Mpavan45 commited on
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fcecef6
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1 Parent(s): 9be06fa

Update app.py

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![DALL路E 2025-03-19 12.15.26 - A landscape background image for a news-related Streamlit web application. The image features a futuristic newsroom with digital screens displaying br.webp](https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/WwOWG8MBGYxHnIeM2Dowo.webp)

Files changed (1) hide show
  1. app.py +17 -17
app.py CHANGED
@@ -60,35 +60,35 @@ def pre_process(x):
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  return " ".join(x)
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- @st.cache_resource
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- def load_model():
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- model = keras.models.load_model("model_m3_new.keras")
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- with open("label_encoder_m5.pkl", 'rb') as file:
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- label_encoder = pickle.load(file)
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- return model, label_encoder
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- model, label_encoder = load_model()
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- def predict_category(text):
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- cleaned_text = pre_process(text)
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- vectorizer = keras.models.load_model("vec_text_m3_new.keras")
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- # Vectorizing the pre-processed text
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- text_vectorized = pad_sequences(vectorizer.predict(np.array([cleaned_text])).numpy(), padding='pre', maxlen=128)
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- # Model prediction
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- prediction = model.predict(text_vectorized)
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- category_idx = np.argmax(prediction, axis=1)[0]
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- return label_encoder.inverse_transform([category_idx])[0], cleaned_text
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  # Custom CSS
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  st.markdown(
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  """
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  <style>
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  body {
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- background-image: url('https://cdn-uploads.huggingface.co/production/uploads/67441c51a784a9d15cb12871/4FFTjgkYjYUq6w-0gR15v.jpeg');
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  background-size: cover;
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  background-repeat: no-repeat;
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  background-attachment: fixed;
 
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  return " ".join(x)
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+ # @st.cache_resource
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+ # def load_model():
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+ # model = keras.models.load_model("model_m3_new.keras")
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+ # with open("label_encoder_m5.pkl", 'rb') as file:
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+ # label_encoder = pickle.load(file)
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+ # return model, label_encoder
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+ # model, label_encoder = load_model()
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+ # def predict_category(text):
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+ # cleaned_text = pre_process(text)
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+ # vectorizer = keras.models.load_model("vec_text_m3_new.keras")
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+ # # Vectorizing the pre-processed text
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+ # text_vectorized = pad_sequences(vectorizer.predict(np.array([cleaned_text])).numpy(), padding='pre', maxlen=128)
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+ # # Model prediction
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+ # prediction = model.predict(text_vectorized)
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+ # category_idx = np.argmax(prediction, axis=1)[0]
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+ # return label_encoder.inverse_transform([category_idx])[0], cleaned_text
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  # Custom CSS
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  st.markdown(
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  """
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  <style>
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  body {
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+ background-image: url('https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/WwOWG8MBGYxHnIeM2Dowo.webp');
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  background-size: cover;
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  background-repeat: no-repeat;
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  background-attachment: fixed;