Merge pull request #292 from appwrite/feat-generate-with-tensorflow

feat: python generate with tensorflow

Author: christyjacob4

python-ml/generate_with_tensorflow/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
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+var/
+wheels/
+share/python-wheels/
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+*.spec
+
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+pip-log.txt
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+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
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+
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+profile_default/
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+
+# pyenv
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+#   intended to run in multiple environments; otherwise, check them in:
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+
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
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+/site
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+#.idea/

python-ml/generate_with_tensorflow/README.md

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+# 🤖 Python Generate with TensorFlow Function
+
+Generate text using a TensorFlow-based RNN model.
+
+## 🧰 Usage
+
+### GET /
+
+HTML form for interacting with the function.
+
+### POST /
+
+Query the model for a text generation completion.
+
+**Parameters**
+
+| Name         | Description                          | Location | Type               | Sample Value       |
+| ------------ | ------------------------------------ | -------- | ------------------ | ------------------ |
+| Content-Type | The content type of the request body | Header   | `application/json` | N/A                |
+| prompt       | Text to prompt the model             | Body     | String             | `Once upon a time` |
+
+Sample `200` Response:
+
+Response from the model.
+
+```json
+{
+  "ok": true,
+  "completion": "Once upon a time, in a land far, far away, there lived a wise old owl."
+}
+```
+
+Sample `400` Response:
+
+Response when the request body is missing.
+
+```json
+{
+  "ok": false,
+  "error": "Missing body with a prompt."
+}
+```
+
+Sample `500` Response:
+
+Response when the model fails to respond.
+
+```json
+{
+  "ok": false,
+  "error": "Failed to query model."
+}
+```
+
+## ⚙️ Configuration
+
+| Setting           | Value                                                    |
+| ----------------- | -------------------------------------------------------- |
+| Runtime           | Python ML (3.11)                                         |
+| Entrypoint        | `src/main.py`                                            |
+| Build Commands    | `pip install -r requirements.txt && python src/train.py` |
+| Permissions       | `any`                                                    |
+| Timeout (Seconds) | 30                                                       |

python-ml/generate_with_tensorflow/requirements.txt

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+tensorflow
+numpy

python-ml/generate_with_tensorflow/src/main.py

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+import tensorflow as tf
+import numpy as np
+from .utils import get_static_file, throw_if_missing
+
+
+def main(context):
+    if context.req.method == "GET":
+        return context.res.send(
+            get_static_file("index.html"),
+            200,
+            {"content-type": "text/html; charset=utf-8"},
+        )
+
+    try:
+        throw_if_missing(context.req.body, ["prompt"])
+    except ValueError as err:
+        return context.res.json({"ok": False, "error": str(err)}, 400)
+
+    prompt = context.req.body["prompt"]
+    generated_text = generate_text(prompt)
+    return context.res.json({"ok": True, "completion": generated_text}, 200)
+
+
+def generate_text(prompt):
+    # Load the trained model and tokenizer
+    model = tf.keras.models.load_model("text_generation_model.h5")
+    char2idx = np.load("char2idx.npy", allow_pickle=True).item()
+    idx2char = np.load("idx2char.npy", allow_pickle=True)
+
+    # Vectorize the prompt
+    input_eval = [char2idx[s] for s in prompt]
+    input_eval = tf.expand_dims(input_eval, 0)
+
+    # Generate text
+    text_generated = []
+    temperature = 1.0
+
+    model.reset_states()
+    for _ in range(1000):
+        predictions = model(input_eval)
+        predictions = tf.squeeze(predictions, 0)
+        predictions = predictions / temperature
+        predicted_id = tf.random.categorical(predictions, num_samples=1)[-1, 0].numpy()
+
+        input_eval = tf.expand_dims([predicted_id], 0)
+        text_generated.append(idx2char[predicted_id])
+
+    return prompt + "".join(text_generated)

python-ml/generate_with_tensorflow/src/train.py

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+import tensorflow as tf
+import numpy as np
+import os
+
+
+def main():
+    path_to_file = tf.keras.utils.get_file(
+        "shakespeare.txt",
+        "https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt",
+    )
+    text = open(path_to_file, "rb").read().decode(encoding="utf-8")
+    vocab = sorted(set(text))
+    char2idx = {u: i for i, u in enumerate(vocab)}
+    idx2char = np.array(vocab)
+
+    text_as_int = np.array([char2idx[c] for c in text])
+    seq_length = 100
+    char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
+    sequences = char_dataset.batch(seq_length + 1, drop_remainder=True)
+
+    def split_input_target(chunk):
+        input_text = chunk[:-1]
+        target_text = chunk[1:]
+        return input_text, target_text
+
+    dataset = sequences.map(split_input_target)
+    BATCH_SIZE = 64
+    BUFFER_SIZE = 10000
+    dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
+
+    vocab_size = len(vocab)
+    embedding_dim = 256
+    rnn_units = 1024
+
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.Embedding(
+                vocab_size, embedding_dim, batch_input_shape=[BATCH_SIZE, None]
+            ),
+            tf.keras.layers.GRU(
+                rnn_units,
+                return_sequences=True,
+                stateful=True,
+                recurrent_initializer="glorot_uniform",
+            ),
+            tf.keras.layers.Dense(vocab_size),
+        ]
+    )
+
+    model.compile(
+        optimizer="adam", loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True)
+    )
+
+    EPOCHS = 10
+    checkpoint_dir = "./training_checkpoints"
+
+    os.makedirs(checkpoint_dir, exist_ok=True)
+
+    checkpoint_prefix = f"{checkpoint_dir}/ckpt_{{epoch}}"
+
+    checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
+        filepath=checkpoint_prefix, save_weights_only=True
+    )
+
+    model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])
+
+    model.save("text_generation_model.h5")
+    np.save("char2idx.npy", char2idx)
+    np.save("idx2char.npy", idx2char)
+
+    os.remove(path_to_file)
+
+
+if __name__ == "__main__":
+    main()

python-ml/generate_with_tensorflow/src/utils.py

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+import os
+
+__dirname = os.path.dirname(os.path.abspath(__file__))
+static_folder = os.path.join(__dirname, "../static")
+
+
+def get_static_file(file_name: str) -> str:
+    """
+    Returns the contents of a file in the static folder
+
+    Parameters:
+        file_name (str): Name of the file to read
+
+    Returns:
+        (str): Contents of static/{file_name}
+    """
+    file_path = os.path.join(static_folder, file_name)
+    with open(file_path, "r") as file:
+        return file.read()
+
+
+def throw_if_missing(obj: object, keys: list[str]) -> None:
+    """
+    Throws an error if any of the keys are missing from the object
+
+    Parameters:
+        obj (object): Object to check
+        keys (list[str]): List of keys to check
+
+    Raises:
+        ValueError: If any keys are missing
+    """
+    missing = [key for key in keys if key not in obj or not obj[key]]
+    if missing:
+        raise ValueError(f"Missing required fields: {', '.join(missing)}")