app .py

# -*- coding: utf-8 -*-
"""model.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1gCiedN3pbGAmSaO0KWH3Z2IFLcaZLwuw
"""

from huggingface_hub import hf_hub_download
import pickle
import gradio as gr

# Replace with your Hugging Face repo info
repo_id = "Sonia2k5/Number_to_words"  # e.g., "syoga/image-classifier"
filename = "Number_to_word_model.pkl"

# Download the model from the hub
model_path = hf_hub_download(repo_id=repo_id, filename=filename)

# Now `model` is ready to use
with open(model_path, "rb") as f:
    model, le = pickle.load(f)

# Get input from the user, convert to integer, and reshape to a 2D array
try:
    number_input = int(input("Enter a number: "))
    encoded = model.predict([[number_input]])
    word = le.inverse_transform(encoded)[0]
    print(word)
except ValueError:
    print("Invalid input. Please enter an integer.")

def predict_number_to_word(number):
    if not isinstance(number, (int, float)):
        return "Please enter a valid number."
    if number < 1 or number > 1000:
        return "❌ Please enter a number between 1 and 1000 only."
    encoded = model.predict([[int(number)]])
    word = le.inverse_transform(encoded)[0]
    return f"{int(number)} → {word}"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_number_to_word,
    inputs=gr.Number(label="Enter a number (1 to 1000)"),
    outputs=gr.Textbox(label="Number in Words"),
    title="🔢 Number to Word Converter",
    description="Converts a number between 1 and 1000 to its English word using a Decision Tree model."
)

iface.launch()