app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification

def predict_rooms(new_students, new_temperature):
    # Load the model and tokenizer
    model_name = "AI"  # Replace with the name or path of the model you want to use
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)

    # Convert the input to tokens
    inputs = tokenizer.encode_plus(
        "Number of students: {}, Temperature: {}".format(new_students, new_temperature),
        padding="max_length",
        truncation=True,
        max_length=64,
        return_tensors="pt"
    )

    # Make the prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_rooms = torch.argmax(logits, dim=1).item()

    return predicted_rooms

def greet(name):
    return "Hello " + name + "!"

iface = gr.Interface(
    fn=[predict_rooms, greet],
    inputs=[["number", "number"], "text"],
    outputs=["number", "text"],
    title="Room Prediction",
    description="Predict the number of rooms based on the number of students and temperature, and greet the user."
)

iface.launch()