Minor changes

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
+from model import CustomModel
 import os
-from nltk.corpus import stopwords
 
 os.system("cp -r ./nltk_data/ /home/user/nltk_data")
 
 def analyze(text):
-    text = stopwords.words("english")[0]
+    model = CustomModel()
     return text
 
 app = gr.Interface(fn=analyze, inputs="text", outputs="text")

model.py

@@ -0,0 +1,51 @@
+class CustomModel:
+
+    def __init__(self):
+        self.attr_1_model = pickle.load(open("models/cog_model.pkl", "rb"))
+        self.attr_2_model = pickle.load(open("models/eff_model.pkl", "rb"))
+        self.attr_3_model = pickle.load(open("models/reas_model.pkl", "rb"))
+        self.arg_model = pickle.load(open("models/qual_model.pkl", "rb"))
+
+    def predict(self, array):
+        attr_1 = self.attr_1_model.predict(array, verbose=0)
+        attr_2 = self.attr_2_model.predict(array, verbose=0)
+        attr_3 = self.attr_3_model.predict(array, verbose=0)
+        attr_1 = self.__decode(attr_1)
+        attr_2 = self.__decode(attr_2)
+        attr_3 = self.__decode(attr_3)
+        array = self.__transform(attr_1, attr_2, attr_3, array)
+        pred = self.arg_model.predict(array)
+        return pred
+
+    def __decode(self, array):
+        new_array = []
+        label_map = {
+            0: "1 (Low)",
+            1: "2 (Average)",
+            2: "3 (High)",
+        }
+        for ele in array:
+            new_array.append(label_map[np.argmax(ele)])
+        return np.array(new_array)
+
+    def __transform(self, attr_1, attr_2, attr_3, array):
+        attr_1 = self.__encode(attr_1)
+        attr_2 = self.__encode(attr_2)
+        attr_3 = self.__encode(attr_3)
+        array_new = []
+        for idx, ele in enumerate(array):
+            temp = np.concatenate((attr_1[idx], attr_2[idx], attr_3[idx], ele))
+            array_new.append(temp)
+        array = np.array(array_new)
+        return array
+
+    def __encode(self, array):
+        new_array = []
+        label_map = {
+            "1 (Low)": np.array([0, 0, 1]),
+            "2 (Average)": np.array([0, 1, 0]),
+            "3 (High)": np.array([1, 0, 0]),
+        }
+        for ele in array:
+            new_array.append(label_map[ele])
+        return np.array(new_array)