Add custom inference script and model files

app.py

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+import gradio as gr
+import tensorflow as tf
+import joblib
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load your model and tokenizer
+model = tf.keras.models.load_model('lstm_model.h5')
+tokenizer = joblib.load('tokenizer.pkl')
+
+with open('padding_config.pkl', 'rb') as file:
+    padding_config = pickle.load(file)
+
+# Preprocessing function
+def preprocess(text):
+    tokenized_text = tokenizer.texts_to_sequences([text])
+    padded_text = pad_sequences(tokenized_text, **padding_config)
+    return padded_text
+
+# Prediction function
+def predict(text):
+    processed_text = preprocess(text)
+    prediction = model.predict(processed_text)
+    return prediction.tolist()
+
+# Gradio interface
+iface = gr.Interface(fn=predict, inputs="text", outputs="json")
+
+# Launch Gradio app
+iface.launch()
+

lstm_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c0a6a4c3b2b4e04b5b4969f702aa9e3658807876ea14aa645929f0bdfed8622
+size 3047376

padding_config.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a657ebd6b118ff15b47ff05d0cbad88200afbb206a424531eba767c3641bd01e
+size 44

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92911129cda7e87c410497dd5d3a26307dcbd34c107cc0d422ffcd538687461e
+size 10093140