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 text_area for multiline input   
user_input = st.text_area("", height=5)  
if user_input:
    chat_history.append({"role": role, "parts": [{"text": user_input}]}) 
    if role == "user":
        
        # Model code
        model_name = "gemini-pro"
        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()

# Separate section for image uploading
st.title("Image Description Generator")

uploaded_file = st.file_uploader("Upload an image here", type=["png", "jpg", "jpeg"])

# Text input for asking questions about the image
image_question = st.text_input("Ask something about the image:")

if uploaded_file and image_question:
   image_parts = [
     {
       "mime_type": uploaded_file.type,
       "data": uploaded_file.read() 
     },
   ]

   prompt_parts = [
     image_question,
     image_parts[0],
   ]

   model = genai.GenerativeModel(
     model_name="gemini-pro-vision", 
     generation_config=generation_config, 
     safety_settings=safety_settings 
   )
   
   response = model.generate_content(prompt_parts)
   st.markdown(f"**Model's answer:** {response.text}")