section"]):
with gr.Row():
msg_input = gr.Textbox(
placeholder="What music are you in the mood for?",
label="",
scale=4,
lines=1,
show_label=False
)
send_btn = gr.Button(
"Send",
scale=1,
variant="primary",
size="lg"
)
with gr.Column(scale=3):
# Track info with improved styling
with gr.Column(elem_classes=["info-container"]):
gr.HTML("""
<div style="
padding: 20px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: 12px 12px 0 0;
text-align: center;
font-weight: 600;
font-size: 16px;
">
<h3 style="margin: 0; font-size: 18px; font-weight: 600;">🎧 Latest Tracks</h3>
<p style="margin: 8px 0 0 0; opacity: 0.9; font-size: 14px;">Click links to listen!</p>
</div>
""")
player_display = gr.HTML(
label="",
elem_classes=[]
)
# Agent system info with improved styling
with gr.Column(elem_classes=["agent-info"]):
gr.Markdown("""
### 🤖 **AI Agent System**
- **🧠 Planner**: Analyzes your query intent
- **🎵 GenreMood**: Finds style/vibe matches
- **🔍 Discovery**: Uncovers hidden gems
- **⚖️ Judge**: Ranks & selects best tracks
""")
# Event handlers with session state management
async def handle_message(message, history, session_id):
# Generate session ID if not already set (first interaction)
if session_id is None:
session_id = str(uuid.uuid4())
logger.info(f"🆕 Generated new session ID: {session_id}")
return await self.process_message(message, history, session_id)
# Example button handlers
for btn, example_text in example_buttons:
btn.click(
fn=lambda x=example_text: x,
inputs=[],
outputs=[msg_input]
)
# Submit on button click or enter with session state
send_btn.click(
fn=handle_message,
inputs=[msg_input, chatbot, session_id_state],
outputs=[msg_input, chatbot, player_display, session_id_state]
)
msg_input.submit(
fn=handle_message,
inputs=[msg_input, chatbot, session_id_state],
outputs=[msg_input, chatbot, player_display, session_id_state]
)
return interface
def create_chat_interface(backend_url: str = "http://localhost:8000") -> gr.Blocks:
"""
Factory function to create the BeatDebate chat interface.
Args:
backend_url: URL of the FastAPI backend
Returns:
Gradio Blocks interface
"""
chat_interface = BeatDebateChatInterface(backend_url)
return chat_interface.create_interface()
if __name__ == "__main__":
# For testing the interface standalone
interface = create_chat_interface()
interface.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
) |