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import os
import streamlit as st
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import requests
import torch
# Step 1: Log in to Hugging Face with your access token from secrets
huggingface_token = os.getenv("HUGGINGFACE_TOKEN") # Fetch the token from environment
if huggingface_token:
login(token=huggingface_token) # Authenticate using the token
else:
st.error("Hugging Face token not found. Please set it in the Secrets section.")
# Step 2: Load the model and tokenizer
model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" # Adjust if needed
try:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
st.success("Model loaded successfully!")
except Exception as e:
st.error(f"Error loading model: {str(e)}")
# Step 3: Create a simple Streamlit app
def main():
st.title("Llama 3.2 11B Vision Model")
st.write("Upload an image and enter a prompt to generate output.")
# Upload image
image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
prompt = st.text_area("Enter your prompt here:")
if st.button("Generate Output"):
if image_file and prompt:
# Load image
image = Image.open(image_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocess the image if needed (convert to tensor, etc.)
# This depends on how the model expects the image input
# Example of converting image to a format suitable for the model
# Note: Adjust this part based on your model's requirements.
# Here, we're just using a placeholder for the model input.
# You might need to resize or normalize the image based on the model's requirements.
# For example:
# image_tensor = preprocess_image(image)
try:
# Prepare the input for the model
inputs = tokenizer(prompt, return_tensors='pt')
# Perform inference
# Adjust the input format for the model accordingly
# Here we assume the model takes a prompt and an image (adjust as necessary)
with torch.no_grad():
model_output = model.generate(**inputs) # Pass image tensor if required
# Decode the output
output_text = tokenizer.decode(model_output[0], skip_special_tokens=True)
st.write("Generated Output:", output_text)
except Exception as e:
st.error(f"Error during prediction: {str(e)}")
else:
st.warning("Please upload an image and enter a prompt.")
if __name__ == "__main__":
main() |