Update app.py

app.py

@@ -1,80 +1,72 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
+@st.cache_resource
 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 instruct model as causal language model
+
+    # 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 a question from a given passage
+# 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)
-    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return question
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Function to generate a recipe from ingredients or a title
-def generate_recipe(prompt, model, tokenizer):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1)
-    recipe = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return recipe
+# 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 an instruction-based response
+# Function to generate instructions using the instruction model
 def generate_instruction(prompt, model, tokenizer):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=100, num_return_sequences=1)
-    instruction = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return instruction
+    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")
 
-# Streamlit interface
-st.title("Multi-Model Application: Question, Recipe & Instruction Generation")
+    # Load all models
+    (question_model, question_tokenizer), (recipe_model, recipe_tokenizer), (instruct_model, instruct_tokenizer) = load_models()
 
-# Select task
-task = st.selectbox("Choose a task:", ["Generate Question", "Generate Recipe", "Instruction Generation"])
+    # Tabs for different functionalities
+    tab = st.selectbox("Choose task", ["Question Generation", "Recipe Generation", "Instruction Following"])
 
-if task == "Generate Question":
-    st.subheader("Generate a Question")
-    passage = st.text_area("Enter a passage to generate a question:")
-    if st.button("Generate Question"):
-        if passage:
+    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(f"Generated Question: {question}")
-        else:
-            st.write("Please enter a passage to generate a question.")
+            st.write("Generated Question:", question)
 
-elif task == "Generate Recipe":
-    st.subheader("Generate a Recipe")
-    recipe_prompt = st.text_area("Enter ingredients or a recipe title:")
-    if st.button("Generate Recipe"):
-        if recipe_prompt:
-            recipe = generate_recipe(recipe_prompt, recipe_model, recipe_tokenizer)
-            st.write("Generated Recipe:")
-            st.write(recipe)
-        else:
-            st.write("Please enter ingredients or a recipe title to generate a recipe.")
+    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 task == "Instruction Generation":
-    st.subheader("Generate an Instruction")
-    instruction_prompt = st.text_area("Enter an instruction prompt:")
-    if st.button("Generate Instruction"):
-        if instruction_prompt:
+    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:")
-            st.write(instruction)
-        else:
-            st.write("Please enter an instruction prompt.")
+            st.write("Generated Instruction:", instruction)
 
+if __name__ == "__main__":
+    main()