Create app.py

app.py

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+import streamlit as st
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.tools.duckduckgo import DuckDuckGo
+from dotenv import load_dotenv
+import time
+
+# Load environment variables
+load_dotenv()
+
+# Groq API Key (Replace with your actual API key)
+GROQ_API_KEY = "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"
+
+# Web agent to fetch and summarize news
+web_agent = Agent(
+    name="Web Agent",
+    model=Groq(id="llama-3.3-70b-versatile", api_key=GROQ_API_KEY),
+    tools=[DuckDuckGo()],  # Use DuckDuckGo for web searches
+    instructions=[
+        "Search for the latest AI news.",
+        "Summarize the main points clearly and concisely.",
+        "Always include sources for all information.",
+        "Format the response in Markdown for clarity."
+    ],
+    show_tool_calls=True,  # Enable logging of tool usage
+    markdown=True  # Format the output in Markdown
+)
+
+# Function to fetch the latest AI news
+def fetch_latest_ai_news():
+    # Simulating some processing delay
+    time.sleep(3)
+
+    query = "Find the latest news about Artificial Intelligence and summarize it."
+    response = web_agent.ask(query)
+    
+    raw_response = str(response)  # Simulating raw data for terminal-like output
+    processed_response = response.get('response', 'No summary available.')  # Cleaned summary
+    
+    return raw_response, processed_response  # Return raw and processed data for side-by-side display
+
+# Streamlit App Interface
+def create_streamlit_app():
+    st.title("AI News Fetcher")  # Title of the app
+    st.markdown("This app fetches and summarizes the latest AI news using AI models.")
+
+    # Add a button for triggering the process
+    if st.button("Generate AI News"):
+        with st.spinner("Fetching AI news..."):
+            # Get the raw and processed AI news
+            raw_response, processed_response = fetch_latest_ai_news()
+
+            # Display raw response (Terminal-like view)
+            st.subheader("Raw Data")
+            st.text_area("Raw Data", value=raw_response, height=300)
+
+            # Display processed (summarized) response
+            st.subheader("Processed Data")
+            st.markdown(processed_response)
+
+# Running the Streamlit app
+if __name__ == "__main__":
+    create_streamlit_app()