Update app.py

app.py

@@ -1,9 +1,9 @@
 import os
 import time
 import uuid
-import sqlite3
 from typing import List, Tuple, Optional, Dict, Union
 
+import sqlite3
 import google.generativeai as genai
 import streamlit as st
 from PIL import Image
@@ -78,13 +78,6 @@ model_name = st.sidebar.selectbox(
     index=0,
     help="Gemini Pro is a text-only model that can generate natural language responses based on the chat history. Gemini Pro Vision is a multimodal model that can generate natural language responses based on the chat history and the uploaded images."
 )
-model_info = st.sidebar.expander("Model info", expanded=False)
-with model_info:
-    st.markdown(f"""
-    - Model name: {model_name}
-    - Model size: {genai.get_model_size(model_name)}
-    - Model description: {genai.get_model_description(model_name)}
-    """)
 
 # Chat history
 st.title("Chatbot")
@@ -143,67 +136,4 @@ if clear_button:
     st.session_state["uploaded_files"] = None
     st.experimental_rerun()
 
-# Save chat history to a text file
-if download_button:
-    chat_text = "\n".join([f"{r.title()}: {t}" for r, t in chat_history])
-    st.download_button(
-        label="Download chat history",
-        data=chat_text,
-        file_name="chat_history.txt",
-        mime="text/plain"
-    )
-
-# Generate model response
-if run_button or user_input:
-    if user_input:
-        chat_history.append({"role": role, "parts": [{"text": user_input}]}) 
-        st.session_state["user_input"] = ""
-        if role == "user":
-            
-            # Model code
-            model = genai.GenerativeModel(
-                model_name=model_name,   
-                generation_config=generation_config,
-                safety_settings=safety_settings
-            )
-            
-            if uploaded_files:
-                # Preprocess the uploaded images and convert them to image_parts
-                image_parts = []
-                for uploaded_file in uploaded_files:
-                    image = Image.open(uploaded_file).convert('RGB')
-                    image_parts.append({
-                        "mime_type": uploaded_file.type,
-                        "data": uploaded_file.read() 
-                    })
-                    # Display the uploaded images
-                    st.image(image)
-
-                # Add the user input to the prompt_parts
-                prompt_parts = [
-                    user_input,
-                ] + image_parts
-
-                # Use gemini-pro-vision model to generate the response
-                response = model.generate_content(prompt_parts, stream=True) 
-            else:
-                # Use gemini-pro model to generate the response
-                response = model.generate_content(chat_history, stream=True) 
-            
-            # Streaming effect
-            chat_history.append({"role": "model", "parts": [{"text": ""}]})
-            progress_bar.progress(0)
-            for chunk in response:
-                for i in range(0, len(chunk.text), 10):
-                    section = chunk.text[i:i + 10]
-                    chat_history[-1]["parts"][0]["text"] += section
-                    progress = min((i + 10) / len(chunk.text), 1.0)
-                    progress_bar.progress(progress)
-                    time.sleep(0.01)
-                    st.experimental_rerun()
-            progress_bar.progress(1.0)
-            
-            st.session_state["chat_history"] = chat_history
-            st.session_state["uploaded_files"] = None
-            
-    st.experimental_rerun()
+# Save chat history to a text```