Update app.py

app.py

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+# 🔽 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"
+)
+
+# 🔽 STEP 10: Create README.md
+readme = """
+# 🔢 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}")