import streamlit as st from transformers import pipeline from PIL import Image from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto") generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) text=generate_text("Explain to me the difference between nuclear fission and fusion.") st.title(text) file_name = st.file_uploader("Upload a hot dog candidate image") if file_name is not None: col1, col2 = st.columns(2) image = Image.open(file_name) col1.image(image, use_column_width=True) predictions = pipeline(image) col2.header(text) for p in predictions: col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")