import os
# from dotenv import load_dotenv
import streamlit as st
import PIL.Image
import google.generativeai as genai
from langchain.prompts import ChatPromptTemplate
from langchain_community.llms import Ollama
from transformers import MllamaForConditionalGeneration, AutoProcessor
import torch
from accelerate import init_empty_weights
# Load environment variables
from transformers import AutoProcessor, AutoModelForPreTraining
from transformers import MllamaForConditionalGeneration, AutoProcessor



prompt="<|image|><|begin_of_text|>You are a helpful assistant. Please respond to the user's queries."



model_id = "meta-llama/Llama-3.2-11B-Vision"
model = MllamaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_id)

# Define function to get response from the model
def get_gemin_response(input_text, img):
    # complete_prompt = prompt.format(question=input_text)
    inputs = processor(images=img, text=prompt, return_tensors="pt").to(model.device)
    response=model.generate(**inputs, max_new_tokens=30)

    # if input_text != "":
    #     # Only generate content from input text if present
    #     response = model.generate([input_text])
    # else:
    #     response = model.generate([img_text])
    return response

# Define the main function for the Streamlit app
def main():
    st.set_page_config(page_title='Gemini Image & Text')
    st.header('Gemini LLM Application')

    # Input text
    input_text = st.text_input("Input :", key='input')

    # Image uploader
    imgupload = st.file_uploader('Choose an image file', type=['jpg', 'jpeg', 'png'])

    # Display uploaded image and convert to text format (if needed)
    img_text = ""
    if imgupload is not None:
        img = PIL.Image.open(imgupload)
        st.image(img, caption='Uploaded Image', use_column_width=True)
        img_text = "Image uploaded successfully."
    if st.button('Generate Response'):
        # Ensure both inputs are provided
        if img is not None and input_text:  
            # Get response from the model
            response = get_gemin_response(input_text, img)
            st.write(processor.decode(response[0]))
        else:
            st.error("Please provide both input text and an image before generating a response.")


# Run the app
if __name__ == "__main__":
    main()