feat: refactor inference function

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/Hexabot.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.10 (Hexabot)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.10" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Hexabot)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Hexabot.iml" filepath="$PROJECT_DIR$/.idea/Hexabot.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

nlu/data_loaders/tflcdl.py

@@ -125,14 +125,6 @@ class TFLCDL(tfbp.DataLoader):
         else:
             raise ValueError("Unknown method!")
 
-    def get_prediction_data(self):
-        # The predict file contains a single JSON object whose only key is text.
-        data = self.json_helper.read_dataset_json_file("predict.json")
-        text = self.strip_numbers(data["text"])
-        encoded_texts = np.array(self.tfidf.transform(
-            [text]).toarray())  # type: ignore
-        return np.array([text]), encoded_texts
-
     def encode_text(self, text: str):
         sanitized_text = self.strip_numbers(text)
         return self.tfidf.transform([sanitized_text]).toarray() # type: ignore

nlu/models/intent_classifier.py

@@ -211,27 +211,10 @@ class IntentClassifier(tfbp.Model):
     @tfbp.runnable
     def predict(self):
         while True:
+
             text = input("Provide text: ")
-            inputs = self.data_loader.encode_text(text, self.tokenizer)
+            output = self.get_prediction(text)
-            intent_probas = self(inputs)  # type: ignore
-
-            intent_probas_np = intent_probas.numpy()
-
-            # Get the indices of the maximum values
-            intent_id = intent_probas_np.argmax(axis=-1)[0]
-
-            # get the confidences for each intent
-            intent_confidences = intent_probas_np[0]
-
-            weighted_margin = self.compute_normalized_confidence_margin(intent_probas_np)
-            output = {
-                "text": text,
-                "intent": {"name": self.extra_params["intent_names"][intent_id],
-                           "confidence": float(intent_confidences[intent_id])},
-                "margin": weighted_margin,
-            }
             print(output)
-
             # Optionally, provide a way to exit the loop
             if input("Try again? (y/n): ").lower() != 'y':
                 break

nlu/models/slot_filler.py

@@ -151,16 +151,19 @@ class SlotFiller(tfbp.Model):
 
     @tfbp.runnable
     def predict(self):
-        text = self.data_loader.get_prediction_data()
+        while True:
+            text = input("Provide text: ")
+            info = self.get_prediction(text)
 
-        info = self.get_prediction(text)
+            print(self.summary())
+            print("Text : " + text)
+            print(info)
+            
+            # Optionally, provide a way to exit the loop
+            if input("Try again? (y/n): ").lower() != 'y':
+                break
 
-        print(self.summary())
-        print("Text : " + text)
-        print(json.dumps(info, indent=2))
 
-        return json.dumps(info, indent=2)
-    
     def get_slots_prediction(self, text: str, inputs, slot_probas):
         slot_probas_np = slot_probas.numpy()
         # Get the indices of the maximum values

nlu/models/tflc.py

@@ -95,19 +95,27 @@ class TFLC(tfbp.Model):
 
         self.calculate_metrics(y_test, y_pred, languages)
 
+    def preprocess_text(self, text):
+        # The predict file contains a single JSON object whose only key is text.
+        stripped_text = self.strip_numbers(text)
+        encoded_text = np.array(self.tfidf.transform(
+            [stripped_text]).toarray())  # type: ignore
+        return np.array([stripped_text]), encoded_text
+
     @tfbp.runnable
     def predict(self):
         languages = list(self.extra_params['languages'])
-        texts, encoded_texts = self.data_loader.get_prediction_data()
+        input_provided = input("Provide text: ")
+        text, encoded_text = self.preprocess_text(input_provided)
         # converting a one hot output to language index
-        probas = super().predict(encoded_texts)
+        probas = super().predict(encoded_text)
         predictions = np.argmax(probas, axis=1)
 
         results = []
         for idx, prediction in enumerate(predictions):
             print('The sentence "{}" is in {}.'.format(
-                texts[idx], languages[prediction].upper()))
+                text[idx], languages[prediction].upper()))
-            results.append({'text': texts[idx], 'language': prediction})
+            results.append({'text': text[idx], 'language': prediction})
         return results
 
     def get_prediction(self, text: str):