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import gradio as gr
# Load model directly
from transformers import AutoModel, AutoTokenizer
# Load the LoRA model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ID2223JR/lora_model")
model = AutoModel.from_pretrained("ID2223JR/lora_model")
# Data storage
ingredients_list = []
# Function to add ingredient
def add_ingredient(ingredient, quantity):
if ingredient and quantity > 0:
ingredients_list.append(f"{ingredient}, {quantity} grams")
return (
"\n".join(ingredients_list),
gr.update(value="", interactive=True),
gr.update(value=None, interactive=True),
)
# Function to enable/disable add button
def validate_inputs(ingredient, quantity):
if ingredient and quantity > 0:
return gr.update(interactive=True)
return gr.update(interactive=False)
# Function to handle model submission
def submit_to_model():
if not ingredients_list:
return "Ingredients list is empty! Please add ingredients first."
# Join ingredients into a single prompt
prompt = f"Using the following ingredients, suggest a recipe:\n\n" + "\n".join(
ingredients_list
)
# Tokenize and pass the prompt to the model
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
# Decode the model output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# App
def app():
with gr.Blocks() as demo:
with gr.Row():
ingredient_input = gr.Textbox(
label="Ingredient", placeholder="Enter ingredient name"
)
quantity_input = gr.Number(label="Quantity (grams)", value=None)
add_button = gr.Button("Add Ingredient", interactive=False)
output = gr.Textbox(label="Ingredients List", lines=10, interactive=False)
with gr.Row():
submit_button = gr.Button("Submit")
model_output = gr.Textbox(
label="Recipe Suggestion", lines=10, interactive=False
)
# Validate inputs
ingredient_input.change(
validate_inputs, [ingredient_input, quantity_input], add_button
)
quantity_input.change(
validate_inputs, [ingredient_input, quantity_input], add_button
)
# Add ingredient logic
add_button.click(
add_ingredient,
[ingredient_input, quantity_input],
[output, ingredient_input, quantity_input],
)
# Submit to model logic
submit_button.click(
submit_to_model,
inputs=None, # No inputs required as it uses the global ingredients_list
outputs=model_output,
)
return demo
demo = app()
demo.launch()
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