Update app.py

app.py

@@ -1,6 +1,9 @@
 import streamlit as st
 from openai import OpenAI
 import os
+import base64
+import io
+
 
 # Set your API key and base URL
 API_KEY = os.getenv("API_KEY")
@@ -13,15 +16,13 @@ client = OpenAI(
 )
 
 # App Title and Sidebar Configuration
-st.title("Deepseek V3 Advance Reasoning")
+st.title("Advance Reasoning!!!")
 
 # Sidebar: Model Selection and Advanced Settings
 st.sidebar.header("Configuration")
 model_options = [
     "deepseek/deepseek-v3-base:free",
-    "gpt-3.5-turbo",
-    "gpt-4",
-    # Add more models as needed
+    "google/gemini-2.5-pro-exp-03-25:free",  # Gemini model with image support
 ]
 selected_model = st.sidebar.selectbox("Select Model", model_options)
 
@@ -43,12 +44,42 @@ for msg in st.session_state.messages:
     else:
         st.chat_message("user").write(msg["content"])
 
-# Input for New Message
-user_input = st.text_input("Type your message here...", key="input")
+# Input for New Message (text input)
+user_text = st.text_input("Type your message here...", key="input")
+
+# If the Gemini model is selected, show an image uploader option
+uploaded_image = None
+if selected_model == "google/gemini-2.5-pro-exp-03-25:free":
+    uploaded_image = st.file_uploader("Upload an image (optional)", type=["png", "jpg", "jpeg"])
 
-if st.button("Send") and user_input:
+# When the user clicks "Send", process the input
+if st.button("Send") and (user_text or uploaded_image):
     # Append the user's message to the conversation history
-    st.session_state.messages.append({"role": "user", "content": user_input})
+    # For Gemini, if an image is uploaded, create a message with both text and image
+    if selected_model == "google/gemini-2.5-pro-exp-03-25:free" and uploaded_image is not None:
+        # Read image bytes and encode as a base64 data URL
+        image_bytes = uploaded_image.read()
+        encoded_image = base64.b64encode(image_bytes).decode('utf-8')
+        # Attempt to determine image type from the file name extension
+        ext = uploaded_image.name.split('.')[-1]
+        data_url = f"data:image/{ext};base64,{encoded_image}"
+        
+        # Construct a combined message payload as a list of objects
+        user_message = {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": user_text},
+                {"type": "image_url", "image_url": {"url": data_url}}
+            ]
+        }
+    else:
+        # Regular text message payload
+        user_message = {
+            "role": "user",
+            "content": user_text
+        }
+    
+    st.session_state.messages.append(user_message)
     
     # Use a spinner while waiting for the API response
     with st.spinner("Waiting for response..."):
@@ -62,6 +93,7 @@ if st.button("Send") and user_input:
                 model=selected_model,
                 messages=st.session_state.messages,
             )
+            # Assuming the API returns a similar structure for assistant messages
             assistant_reply = response.choices[0].message.content
         except Exception as e:
             assistant_reply = f"An error occurred: {str(e)}"
@@ -69,5 +101,5 @@ if st.button("Send") and user_input:
     # Append the assistant's reply to the conversation history
     st.session_state.messages.append({"role": "assistant", "content": assistant_reply})
     
-    # Rerun the app to update the chat window with new messages
+    # Refresh the app to update the chat window with new messages
     st.rerun()