import gradio as gr
from transformers import T5ForConditionalGeneration, T5Tokenizer
from textwrap import fill

# Load fine-tuned model and tokenizer
last_checkpoint = "Jyotiyadav/FLANT-5_Model_Forecasting"
finetuned_model = T5ForConditionalGeneration.from_pretrained(last_checkpoint)
tokenizer = T5Tokenizer.from_pretrained(last_checkpoint)

# Define inference function
def answer_question(question):
    # Format input
    inputs = ["Please answer this question: " + question]
    inputs = tokenizer(inputs, return_tensors="pt")
    
    # Generate answer
    outputs = finetuned_model.generate(**inputs)
    answer = tokenizer.decode(outputs[0])
    
    # Wrap answer for better display
    return fill(answer, width=80)

# Create Gradio interface
iface = gr.Interface(
    fn=answer_question,
    inputs="text",
    outputs="text",
    title="Question Answering with T5 Model",
    description="Enter your question to get the answer.",
    examples=[
        ["On 2013-02-11, at store number 1 in Quito, Pichincha, under store type D and cluster 13, with 396 transactions recorded, and crude oil price at 97.01, what was the sales quantity of BABY CARE products (ID: 73063), considering whether they were on promotion (On Promotion: 0) in Ecuador during Carnaval (Transferred: False)?"]
    ]
)

# Launch Gradio interface
iface.launch()