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Update app.py
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app.py
CHANGED
@@ -90,6 +90,11 @@ def process_api(text):
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# Seq_Predicted = Seq_model.predict(padded_sequence)
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# predicted_label_index = np.argmax(Seq_Predicted)
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# ----------- Debug Logs -----------
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logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
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svc_debug = decodedLabel(int(SVM_Predicted[0]))
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@@ -98,8 +103,14 @@ def process_api(text):
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print('SVM', int(SVM_Predicted[0]), svc_debug)
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return {
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'Logistic_Predicted':
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# 'LSTM': decodedLabel(int(predicted_label_index)),
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'Article_Content': text
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}
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# Seq_Predicted = Seq_model.predict(padded_sequence)
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# predicted_label_index = np.argmax(Seq_Predicted)
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# ----------- Proba -----------
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Logistic_Predicted_proba = logistic_model.predict_proba(processed_text)
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svm_new_probs = SVM_model.decision_function(processed_text)
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svm_probs = svm_model.predict_proba(svm_new_probs)
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# ----------- Debug Logs -----------
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logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
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svc_debug = decodedLabel(int(SVM_Predicted[0]))
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print('SVM', int(SVM_Predicted[0]), svc_debug)
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return {
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'Logistic_Predicted':{
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'predicted_label': decodedLabel(int(Logistic_Predicted[0])),
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'probability': int(float(np.max(Logistic_Predicted_proba))*10000//100)
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},
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'SVM_Predicted': {
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'predicted_label': decodedLabel(int(SVM_Predicted[0])),
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'probability': int(float(np.max(svm_probs))*10000//100)
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},
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# 'LSTM': decodedLabel(int(predicted_label_index)),
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'Article_Content': text
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}
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