import os
import gradio as gr
from groq import Groq
import json
from datetime import datetime
import time
class RealTimeFactChecker:
def __init__(self):
self.client = None
self.model_options = ["compound-beta", "compound-beta-mini"]
def initialize_client(self, api_key):
"""Initialize Groq client with API key"""
try:
self.client = Groq(api_key=api_key)
return True, "✅ API Key validated successfully!"
except Exception as e:
return False, f"❌ Error initializing client: {str(e)}"
def get_system_prompt(self):
"""Get the system prompt for consistent behavior"""
return """You are a Real-time Fact Checker and News Agent. Your primary role is to provide accurate, up-to-date information by leveraging web search when needed.
CORE RESPONSIBILITIES:
1. **Fact Verification**: Always verify claims with current, reliable sources
2. **Real-time Information**: Use web search for any information that changes frequently (news, stocks, weather, current events)
3. **Source Transparency**: When using web search, mention the sources or indicate that you've searched for current information
4. **Accuracy First**: If information is uncertain or conflicting, acknowledge this clearly
RESPONSE GUIDELINES:
- **Structure**: Start with a clear, direct answer, then provide supporting details
- **Recency**: Always prioritize the most recent, reliable information
- **Clarity**: Use clear, professional language while remaining accessible
- **Completeness**: Provide comprehensive answers but stay focused on the query
- **Source Awareness**: When you've searched for information, briefly indicate this (e.g., "Based on current reports..." or "Recent data shows...")
WHEN TO SEARCH:
- Breaking news or current events
- Stock prices, market data, or financial information
- Weather conditions or forecasts
- Recent scientific discoveries or research
- Current political developments
- Real-time statistics or data
- Verification of recent claims or rumors
RESPONSE FORMAT:
- Lead with key facts
- Include relevant context
- Mention timeframe when relevant (e.g., "as of today", "this week")
- If multiple sources conflict, acknowledge this
- End with a clear summary for complex topics
Remember: Your goal is to be the most reliable, up-to-date source of information possible."""
def query_compound_model(self, query, model, temperature=0.7, custom_system_prompt=None):
"""Query the compound model and return response with tool execution info"""
if not self.client:
return "❌ Please set a valid API key first.", None, None
try:
start_time = time.time()
# Use custom system prompt if provided
system_prompt = custom_system_prompt if custom_system_prompt else self.get_system_prompt()
chat_completion = self.client.chat.completions.create(
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": query,
}
],
model=model,
temperature=temperature,
max_tokens=1500
)
end_time = time.time()
response_time = round(end_time - start_time, 2)
# Extract response
response_content = chat_completion.choices[0].message.content
# Check for executed tools - Fixed the error here
executed_tools = getattr(chat_completion.choices[0].message, 'executed_tools', None)
# Format tool execution info
tool_info = self.format_tool_info(executed_tools)
return response_content, tool_info, response_time
except Exception as e:
return f"❌ Error querying model: {str(e)}", None, None
def format_tool_info(self, executed_tools):
"""Format executed tools information for display - FIXED"""
if not executed_tools:
return "🔍 **Tools Used:** None (Used existing knowledge)"
tool_info = "🔍 **Tools Used:**\n"
for i, tool in enumerate(executed_tools, 1):
try:
# Handle different tool object types
if hasattr(tool, 'name'):
tool_name = tool.name
elif hasattr(tool, 'tool_name'):
tool_name = tool.tool_name
elif isinstance(tool, dict):
tool_name = tool.get('name', 'Unknown')
else:
tool_name = str(tool)
tool_info += f"{i}. **{tool_name}**\n"
# Add tool parameters if available
if hasattr(tool, 'parameters'):
params = tool.parameters
if isinstance(params, dict):
for key, value in params.items():
tool_info += f" - {key}: {value}\n"
elif hasattr(tool, 'input'):
tool_info += f" - Input: {tool.input}\n"
except Exception as e:
tool_info += f"{i}. **Tool {i}** (Error parsing details)\n"
return tool_info
def get_example_queries(self):
"""Return categorized example queries"""
return {
"📰 Latest News": [
"What are the top 3 news stories today?",
"Latest developments in AI technology this week",
"Recent political events in the United States",
"Breaking news about climate change",
"What happened in the stock market today?"
],
"💰 Financial Data": [
"Current price of Bitcoin",
"Tesla stock price today",
"How is the S&P 500 performing today?",
"Latest cryptocurrency market trends",
"What's the current inflation rate?"
],
"🌤️ Weather Updates": [
"Current weather in New York City",
"Weather forecast for London this week",
"Is it going to rain in San Francisco today?",
"Temperature in Tokyo right now",
"Weather conditions in Sydney"
],
"🔬 Science & Technology": [
"Latest breakthroughs in fusion energy",
"Recent discoveries in space exploration",
"New developments in quantum computing",
"Latest medical research findings",
"Recent advances in renewable energy"
],
"🏆 Sports & Entertainment": [
"Latest football match results",
"Who won the recent tennis tournament?",
"Box office numbers for this weekend",
"Latest movie releases this month",
"Recent celebrity news"
],
"🔍 Fact Checking": [
"Is it true that the Earth's population reached 8 billion?",
"Verify: Did company X announce layoffs recently?",
"Check if the recent earthquake in Turkey was magnitude 7+",
"Confirm the latest unemployment rate statistics",
"Verify recent claims about electric vehicle sales"
]
}
def get_custom_prompt_examples(self):
"""Return custom system prompt examples"""
return {
"🎯 Fact-Checker": "You are a fact-checker. Always verify claims with multiple sources and clearly indicate confidence levels in your assessments. Use phrases like 'highly confident', 'moderately confident', or 'requires verification' when presenting information.",
"📊 News Analyst": "You are a news analyst. Focus on providing balanced, unbiased reporting with multiple perspectives on current events. Always present different viewpoints and avoid partisan language.",
"💼 Financial Advisor": "You are a financial advisor. Provide accurate market data with context about trends and implications for investors. Always include disclaimers about market risks and the importance of professional financial advice.",
"🔬 Research Assistant": "You are a research assistant specializing in scientific and technical information. Provide detailed, evidence-based responses with proper context about methodology and limitations of studies.",
"🌍 Global News Correspondent": "You are a global news correspondent. Focus on international events and their interconnections. Provide cultural context and explain how events in one region might affect others.",
"📈 Market Analyst": "You are a market analyst. Provide detailed financial analysis including technical indicators, market sentiment, and economic factors affecting price movements."
}
def create_interface():
fact_checker = RealTimeFactChecker()
# Custom CSS for beautiful styling
custom_css = """
"""
def validate_api_key(api_key):
if not api_key or api_key.strip() == "":
return "❌ Please enter a valid API key", False
success, message = fact_checker.initialize_client(api_key.strip())
return message, success
def process_query(query, model, temperature, api_key, system_prompt):
if not api_key or api_key.strip() == "":
return "❌ Please set your API key first", "", ""
if not query or query.strip() == "":
return "❌ Please enter a query", "", ""
# Initialize client if not already done
if not fact_checker.client:
success, message = fact_checker.initialize_client(api_key.strip())
if not success:
return message, "", ""
response, tool_info, response_time = fact_checker.query_compound_model(
query.strip(), model, temperature, system_prompt.strip() if system_prompt else None
)
# Format response with timestamp
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
formatted_response = f"**Query:** {query}\n\n**Response:**\n{response}\n\n---\n*Generated at {timestamp} in {response_time}s*"
return formatted_response, tool_info or "", f"⚡ Response time: {response_time}s"
def reset_system_prompt():
return fact_checker.get_system_prompt()
def load_example(example_text):
return example_text
def load_custom_prompt(prompt_text):
return prompt_text
# Create the Gradio interface
with gr.Blocks(title="Real-time Fact Checker & News Agent", css=custom_css) as demo:
# Header
gr.HTML("""
🔍 Real-time Fact Checker & News Agent
Powered by Groq's Compound Models with Built-in Web Search
""")
with gr.Row():
with gr.Column(scale=2):
# API Key section
with gr.Group():
gr.HTML('')
gr.Markdown("### 🔑 API Configuration")
api_key_input = gr.Textbox(
label="Groq API Key",
placeholder="Enter your Groq API key here...",
type="password",
info="Get your free API key from https://console.groq.com/"
)
api_status = gr.Textbox(
label="Status",
value="⚠️ Please enter your API key",
interactive=False
)
validate_btn = gr.Button("Validate API Key", variant="secondary")
gr.HTML('
')
# Advanced options
with gr.Group():
gr.HTML('')
gr.Markdown("### ⚙️ Advanced Options")
# Custom System Prompt Examples
with gr.Accordion("📝 System Prompt Examples (Click to view)", open=False):
gr.Markdown("**Click any example to load it as your system prompt:**")
custom_prompts = fact_checker.get_custom_prompt_examples()
for title, prompt in custom_prompts.items():
with gr.Row():
gr.HTML(f"""
{title}
{prompt[:100]}...
""")
with gr.Accordion("🔧 System Prompt (Click to customize)", open=False):
system_prompt_input = gr.Textbox(
label="System Prompt",
value=fact_checker.get_system_prompt(),
lines=8,
info="Customize how the AI behaves and responds",
elem_id="system_prompt_input"
)
reset_prompt_btn = gr.Button("Reset to Default", variant="secondary", size="sm")
# Add buttons for each custom prompt
gr.Markdown("**Quick Load Custom Prompts:**")
custom_prompts = fact_checker.get_custom_prompt_examples()
for title, prompt in custom_prompts.items():
prompt_btn = gr.Button(title, variant="secondary", size="sm")
prompt_btn.click(
fn=lambda p=prompt: p,
outputs=[system_prompt_input]
)
gr.HTML('
')
gr.Markdown("### 💭 Your Query")
query_input = gr.Textbox(
label="Ask anything that requires real-time information",
placeholder="e.g., What are the latest AI developments today?",
lines=4
)
with gr.Row():
model_choice = gr.Dropdown(
choices=fact_checker.model_options,
value="compound-beta",
label="Model",
info="compound-beta: More capable | compound-beta-mini: Faster"
)
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.7,
step=0.1,
label="Temperature",
info="Higher = more creative, Lower = more focused"
)
submit_btn = gr.Button("🔍 Get Real-time Information", variant="primary", size="lg")
clear_btn = gr.Button("Clear", variant="secondary")
gr.HTML('
')
with gr.Column(scale=1):
# Example queries with tabs
with gr.Group():
gr.HTML('')
gr.Markdown("### 📝 Example Queries")
gr.Markdown("Click any example to load it:")
examples = fact_checker.get_example_queries()
with gr.Accordion("📰 Latest News", open=True):
for query in examples["📰 Latest News"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
with gr.Accordion("💰 Financial Data", open=False):
for query in examples["💰 Financial Data"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
with gr.Accordion("🌤️ Weather Updates", open=False):
for query in examples["🌤️ Weather Updates"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
with gr.Accordion("🔬 Science & Technology", open=False):
for query in examples["🔬 Science & Technology"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
with gr.Accordion("🏆 Sports & Entertainment", open=False):
for query in examples["🏆 Sports & Entertainment"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
with gr.Accordion("🔍 Fact Checking", open=False):
for query in examples["🔍 Fact Checking"]:
example_btn = gr.Button(query, variant="secondary", size="sm")
example_btn.click(
fn=lambda q=query: q,
outputs=[query_input]
)
gr.HTML('
')
# Results section
gr.HTML('')
gr.Markdown("### 📊 Results")
with gr.Row():
with gr.Column(scale=2):
response_output = gr.Markdown(
label="Response",
value="*Your response will appear here...*"
)
with gr.Column(scale=1):
tool_info_output = gr.Markdown(
label="Tool Execution Info",
value="*Tool execution details will appear here...*"
)
performance_output = gr.Textbox(
label="Performance",
value="",
interactive=False
)
gr.HTML('
')
# Event handlers
validate_btn.click(
fn=validate_api_key,
inputs=[api_key_input],
outputs=[api_status, gr.State()]
)
reset_prompt_btn.click(
fn=reset_system_prompt,
outputs=[system_prompt_input]
)
submit_btn.click(
fn=process_query,
inputs=[query_input, model_choice, temperature, api_key_input, system_prompt_input],
outputs=[response_output, tool_info_output, performance_output]
)
clear_btn.click(
fn=lambda: ("", "*Your response will appear here...*", "*Tool execution details will appear here...*", ""),
outputs=[query_input, response_output, tool_info_output, performance_output]
)
# Footer
gr.HTML("""
""")
return demo
# Launch the application
if __name__ == "__main__":
demo = create_interface()
demo.launch(
share=True
)