app.py

import streamlit as st
import google.generativeai as genai
import sqlite3 
from streamlit import file_uploader

# Database setup
conn = sqlite3.connect('chat_history.db')  
c = conn.cursor()

c.execute('''
          CREATE TABLE IF NOT EXISTS history  
          (role TEXT, message TEXT)
          ''')

# Generative AI setup
api_key = "AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM"   
genai.configure(api_key=api_key)

generation_config = {
  "temperature": 0.9,
  "max_output_tokens": 3000  
}

safety_settings = []

# Streamlit UI
st.title("Chatbot")

chat_history = st.session_state.get("chat_history", []) 

if len(chat_history) % 2 == 0:
    role = "user"
else:
    role = "model"

for message in chat_history:
    r, t = message["role"], message["parts"][0]["text"]
    st.markdown(f"**{r.title()}:** {t}")
    
# Use file_uploader for text and image input   
user_input = st.file_uploader("Upload a text or an image file here", type=["txt", "png", "jpg", "jpeg"])  
if user_input:
    # Check the file type and create the prompt parts accordingly
    if user_input.type == "text/plain":
        prompt_parts = [user_input.getvalue().decode("utf-8")]
        model_name = "gemini-pro" # Use the text-only model
    else:
        prompt_parts = [{
            "mime_type": user_input.type,
            "data": user_input.read() 
        }]
        model_name = "gemini-pro-vision" # Use the multimodal model
    
    chat_history.append({"role": role, "parts": prompt_parts}) 
    if role == "user":
        
        # Model code
        model = genai.GenerativeModel(
            model_name=model_name,   
            generation_config=generation_config,
            safety_settings=safety_settings
        )
        
        response = model.generate_content(chat_history) 
        response_text = response.text
        chat_history.append({"role": "model", "parts": [{"text": response_text}]})
        
        st.session_state["chat_history"] = chat_history
        
for message in chat_history:
    r, t = message["role"], message["parts"][0]["text"]
    st.markdown(f"**{r.title()}:** {t}")
if st.button("Display History"): 
    c.execute("SELECT * FROM history") 
    rows = c.fetchall() 

    for row in rows: 
        st.markdown(f"**{row[0].title()}:** {row[1]}") 
        
# Save chat history to database
for message in chat_history: 
    c.execute("INSERT INTO history VALUES (?, ?)", 
            (message["role"], message["parts"][0]["text"])) 
    conn.commit() 

conn.close()