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| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM | |
| import streamlit as st | |
| def load_models(): | |
| question_model_name = "mrm8488/t5-base-finetuned-question-generation-ap" | |
| recipe_model_name = "flax-community/t5-recipe-generation" | |
| instruct_model_name = "norallm/normistral-7b-warm-instruct" | |
| # Load T5-based models for question generation and recipe generation | |
| question_model = AutoModelForSeq2SeqLM.from_pretrained(question_model_name) | |
| question_tokenizer = AutoTokenizer.from_pretrained(question_model_name) | |
| recipe_model = AutoModelForSeq2SeqLM.from_pretrained(recipe_model_name) | |
| recipe_tokenizer = AutoTokenizer.from_pretrained(recipe_model_name) | |
| # Load the instruction model as a causal language model | |
| instruct_model = AutoModelForCausalLM.from_pretrained(instruct_model_name) | |
| instruct_tokenizer = AutoTokenizer.from_pretrained(instruct_model_name) | |
| return (question_model, question_tokenizer), (recipe_model, recipe_tokenizer), (instruct_model, instruct_tokenizer) | |
| # Function to generate questions using the question model | |
| def generate_question(text, model, tokenizer): | |
| input_text = f"generate question: {text}" | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| outputs = model.generate(input_ids) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Function to generate recipes using the recipe model | |
| def generate_recipe(ingredients, model, tokenizer): | |
| input_text = f"generate recipe: {ingredients}" | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| outputs = model.generate(input_ids) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Function to generate instructions using the instruction model | |
| def generate_instruction(prompt, model, tokenizer): | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| outputs = model.generate(input_ids) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Streamlit Interface | |
| def main(): | |
| st.title("Multi-Model Application") | |
| # Load all models | |
| (question_model, question_tokenizer), (recipe_model, recipe_tokenizer), (instruct_model, instruct_tokenizer) = load_models() | |
| # Tabs for different functionalities | |
| tab = st.selectbox("Choose task", ["Question Generation", "Recipe Generation", "Instruction Following"]) | |
| if tab == "Question Generation": | |
| passage = st.text_area("Enter a passage for question generation") | |
| if st.button("Generate Question"): | |
| question = generate_question(passage, question_model, question_tokenizer) | |
| st.write("Generated Question:", question) | |
| elif tab == "Recipe Generation": | |
| ingredients = st.text_area("Enter ingredients for recipe generation") | |
| if st.button("Generate Recipe"): | |
| recipe = generate_recipe(ingredients, recipe_model, recipe_tokenizer) | |
| st.write("Generated Recipe:", recipe) | |
| elif tab == "Instruction Following": | |
| instruction_prompt = st.text_area("Enter an instruction prompt") | |
| if st.button("Generate Instruction"): | |
| instruction = generate_instruction(instruction_prompt, instruct_model, instruct_tokenizer) | |
| st.write("Generated Instruction:", instruction) | |
| if __name__ == "__main__": | |
| main() | |