app.py

import streamlit as st
from PIL import Image
import io
import base64

import google.generativeai as genai

# Configure the API key (should be set as an environment variable or secure storage in production)
api_key = "AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM"  # Replace with your actual API key
genai.configure(api_key=api_key)

generation_config = genai.GenerationConfig(
    temperature=0.9,
    max_output_tokens=3000
)

safety_settings = []

# Initialize session state for chat history
if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

# UI layout
st.title("Gemini Chatbot")

# Display the chat history
for message in st.session_state['chat_history']:
    role, text = message
    st.markdown(f"**{role.title()}**: {text}")

# Multiline text input for the user to send messages
user_input = st.text_area("Enter your message here:", key="user_input")

# File uploader for images
uploaded_files = st.file_uploader(
    "Upload images:",
    type=["png", "jpg", "jpeg"],
    accept_multiple_files=True,
    key="file_uploader"
)

# Function to convert image to base64
def get_image_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return img_str

# When the 'Send' button is clicked, process the input and generate a response
if st.button("Send", key="send_button"):
    if user_input or uploaded_files:
        # Save user input to the chat history
        if user_input:
            st.session_state['chat_history'].append(("User", user_input))

        if uploaded_files:
            images_prompts = []
            for uploaded_file in uploaded_files:
                bytes_data = uploaded_file.read()
                image = Image.open(io.BytesIO(bytes_data))
                image_base64 = get_image_base64(image)
                st.session_state['chat_history'].append(("User", f"Uploaded image: {uploaded_file.name}"))
                image_prompt = {"role": "user", "parts": [{"mime_type": uploaded_file.type, "data": image_base64}]}
                images_prompts.append(image_prompt)
            
            # Use the Gemini model for vision
            model_name = 'gemini-pro-vision'
            model = genai.GenerativeModel(
                model_name=model_name,
                generation_config=generation_config,
                safety_settings=safety_settings
            )
            response = model.generate_content(images_prompts)
            response_text = response.text if hasattr(response, "text") else "No response text found."
            st.session_state['chat_history'].append(("Gemini", response_text))
        elif user_input:
            # Use the Gemini model for text
            model_name = 'gemini-pro'
            model = genai.GenerativeModel(
                model_name=model_name,
                generation_config=generation_config,
                safety_settings=safety_settings
            )
            prompts = [{"role": "user", "parts": [{"text": user_input}]}]
            response = model.generate_content(prompts)
            response_text = response.text if hasattr(response, "text") else "No response text found."
            st.session_state['chat_history'].append(("Gemini", response_text))

    # Clear the input box after sending the message
    st.session_state['user_input'] = ""

# Re-display the chat history to include the new messages
for message in st.session_state['chat_history']:
    role, text = message
    st.markdown(f"**{role.title()}**: {text}")