app.py

import streamlit as st
import transformers
import torch
from transformers import AutoModel, AutoTokenizer


model_path = 'openbmb/MiniCPM-Llama3-V-2_5'
if 'int4' in model_path:
    if device == 'mps':
        print('Error: running int4 model with bitsandbytes on Mac is not supported right now.')
        exit()
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
else:
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.float16)
    model = model.to(device=device)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model.eval()

st.title('Ollama Chatbot')

# File uploader for the image
image = st.file_uploader('Upload an image')

# Button to submit the image for analysis
if st.button('Submit'):
    if image is not None:
        # Read the uploaded image file
        image_bytes = image.read()
        
        # Send the image to the Ollama chatbot for analysis
        response = model.chat(
            image=image,
            msgs=msgs,
            tokenizer=tokenizer,
            sampling=True,
            temperature=0.1
        )
        
        # Display the chatbot's response
        st.write(response)