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import numpy as np
import gradio as gr
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
# Config
WINDOW_SIZE = 3
SEQUENCE_LIMIT = 1000
# Prepare dataset from 1 to 1000
seq = np.array(range(1, SEQUENCE_LIMIT + 1))
x, y = [], []
for i in range(len(seq) - WINDOW_SIZE):
x.append(seq[i:i + WINDOW_SIZE])
y.append(seq[i + WINDOW_SIZE])
x = np.array(x).reshape(-1, WINDOW_SIZE, 1)
y = np.array(y)
# Build and train the RNN model
model = Sequential([
LSTM(64, input_shape=(WINDOW_SIZE, 1)),
Dense(1)
])
model.compile(optimizer="adam", loss="mse")
model.fit(x, y, epochs=1000, batch_size=512, verbose=0) # use 1 epoch for faster Hugging Face init
# Prediction function
def predict_next(n1, n2, n3):
try:
# Input validation
for val in [n1, n2, n3]:
if not (1 <= val <= SEQUENCE_LIMIT):
return f"Input numbers must be between 1 and {SEQUENCE_LIMIT}"
input_seq = np.array([n1, n2, n3]).reshape(1, 3, 1)
prediction = model.predict(input_seq, verbose=0)[0][0]
return f"Predicted next number: {prediction:.2f}"
except Exception as e:
return f"Error: {str(e)}"
# Gradio interface
demo = gr.Interface(
fn=predict_next,
inputs=[
gr.Number(label="1st Number (≤ 1000)"),
gr.Number(label="2nd Number (≤ 1000)"),
gr.Number(label="3rd Number (≤ 1000)")
],
outputs="text",
title="RNN Number Sequence Predictor",
description="Enter any 3 increasing numbers (up to 1000). Model predicts the next number.",
)
demo.launch()
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