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import os
import pandas as pd
import gradio as gr
import glob
import threading
import time
import queue
import numpy as np
from rank_bm25 import BM25Okapi
from dotenv import load_dotenv
from smolagents import CodeAgent, LiteLLMModel
from agent import create_web_agent, generate_prompt
from followup_agent import run_followup_analysis
from unidecode import unidecode
load_dotenv()
# Global variables for progress tracking
progress_queue = queue.Queue()
current_status = ""
# Initialize LLM translator and BM25
llm_translator = None
bm25_model = None
precomputed_titles = None
def initialize_models():
"""Initialize the LLM translator and BM25 model"""
global llm_translator, bm25_model, precomputed_titles
if llm_translator is None:
# Initialize LLM for translation
try:
model = LiteLLMModel(
model_id="gemini/gemini-2.5-flash-preview-05-20",
api_key=os.getenv("GEMINI_API_KEY")
)
llm_translator = CodeAgent(tools=[], model=model, max_steps=1)
print("✅ LLM translator initialized")
except Exception as e:
print(f"⚠️ Error initializing LLM translator: {e}")
# Load pre-computed BM25 model if available
if bm25_model is None:
try:
import pickle
with open('bm25_data.pkl', 'rb') as f:
bm25_data = pickle.load(f)
bm25_model = bm25_data['bm25_model']
precomputed_titles = bm25_data['titles']
print(f"✅ Loaded pre-computed BM25 model for {len(precomputed_titles)} datasets")
except FileNotFoundError:
print("⚠️ Pre-computed BM25 model not found. Will compute at runtime.")
except Exception as e:
print(f"⚠️ Error loading pre-computed BM25 model: {e}")
print("Will compute BM25 at runtime.")
def translate_query_llm(query, target_lang='fr'):
"""Translate query using LLM"""
global llm_translator
if llm_translator is None:
initialize_models()
if llm_translator is None:
print("⚠️ LLM translator not available, returning original query")
return query, 'unknown'
try:
# Create translation prompt
if target_lang == 'fr':
target_language = "French"
elif target_lang == 'en':
target_language = "English"
else:
target_language = target_lang
translation_prompt = f"""
Translate the following text to {target_language}.
If the text is already in {target_language}, return it as is.
Only return the translated text, nothing else.
Text to translate: "{query}"
"""
# Get translation from LLM
response = llm_translator.run(translation_prompt)
translated_text = str(response).strip().strip('"').strip("'")
# Simple language detection
if query.lower() == translated_text.lower():
source_lang = target_lang
else:
source_lang = 'en' if target_lang == 'fr' else 'fr'
return translated_text, source_lang
except Exception as e:
print(f"LLM translation error: {e}")
return query, 'unknown'
def simple_keyword_preprocessing(text):
"""Simple preprocessing for keyword matching - handles case, accents and basic plurals"""
# Convert to lowercase and remove accents
text = unidecode(str(text).lower())
# Basic plural handling - just remove trailing 's' and 'x'
words = text.split()
processed_words = []
for word in words:
# Remove common plural endings
if word.endswith('s') and len(word) > 3 and not word.endswith('ss'):
word = word[:-1]
elif word.endswith('x') and len(word) > 3:
word = word[:-1]
processed_words.append(word)
return processed_words
def find_similar_dataset_bm25(query, df):
"""Find the most similar dataset using BM25 keyword matching"""
global bm25_model, precomputed_titles
# Translate query to French for better matching with French datasets
translated_query, original_lang = translate_query_llm(query, target_lang='fr')
# Combine original and translated queries for search
search_queries = [query, translated_query] if query != translated_query else [query]
# Get dataset titles
dataset_titles = df['title'].fillna('').tolist()
# Use pre-computed BM25 model if available and matches current dataset
if (bm25_model is not None and precomputed_titles is not None and
len(dataset_titles) == len(precomputed_titles) and dataset_titles == precomputed_titles):
print("🚀 Using pre-computed BM25 model for fast matching")
bm25 = bm25_model
else:
# Build BM25 model at runtime
print("⚠️ Computing BM25 model at runtime...")
# Preprocess all dataset titles into tokenized form
processed_titles = [simple_keyword_preprocessing(title) for title in dataset_titles]
bm25 = BM25Okapi(processed_titles)
best_score = -1
best_idx = 0
for search_query in search_queries:
try:
# Preprocess the search query
processed_query = simple_keyword_preprocessing(search_query)
# Get BM25 scores for all documents
scores = bm25.get_scores(processed_query)
max_score = scores.max()
max_idx = scores.argmax()
if max_score > best_score:
best_score = max_score
best_idx = max_idx
except Exception as e:
print(f"Error processing query '{search_query}': {e}")
continue
# Show top 5 matches for comparison
if len(search_queries) > 0:
processed_query = simple_keyword_preprocessing(search_queries[0])
scores = bm25.get_scores(processed_query)
return best_idx, best_score, translated_query, original_lang
def create_progress_callback():
"""Create a callback function for tracking agent progress"""
def progress_callback(memory_step, agent=None):
"""Callback function called at each agent step"""
step_number = memory_step.step_number
# Extract information about the current step
if hasattr(memory_step, 'action_input') and memory_step.action_input:
action_content = memory_step.action_input
elif hasattr(memory_step, 'action_output') and memory_step.action_output:
action_content = str(memory_step.action_output)
else:
action_content = ""
# Define progress based on step content and number
progress_val = min(0.1 + (step_number * 0.03), 0.95) # Progressive increase
# Analyze the step content to provide meaningful status
action_lower = action_content.lower() if action_content else ""
if "visit_webpage" in action_lower or "examining" in action_lower:
description = f"🔍 Step {step_number}: Examining webpage..."
elif "get_all_links" in action_lower or "links" in action_lower:
description = f"🔗 Step {step_number}: Extracting data links..."
elif "read_file_from_url" in action_lower or "reading" in action_lower:
description = f"📊 Step {step_number}: Loading dataset..."
elif "get_dataset_description" in action_lower or "description" in action_lower:
description = f"📋 Step {step_number}: Analyzing dataset structure..."
elif "department" in action_lower or "region" in action_lower:
description = f"🗺️ Step {step_number}: Processing geographic data..."
elif "plot" in action_lower or "map" in action_lower or "france" in action_lower:
description = f"🗺️ Step {step_number}: Creating France map..."
elif "visualization" in action_lower or "chart" in action_lower:
description = f"📈 Step {step_number}: Generating visualizations..."
elif "save" in action_lower or "png" in action_lower:
description = f"💾 Step {step_number}: Saving visualizations..."
elif "docx" in action_lower or "report" in action_lower:
description = f"📄 Step {step_number}: Creating DOCX report..."
elif hasattr(memory_step, 'error') and memory_step.error:
description = f"⚠️ Step {step_number}: Handling error..."
else:
description = f"🤖 Step {step_number}: Processing..."
# Check if this is the final step
if hasattr(memory_step, 'action_output') and memory_step.action_output and "final" in action_lower:
progress_val = 1.0
description = "✅ Analysis complete!"
# Put the progress update in the queue
try:
progress_queue.put((progress_val, description))
except:
pass
return progress_callback
def run_agent_analysis_with_progress(query, progress_callback, df=None, page_url_callback=None, data_gouv_page=None, most_similar_idx=None):
"""
Run the agent analysis with progress tracking using smolagents callbacks.
"""
try:
# Clean up previous results
if os.path.exists('generated_data'):
for file in glob.glob('generated_data/*'):
try:
os.remove(file)
except:
pass
else:
os.makedirs('generated_data', exist_ok=True)
# If dataset info not provided, find it (fallback)
if data_gouv_page is None or most_similar_idx is None:
progress_callback(0.02, "🤖 Initializing LLM translator and BM25...")
initialize_models()
progress_callback(0.05, "🔍 Searching for relevant datasets (using BM25 keyword matching)...")
# Read the filtered dataset if not provided
if df is None:
df = pd.read_csv('filtered_dataset.csv')
# Find the most similar dataset using BM25 keyword matching
most_similar_idx, similarity_score, translated_query, original_lang = find_similar_dataset_bm25(query, df)
data_gouv_page = df.iloc[most_similar_idx]['url']
# Immediately show the page URL via callback
if page_url_callback:
page_url_callback(data_gouv_page)
progress_callback(0.08, "🤖 Initializing agent...")
else:
# Dataset already found, continue from where we left off
progress_callback(0.09, "🤖 Initializing agent...")
step_callback = create_progress_callback()
progress_callback(0.1, "🤖 Starting agent analysis...")
# Create the agent with progress callback
web_agent = create_web_agent(step_callback)
prompt = generate_prompt(data_gouv_page)
# Run the agent - the step_callbacks will automatically update progress
answer = web_agent.run(prompt)
# Check if the agent found no processable data
answer_lower = str(answer).lower() if answer else ""
if ("no processable data" in answer_lower or
"no csv nor json" in answer_lower or
"cannot find csv" in answer_lower or
"cannot find json" in answer_lower or
"no data to process" in answer_lower):
progress_callback(1.0, "❌ No CSV/JSON files found in the dataset")
return "❌ No CSV/JSON files found in the selected dataset. This dataset cannot be processed automatically.", [], data_gouv_page
# Check if files were generated
generated_files = glob.glob('generated_data/*')
if generated_files:
progress_callback(1.0, "✅ Analysis completed successfully!")
return "Analysis completed successfully!", generated_files, data_gouv_page
else:
progress_callback(1.0, "⚠️ Analysis completed but no files were generated.")
return "Analysis completed but no files were generated.", [], data_gouv_page
except Exception as e:
progress_callback(1.0, f"❌ Error: {str(e)}")
return f"Error during analysis: {str(e)}", [], None
def search_and_analyze(query, progress=gr.Progress()):
"""
Unified function that does initial search then lets agent analyze with full autonomy.
Uses Gradio's progress bar for visual feedback.
"""
# Clear the progress queue
while not progress_queue.empty():
try:
progress_queue.get_nowait()
except queue.Empty:
break
# Initialize outputs
docx_file = None
images_output = [gr.Image(visible=False)] * 4
status = "🚀 Starting agent-driven analysis..."
# Initial progress
progress(0.05, desc="🚀 Initializing agent...")
def progress_callback(progress_val, description):
"""Callback function to update progress - puts updates in queue"""
try:
progress_queue.put((progress_val, description))
except:
pass
# Run analysis in a separate thread
result_queue = queue.Queue()
def run_analysis():
try:
# Clean up previous results
if os.path.exists('generated_data'):
for file in glob.glob('generated_data/*'):
try:
os.remove(file)
except:
pass
else:
os.makedirs('generated_data', exist_ok=True)
# Do initial search if query provided
initial_search_results = None
if query.strip():
progress_callback(0.06, f"🔍 Initial search for: {query[:50]}...")
try:
# Import search function from tools
from tools.retrieval_tools import search_datasets
initial_search_results = search_datasets(query, top_k=5)
progress_callback(0.08, "🤖 Starting agent with search results...")
except Exception as e:
print(f"Initial search failed: {e}")
progress_callback(0.08, "🤖 Starting agent without initial results...")
else:
progress_callback(0.08, "🤖 Starting agent for random selection...")
step_callback = create_progress_callback()
# Create the agent with progress callback
web_agent = create_web_agent(step_callback)
# Generate unified prompt with initial search results
prompt = generate_prompt(user_query=query, initial_search_results=initial_search_results)
progress_callback(0.1, "🤖 Agent analyzing datasets...")
# Run the agent - the step_callbacks will automatically update progress
answer = web_agent.run(prompt)
# Check if the agent found no processable data
answer_lower = str(answer).lower() if answer else ""
if ("no processable data" in answer_lower or
"no csv nor json" in answer_lower or
"cannot find csv" in answer_lower or
"cannot find json" in answer_lower or
"no data to process" in answer_lower):
progress_callback(1.0, "❌ No CSV/JSON files found in the dataset")
result_queue.put(("❌ No CSV/JSON files found in the selected dataset. This dataset cannot be processed automatically.", [], None))
return
# Check if files were generated
generated_files = glob.glob('generated_data/*')
if generated_files:
progress_callback(1.0, "✅ Analysis completed successfully!")
result_queue.put(("Analysis completed successfully!", generated_files, "Agent-selected dataset"))
else:
progress_callback(1.0, "⚠️ Analysis completed but no files were generated.")
result_queue.put(("Analysis completed but no files were generated.", [], None))
except Exception as e:
progress_callback(1.0, f"❌ Error: {str(e)}")
result_queue.put((f"Error during analysis: {str(e)}", [], None))
analysis_thread = threading.Thread(target=run_analysis)
analysis_thread.start()
# Show initial status
current_status = "🤖 Agent is finding relevant datasets..."
progress(0.08, desc=current_status)
# Monitor progress while analysis runs
last_progress = 0.08
while analysis_thread.is_alive() or not result_queue.empty():
try:
# Check for progress updates from queue
try:
progress_val, description = progress_queue.get(timeout=0.1)
if progress_val > last_progress:
last_progress = progress_val
current_status = description
progress(progress_val, desc=description)
except queue.Empty:
pass
# Check if analysis is complete
try:
final_status, files, page_url = result_queue.get(timeout=0.1)
# Check if this is a "no data" case
if "❌ No CSV/JSON files found" in final_status:
progress(1.0, desc="❌ No processable data found")
return (gr.Textbox(value="Agent-selected dataset", visible=True),
final_status,
gr.File(visible=False),
gr.Image(visible=False), gr.Image(visible=False),
gr.Image(visible=False), gr.Image(visible=False),
gr.Markdown(visible=False), # keep follow-up hidden
gr.HTML(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False))
# Final progress update
progress(1.0, desc="✅ Processing results...")
# Process results
docx_file = None
png_files = []
for file in files:
if file.endswith('.docx'):
docx_file = file
elif file.endswith('.png'):
png_files.append(file)
# Prepare final outputs
download_button = gr.File(value=docx_file, visible=True) if docx_file else None
# Prepare images for display (up to 4 images)
images = []
for i in range(4):
if i < len(png_files):
images.append(gr.Image(value=png_files[i], visible=True))
else:
images.append(gr.Image(visible=False))
# final progress completion
progress(1.0, desc="🎉 Complete!")
# Show follow-up section after successful completion
return (gr.Textbox(value=page_url if page_url else "Agent-selected dataset", visible=True),
final_status, download_button, *images,
gr.Markdown(visible=True), # followup_section_divider
gr.HTML(visible=True), # followup_section_header
gr.Row(visible=True), # followup_input_row
gr.Row(visible=True), # followup_result_row
gr.Row(visible=True), # followup_image_row
gr.Row(visible=True), # followup_examples_header_row
gr.Row(visible=True)) # followup_examples_row
except queue.Empty:
pass
time.sleep(0.5) # Small delay to prevent excessive updates
except Exception as e:
progress(1.0, desc=f"❌ Error: {str(e)}")
return (gr.Textbox(value="Error", visible=True), f"❌ Error: {str(e)}", None, *images_output,
gr.Markdown(visible=False), # keep follow-up hidden on error
gr.HTML(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False))
# Ensure thread completes
analysis_thread.join(timeout=1)
# Fallback return
progress(1.0, desc="🏁 Finished")
return (gr.Textbox(value="Completed", visible=True), current_status, docx_file, *images_output,
gr.Markdown(visible=False), # keep follow-up hidden
gr.HTML(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False),
gr.Row(visible=False))
def run_followup_question(question, progress=gr.Progress()):
"""
Run a follow-up analysis based on user's question about the previous report.
"""
if not question.strip():
return "Please enter a follow-up question.", gr.Image(visible=False)
progress(0.1, desc="🤖 Starting follow-up analysis...")
try:
# Check if there are previous results
if not os.path.exists('generated_data') or not os.listdir('generated_data'):
return "No previous analysis found. Please run an analysis first.", gr.Image(visible=False)
progress(0.3, desc="🔍 Analyzing previous report and dataset...")
# Run the follow-up analysis
result = run_followup_analysis(question)
progress(0.9, desc="📊 Processing results...")
# Look for new visualizations created by the follow-up analysis
import glob
# Get all images that were created after the analysis started
all_images = glob.glob('generated_data/*.png')
# Get recent images (created in the last few seconds)
import time
current_time = time.time()
recent_images = []
for img_path in all_images:
img_time = os.path.getctime(img_path)
if current_time - img_time < 120: # Images created in last 2 minutes
recent_images.append(img_path)
# Get the most recent image if any
latest_image = None
if recent_images:
latest_image = max(recent_images, key=os.path.getctime)
progress(1.0, desc="✅ Follow-up analysis complete!")
# Enhanced result formatting
final_result = result
if latest_image:
final_result += f"\n\n📊 **Visualization Created:** {os.path.basename(latest_image)}"
if len(recent_images) > 1:
final_result += f"\n📈 **Total new visualizations:** {len(recent_images)}"
return final_result, gr.Image(value=latest_image, visible=True)
else:
return final_result, gr.Image(visible=False)
except Exception as e:
progress(1.0, desc="❌ Error in follow-up analysis")
return f"Error: {str(e)}", gr.Image(visible=False)
# Create the Gradio interface
with gr.Blocks(title="🤖 French Public Data Analysis Agent", theme=gr.themes.Soft(), css="""
.gradio-container {
max-width: 1200px !important;
margin: auto;
width: 100% !important;
}
.main-header {
text-align: center;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 2rem;
border-radius: 15px;
margin-bottom: 2rem;
box-shadow: 0 8px 32px rgba(0,0,0,0.1);
}
.accordion-content {
overflow: hidden !important;
width: 100% !important;
}
.gr-accordion {
width: 100% !important;
max-width: 100% !important;
}
.gr-accordion .gr-row {
width: 100% !important;
max-width: 100% !important;
margin: 0 !important;
}
.gr-accordion .gr-column {
min-width: 0 !important;
flex: 1 !important;
max-width: 50% !important;
padding-right: 1rem !important;
}
.gr-accordion .gr-column:last-child {
padding-right: 0 !important;
padding-left: 1rem !important;
}
""") as demo:
# Main header with better styling
gr.HTML("""
<div class="main-header">
<h1 style="margin: 0; font-size: 2.5rem; font-weight: bold;">
🤖 French Public Data Analysis Agent
</h1>
<p style="font-size: 1.2rem; opacity: 0.9;">
Intelligent analysis of French public datasets with AI-powered insights
</p>
</div>
""")
# What this agent does
gr.HTML("""
<div style="text-align: center; background: #f8fafc; padding: 1.5rem; border-radius: 10px; margin: 1rem 0;">
<p style="font-size: 1.1rem; color: #374151; margin: 0;">
🌐 <strong>Search in French or English</strong> • 🤖 <strong>AI Agent finds & analyzes datasets</strong> • 🗺️ <strong>Generate Reports with visualizations</strong>
</p>
<p style="font-size: 0.9rem; color: #6b7280; margin-top: 0.5rem;">
Initial search results guide the agent, but it can search for different datasets if needed
</p>
</div>
""")
# Tips & Information accordion - moved to the top
with gr.Accordion("💡 Tips & Information", open=False):
with gr.Row():
with gr.Column():
gr.Markdown("""
🎯 **How to Use:**
- Enter search terms related to French public data
- Leave empty for random high-quality dataset selection
- System provides initial search results to guide the agent
- Agent can use provided results or search for different datasets
- Results include visualizations and downloadable reports
⏱️ **Processing Time:**
- Analysis takes 7-15 minutes depending on dataset complexity
- Agent has full autonomy to find the best datasets
""")
with gr.Column():
gr.Markdown("""
⚠️ **Important Notes:**
- Agent gets initial search results but has full autonomy to make decisions
- Agent can choose from initial results or search for different datasets
- Some datasets may not contain processable CSV/JSON files
- All visualizations are automatically generated
- Maps focus on France when geographic data is available
🌐 **Language Support:**
- Search in French or English - queries are automatically translated
""")
with gr.Row():
query_input = gr.Textbox(
label="Search Query",
placeholder="e.g., road traffic accidents, education, housing (or leave empty for random selection)",
scale=4
)
search_button = gr.Button(
"🚀 Analyze Dataset",
variant="primary",
scale=1,
size="lg"
)
# Quick Start Examples row
with gr.Row():
gr.HTML("""
<div>
<h3 style="color: #374151">🚀 Quick Start Examples</h3>
<p style="color: #6b7280">Click any example below to get started</p>
</div>
""")
with gr.Row():
examples = [
("🚗 Road Traffic Accidents 2023", "road traffic accidents 2023"),
("🎓 Education Directory", "education directory"),
("🏠 French Vacant Housing Private Park", "French vacant housing private park"),
]
for emoji_text, query_text in examples:
gr.Button(
emoji_text,
variant="secondary",
size="sm"
).click(
lambda x=query_text: x,
outputs=query_input
)
# Page info and analysis status with progress bar
with gr.Group():
page_url_display = gr.Textbox(label="🔗 Page Started On", interactive=False, visible=False)
with gr.Row():
status_output = gr.Textbox(label="📊 Analysis Status", interactive=False, scale=1)
# Download section
with gr.Row():
download_button = gr.File(
label="📄 Download DOCX Report",
visible=False
)
gr.Markdown("---")
gr.HTML("""
<div style="text-align: center; margin: 2rem 0;">
<h2 style="color: #374151; margin-bottom: 0.5rem;">📊 Generated Visualizations</h2>
<p style="color: #6b7280; margin: 0;">Automatically generated charts and maps will appear below</p>
</div>
""")
with gr.Row():
with gr.Column():
image1 = gr.Image(label="📈 Chart 1", visible=False, height=400)
image2 = gr.Image(label="📊 Chart 2", visible=False, height=400)
with gr.Column():
image3 = gr.Image(label="🗺️ Map/Chart 3", visible=False, height=400)
image4 = gr.Image(label="📉 Chart 4", visible=False, height=400)
# Follow-up Analysis Section (initially hidden)
followup_section_divider = gr.Markdown("---", visible=False)
followup_section_header = gr.HTML("""
<div style="text-align: center; margin: 2rem 0;">
<h2 style="color: #374151; margin-bottom: 0.5rem;">🤖 Follow-up Analysis</h2>
<p style="color: #6b7280; margin: 0;">Ask about report findings, request data analysis, or get contextual information</p>
</div>
""", visible=False)
with gr.Row(visible=False) as followup_input_row:
followup_input = gr.Textbox(
label="Follow-up Question",
placeholder="e.g., What are the main findings?, Show me correlation between columns, What is road safety policy in France?",
scale=4
)
followup_button = gr.Button(
"🔍 Analyze",
variant="secondary",
scale=1,
size="lg"
)
with gr.Row(visible=False) as followup_result_row:
followup_result = gr.Textbox(
label="📊 Follow-up Analysis Results",
interactive=False,
lines=10,
visible=True
)
with gr.Row(visible=False) as followup_image_row:
followup_image = gr.Image(
label="📈 Follow-up Visualization",
visible=False,
height=500
)
# Follow-up Examples (initially hidden)
with gr.Row(visible=False) as followup_examples_header_row:
gr.HTML("""
<div>
<h4 style="color: #374151">💡 Example Follow-up Questions</h4>
<p style="color: #6b7280">Click any example below to try it out</p>
</div>
""")
with gr.Row(visible=False) as followup_examples_row:
followup_examples = [
("📋 Report Summary", "What were the main findings from the analysis?"),
("🌐 Context Info", "What is the policy context for this data in France?"),
("📊 Create Chart", "Show me the correlation between two numerical columns with a scatter plot"),
("📈 Data Statistics", "Give me statistical summary for a specific column"),
("🎯 Filter Data", "Filter the data by specific criteria and show results"),
("🔍 General Question", "Tell me more about this topic and its importance"),
]
for emoji_text, query_text in followup_examples:
gr.Button(
emoji_text,
variant="secondary",
size="sm"
).click(
lambda x=query_text: x,
outputs=followup_input
)
# Set up the search button click event with progress bar
search_button.click(
fn=search_and_analyze,
inputs=[query_input],
outputs=[page_url_display, status_output, download_button, image1, image2, image3, image4,
followup_section_divider, followup_section_header, followup_input_row,
followup_result_row, followup_image_row, followup_examples_header_row, followup_examples_row],
show_progress="full" # Show the built-in progress bar
)
# Set up the follow-up button click event
followup_button.click(
fn=run_followup_question,
inputs=[followup_input],
outputs=[followup_result, followup_image],
show_progress="full"
)
if __name__ == "__main__":
demo.queue() # Enable queuing for real-time updates
demo.launch(
share=True,
server_name="0.0.0.0",
server_port=7860,
show_error=True
) |