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() 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) response_content = chat_completion.choices[0].message.content executed_tools = getattr(chat_completion.choices[0].message, 'executed_tools', None) 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""" if not executed_tools: return '
🔍 Tools Used: None (Used existing knowledge)
' tool_info = '
🔍 Tools Used:
' 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 = """ """ 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
', "", "" 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 ) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") formatted_response = f"""

Query

{query}

Response

{response}

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 with gr.Blocks(title="Real-time Fact Checker & News Agent", css=custom_css) as demo: gr.HTML("""

🔍 Real-time Fact Checker

Powered by Groq's Compound Models with Real-time Web Search

""") with gr.Row(): with gr.Column(scale=2): 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.HTML( value='
⚠️ Please enter your API key
' ) validate_btn = gr.Button("Validate API Key", variant="secondary") gr.HTML('
') with gr.Group(): gr.HTML('
') gr.Markdown("### ⚙️ Advanced Options") with gr.Accordion("📝 System Prompt Examples", 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(): gr.HTML(f"""
{title}
{prompt[:100]}...
""") with gr.Accordion("🔧 System Prompt", 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") gr.Markdown("**Quick Load Custom Prompts:**") for title, prompt in custom_prompts.items(): prompt_btn = gr.Button(title, variant="secondary") prompt_btn.click( fn=lambda p=prompt: p, outputs=[system_prompt_input] ) gr.HTML('
') with gr.Group(): 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" ) temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="Temperature" ) submit_btn = gr.Button("🔍 Get Real-time Information", variant="primary") clear_btn = gr.Button("Clear", variant="secondary") gr.HTML('
') with gr.Column(scale=1): with gr.Group(): gr.HTML('
') gr.Markdown("### 📝 Example Queries") gr.Markdown("Click any example to load it:") examples = fact_checker.get_example_queries() for category, queries in examples.items(): with gr.Accordion(category, open=category == "📰 Latest News"): for query in queries: example_btn = gr.Button(query, variant="secondary") example_btn.click( fn=lambda q=query: q, outputs=[query_input] ) gr.HTML('
') gr.HTML('
') gr.Markdown("### 📊 Results") with gr.Row(): with gr.Column(scale=2): response_output = gr.HTML( value='
Your response will appear here...
' ) with gr.Column(scale=1): tool_info_output = gr.HTML( value='
Tool execution details will appear here...
' ) performance_output = gr.HTML( value="" ) gr.HTML('
') 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] ) gr.HTML(""" """) return demo if __name__ == "__main__": demo = create_interface() demo.launch( share=True )