Update app.py

app.py

@@ -1,66 +1,62 @@
-import os
 import streamlit as st
-import google.generativeai as genai
-from PIL import Image
-
-# Set up the Streamlit App
-st.set_page_config(page_title="Multimodal Chatbot with Gemini Flash", layout="wide")
-st.title("Multimodal Chatbot with Gemini Flash ⚡️")
-st.caption("Chat with Google's Gemini Flash model using image and text input to get lightning fast results. 🌟")
-
-# Get OpenAI API key from user
-api_key =  "AIzaSyC_zxN9IHjEAxIoshWPzMfgb9qwMsu5t5Y"
-# Set up the Gemini model
-genai.configure(api_key=api_key)
-model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest")
-
-if api_key:
-    # Initialize the chat history
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-
-    # Sidebar for image upload
-    with st.sidebar:
-        st.title("Chat with Images")
-        uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
-    
-    if uploaded_file:
-        image = Image.open(uploaded_file)
-        st.image(image, caption='Uploaded Image', use_column_width=True)
-
-    # Main layout
-    chat_placeholder = st.container()
-
-    with chat_placeholder:
-        # Display the chat history
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
-
-    # User input area at the bottom
-    prompt = st.chat_input("What do you want to know?")
-
-    if prompt:
-        inputs = [prompt]
-        
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        # Display user message in chat message container
-        with chat_placeholder:
-            with st.chat_message("user"):
-                st.markdown(prompt)
-        
-        if uploaded_file:
-            inputs.append(image)
-
-        with st.spinner('Generating response...'):
-            # Generate response
-            response = model.generate_content(inputs)
-    
-        # Display assistant response in chat message container
-        with chat_placeholder:
-            with st.chat_message("assistant"):
-                st.markdown(response.text)
+from phi.agent import Agent
+from phi.model.google import Gemini
+import tempfile
+import os
 
-    if uploaded_file and not prompt:
-        st.warning("Please enter a text query to accompany the image.")
+def main():
+    # Set up the reasoning agent
+    agent = Agent(
+        model=Gemini(id="gemini-2.0-flash-thinking-exp-1219"), 
+        markdown=True
+    )
+
+    # Streamlit app title
+    st.title("Multimodal Reasoning AI Agent 🧠")
+
+    # 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."
+    )
+
+    # 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()