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app (5).py

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+# -*- 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()
+

requirements.txt

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+gradio
+transformers
+torch