Update app.py

app.py

@@ -1,62 +1,82 @@
 import streamlit as st
 from phi.agent import Agent
 from phi.model.google import Gemini
+from phi.tools.duckduckgo import DuckDuckGo
+from google.generativeai import upload_file, get_file
+import time
+from pathlib import Path
 import tempfile
-import os
 
-def main():
-    # Set up the reasoning agent
-    agent = Agent(
-        model=Gemini(id="gemini-2.0-flash-thinking-exp-1219"), 
-        markdown=True
+st.set_page_config(
+    page_title="Multimodal AI Agent",
+    page_icon="🧬",
+    layout="wide"
+)
+
+st.title("Multimodal AI Agent 🧬")
+
+# Initialize single agent with both capabilities
+@st.cache_resource
+def initialize_agent():
+    return Agent(
+        name="Multimodal Analyst",
+        model=Gemini(id="gemini-2.0-flash-exp"),
+        tools=[DuckDuckGo()],
+        markdown=True,
     )
 
-    # Streamlit app title
-    st.title("Multimodal Reasoning AI Agent 🧠")
+agent = initialize_agent()
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a video file", type=['mp4', 'mov', 'avi'])
 
-    # Instruction
-    st.write(
-        "Upload an image and provide a reasoning-based task for the AI Agent. "
-        "The AI Agent will analyze the image and respond based on your input."
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file:
+        tmp_file.write(uploaded_file.read())
+        video_path = tmp_file.name
+    
+    st.video(video_path)
+    
+    user_prompt = st.text_area(
+        "What would you like to know?",
+        placeholder="Ask any question related to the video - the AI Agent will analyze it and search the web if needed",
+        help="You can ask questions about the video content and get relevant information from the web"
     )
+    
+    if st.button("Analyze & Research"):
+        if not user_prompt:
+            st.warning("Please enter your question.")
+        else:
+            try:
+                with st.spinner("Processing video and researching..."):
+                    video_file = upload_file(video_path)
+                    while video_file.state.name == "PROCESSING":
+                        time.sleep(2)
+                        video_file = get_file(video_file.name)
+
+                    prompt = f"""
+                    First analyze this video and then answer the following question using both 
+                    the video analysis and web research: {user_prompt}
+                    
+                    Provide a comprehensive response focusing on practical, actionable information.
+                    """
+                    
+                    result = agent.run(prompt, videos=[video_file])
+                    
+                st.subheader("Result")
+                st.markdown(result.content)
+
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+            finally:
+                Path(video_path).unlink(missing_ok=True)
+else:
+    st.info("Please upload a video to begin analysis.")
 
-    # File uploader for image
-    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        try:
-            # Save uploaded file to temporary file
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
-                tmp_file.write(uploaded_file.getvalue())
-                temp_path = tmp_file.name
-
-            # Display the uploaded image
-            st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
-
-            # Input for dynamic task
-            task_input = st.text_area(
-                "Enter your task/question for the AI Agent:"
-            )
-
-            # Button to process the image and task
-            if st.button("Analyze Image") and task_input:
-                with st.spinner("AI is thinking... 🤖"):
-                    try:
-                        # Call the agent with the dynamic task and image path
-                        response = agent.run(task_input, images=[temp_path])
-                        
-                        # Display the response from the model
-                        st.markdown("### AI Response:")
-                        st.markdown(response.content)
-                    except Exception as e:
-                        st.error(f"An error occurred during analysis: {str(e)}")
-                    finally:
-                        # Clean up temp file
-                        if os.path.exists(temp_path):
-                            os.unlink(temp_path)
-
-        except Exception as e:
-            st.error(f"An error occurred while processing the image: {str(e)}")
-
-if __name__ == "__main__":
-    main()
+st.markdown("""
+    <style>
+    .stTextArea textarea {
+        height: 100px;
+    }
+    </style>
+    """, unsafe_allow_html=True)