app.py

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

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1WlcluswqcXF3-jlf1RYLOiRIVzgR1xge
"""

import numpy as np
import gradio as gr
from keras.models import load_model

# Load trained model
model = load_model("pp.h5")

# Characters used during training (exact set used in your poem.py)
chars = sorted(list(set("roses are red,\nviolets are blue,\nsugar is sweet,\nand so are you.")))
char_to_idx = {c: i for i, c in enumerate(chars)}
idx_to_char = {i: c for i, c in enumerate(chars)}
vocab_size = len(chars)
seq_length = 40  # Same as used in training

def clean_seed(seed):
    """Remove unsupported characters from the seed"""
    return ''.join([c for c in seed.lower() if c in char_to_idx])

def generate_text(seed_text, length=200):
    seed_text = clean_seed(seed_text)

    # Pad if too short
    if len(seed_text) < seq_length:
        seed_text = seed_text.rjust(seq_length)

    generated = seed_text[:seq_length]

    for _ in range(length):
        input_seq = [char_to_idx.get(c, 0) for c in generated[-seq_length:]]
        input_seq = np.array(input_seq).reshape(1, seq_length)

        preds = model.predict(input_seq, verbose=0)[0]
        next_index = np.argmax(preds)
        next_char = idx_to_char[next_index]

        generated += next_char

    return generated

# Gradio UI
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Enter seed text", placeholder="roses are red"),
        gr.Slider(minimum=50, maximum=500, value=200, step=10, label="Length of generated text")
    ],
    outputs=gr.Textbox(label="Generated Output"),
    title="Poem Generator",
    description="Generate poem-style text based on a seed input. Only characters from your training data are used."
)

if __name__ == "__main__":
    iface.launch()