app.py

import os
import gradio as gr
from groq import Groq
from datetime import datetime
import time 

class RealTimeFactChecker:

    def __init__(self):
        self.client = None
        self.model_options=["groq/compound", "groq/compound-mini"]

    def initialize_client(self, 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):
        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):

        if not self.client:
            return "❌ Please set a valid API key first.", None, None

        try: 

            start_time=time.time()

            system_prompt = 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 # response to the question
            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:\n"

        for i, tool in enumerate(executed_tools, 1):
            try:
                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"
                
                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

#### UI Design

def create_interface():
    fact_checker = RealTimeFactChecker()

    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):
        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
        )

        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*" # 11/11/2025 Query Response Time: 20s

        return formatted_response, tool_info or "", f"Response time: {response_time}s"

    with gr.Blocks(title="Real-time Fact Checker & News Agent", theme=gr.themes.Ocean()) as demo:
        gr.Markdown("# Real-time Fact Checker & News Agent")
        gr.Markdown("Powered by Groq's Compound Models with Built-in Web Search")

        with gr.Row():
            with gr.Column():
                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")

                query_input = gr.Textbox(
                    label="Query",
                    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="groq/compound",
                        label="Model",
                        info="compound-: More capable | compound-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"
                    )

                with gr.Row():
                    submit_btn = gr.Button("Get Real-time Information")
                    clear_btn = gr.Button("Clear")

            with gr.Column():

                response_output = gr.Markdown(
                    label="Response",
                    value="*Your response will appear here...*"
                )

                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
                )

        validate_btn.click(
            fn=validate_api_key,
            inputs=[api_key_input],
            outputs=[api_status, gr.State()]
        )

        submit_btn.click(
            fn=process_query,
            inputs=[query_input, model_choice, temperature, api_key_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]
        )

    
    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
    )