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Update app.py
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import os
from dotenv import load_dotenv
import gradio as gr
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.messages import HumanMessage
# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
# Initialize the HuggingFace inference endpoint
llm = HuggingFaceEndpoint(
repo_id="flax-community/t5-recipe-generation",
huggingfacehub_api_token=HF_TOKEN.strip(),
temperature=0.7,
max_new_tokens=500
)
# Recipe generation function
def suggest_recipes(ingredients):
# Create a prompt for the recipe generation
prompt = (
f"You are an expert in cooking. Please suggest 3 recipes using the following "
f"ingredients: {ingredients}. Provide a title for each recipe, include "
f"preparation time, and list step-by-step directions."
)
# Wrap the prompt in a HumanMessage object
input_message = HumanMessage(content=prompt)
# Use the HuggingFaceEndpoint model to generate a response
response = llm(input_message)
return response.content
# Gradio interface
with gr.Blocks() as app:
gr.Markdown("# Recipe Suggestion App")
gr.Markdown("Provide the ingredients you have, and this app will suggest recipes along with preparation times!")
with gr.Row():
ingredients_input = gr.Textbox(label="Enter Ingredients (comma-separated):", placeholder="e.g., eggs, milk, flour")
recipe_output = gr.Textbox(label="Suggested Recipes:", lines=15, interactive=False)
generate_button = gr.Button("Get Recipes")
generate_button.click(suggest_recipes, inputs=ingredients_input, outputs=recipe_output)
# Launch the app
app.launch()