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 = ''
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
)