Updates to matthew PC

1/7/2022

.gitignore

@@ -0,0 +1,4 @@
+
+model/phase1_74Q.data-00000-of-00001
+model/phase1_74Q.index
+__pycache__/chartbot_config.cpython-39.pyc

api.py

@@ -4,58 +4,52 @@
 import numpy as np
 import json
 
+from transformers import AutoTokenizer
+import numpy as np
+from transformers import TFAutoModelForSequenceClassification
+from tensorflow.keras.losses import BinaryCrossentropy
+from tensorflow.keras.optimizers import Adam
+import pandas as pd
+from chartbot_config import *
+
+
 app = Flask(__name__)
 api = Api(app)
 
 parser = reqparse.RequestParser()
 parser.add_argument('data')
 
+df = pd.read_csv(raw_data_path, encoding = 'unicode_escape')
+
+num_labels = df.Intent.nunique()
+df = df.melt(id_vars=["Domain", "Sub domain", "Intent", "Answer Format"]).drop("variable", axis = 1)
+Classes_dict_1 = dict(zip(list(df["Answer Format"].unique()), [i for i in range(df["Intent"].nunique())]))
+Classes_dict = {value:key for key, value in Classes_dict_1.items()}
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=num_labels, problem_type="multi_label_classification")
+model.load_weights(model_path)
+
+opt = Adam()
+loss = BinaryCrossentropy(from_logits=True)
+
+model.compile(
+    optimizer = opt,
+    loss = loss,
+    metrics=["accuracy"],
+)
+
 # Define how the api will respond to the post requests
 class QuestionClassifier(Resource):
     @app.route('/foo', methods=['POST']) 
     def post():
+
         data = request.json
         sentence = data['data']
         tokenized_dataset = tokenizer(sentence,  padding=True, truncation=True, return_tensors = "tf")
         output = model(**tokenized_dataset)["logits"]
         class_preds = [np.argmax(i) if np.sum(i) == 1 else len(i) for i in output > 0][0]
 
-        return jsonify(Classes_dict.get(class_preds, "Sorry I cannot understand."))
-
+        return jsonify(data=Classes_dict.get(class_preds, "Sorry I cannot understand."))
 
 api.add_resource(QuestionClassifier, '/chartbot')
-
-if __name__ == '__main__':
-
-    # Load model
-    from transformers import AutoTokenizer
-    import numpy as np
-    from transformers import TFAutoModelForSequenceClassification
-    from tensorflow.keras.losses import BinaryCrossentropy
-    from tensorflow.keras.optimizers import Adam
-    import pandas as pd
-    from chartbot_config import *
-
-    checkpoint = checkpoint
-    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-
-    df = pd.read_csv(raw_data_path, encoding = 'unicode_escape')
-    num_labels = df.Intent.nunique()
-
-    model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=num_labels, problem_type="multi_label_classification")
-    model.load_weights(model_path)
-
-    opt = Adam()
-    loss = BinaryCrossentropy(from_logits=True)
-
-    model.compile(
-        optimizer = opt,
-        loss = loss,
-        metrics=["accuracy"],
-    )
-
-    df = df.melt(id_vars=["Domain", "Sub domain", "Intent", "Answer Format"]).drop("variable", axis = 1)
-    Classes_dict_1 = dict(zip(list(df["Answer Format"].unique()), [i for i in range(df["Intent"].nunique())]))
-    Classes_dict = {value:key for key, value in Classes_dict_1.items()}
-
-    app.run(debug=True)

model/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "phase1_74Q"
+all_model_checkpoint_paths: "phase1_74Q"