Create app.py

app.py

@@ -0,0 +1,246 @@
+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=["compound", "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="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(
+    )