app.py

# 🔽 STEP 1: Install Required Packages
!pip install tensorflow huggingface_hub

# 🔽 STEP 2: Import Libraries
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense
from tensorflow.keras.optimizers import Adam
from huggingface_hub import notebook_login, HfApi, create_repo

# 🔽 STEP 3: Prepare Dataset
def generate_sequence():
    X, y = [], []
    for i in range(1, 100):
        X.append([i, i+1, i+2])
        y.append(i+3)
    X = np.array(X).reshape(-1, 3, 1)
    y = np.array(y)
    return X, y

X, y = generate_sequence()

# 🔽 STEP 4: Build the RNN Model
model = Sequential([
    SimpleRNN(32, activation='relu', input_shape=(3, 1)),
    Dense(1)
])
model.compile(optimizer=Adam(), loss='mse')
model.summary()

# 🔽 STEP 5: Train the Model
model.fit(X, y, epochs=200, verbose=0)

# 🔽 STEP 6: Save the Model
model.save("rnn_next_number.h5")

# 🔽 STEP 7: Login to Hugging Face
notebook_login()  # Paste your token when asked

# 🔽 STEP 8: Create Repo on Hugging Face
repo_id = "your-username/predict-next-number-rnn"  # Replace with your actual username
create_repo(name="predict-next-number-rnn", repo_type="model", private=False)

# 🔽 STEP 9: Upload Model to Hugging Face
api = HfApi()
api.upload_file(
    path_or_fileobj="rnn_next_number.h5",
    path_in_repo="rnn_next_number.h5",
    repo_id=repo_id,
    repo_type="model"
)


# 🔢 Predict Next Number - RNN Model

This is a simple RNN model trained using Keras to predict the next number in a sequence.

## Example

Input: [4, 5, 6] → Output: ~7.0

### Usage

```python
from tensorflow.keras.models import load_model
import numpy as np

model = load_model("rnn_next_number.h5")
x_input = np.array([4, 5, 6]).reshape(1, 3, 1)
prediction = model.predict(x_input)

print(f"Predicted next number: {prediction[0][0]:.2f}")