Create app.py

app.py

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+import numpy as np
+import gradio as gr
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import SimpleRNN, Dense
+
+# 📌 Simulate an RNN model on-the-fly for demo (NOT from HF)
+def create_dummy_rnn():
+    model = Sequential()
+    model.add(SimpleRNN(10, activation='relu', input_shape=(3, 1)))
+    model.add(Dense(1))
+    model.compile(optimizer='adam', loss='mse')
+    
+    # Train on dummy increasing patterns
+    X = []
+    y = []
+    for i in range(1, 100):
+        X.append([i, i+1, i+2])
+        y.append(i+3)
+    X = np.array(X).reshape((len(X), 3, 1))
+    y = np.array(y)
+    model.fit(X, y, epochs=20, verbose=0)
+    return model
+
+# Load dummy model (simulate download)
+model = create_dummy_rnn()
+
+def predict_next_number(a, b, c):
+    try:
+        x = np.array([float(a), float(b), float(c)]).reshape((1, 3, 1))
+        prediction = model.predict(x, verbose=0)[0][0]
+        return f"🔮 Predicted Next Number: {prediction:.2f}"
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
+
+# Gradio Interface
+inputs = [
+    gr.Number(label="First Number"),
+    gr.Number(label="Second Number"),
+    gr.Number(label="Third Number"),
+]
+
+outputs = gr.Textbox(label="Predicted Next Number")
+
+app = gr.Interface(
+    fn=predict_next_number,
+    inputs=inputs,
+    outputs=outputs,
+    title="📈 Next Number Predictor (RNN)",
+    description="Enter 3 numbers (e.g., 1, 2, 3) and this app predicts the next number using a simple RNN!"
+)
+
+if __name__ == "__main__":
+    app.launch()