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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")
# Change the file_uploader to accept multiple files
uploaded_files = st.file_uploader("Upload one or more images here", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
# Text input for asking questions about the images
image_question = st.text_input("Ask something about the images:")
# Check if the user has entered a question
if image_question:
# Create a list of image parts from the uploaded files
image_parts = []
for uploaded_file in uploaded_files:
image_parts.append({
"mime_type": uploaded_file.type,
"data": uploaded_file.read()
})
# Create a prompt parts list with the question and the image parts
prompt_parts = [image_question] + image_parts
# Use the gemini-pro-vision model to generate a response
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}")
# Loop through the uploaded files and display them
for uploaded_file in uploaded_files:
# Display the image
st.image(uploaded_file)
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