Create main.py

main.py

@@ -0,0 +1,90 @@
+import math
+
+similar_letters = {"й": "и", "ё": "е", "e": "е", "t": "т", "i": "l", "o": "о", "k": "к", "3": "з", "a": "а", "x": "х", "c": "с", "m": "м"}
+letters = "qwertyuiopasdfghjklzxcvbnmёйцукенгшщзхъфывапролджэячсмитьбю"
+
+def countwords(x):
+    temp = {}
+    for word in x:
+        if word not in temp:
+            temp[word] = 1
+        else:
+            temp[word] += 1
+    return temp
+
+def add_dict(a, b):
+    temp = {}
+    for key in a:
+        if key in b:
+            temp[key] = a[key]+b[key]
+        else:
+            temp[key] = a[key]
+    for key in b:
+        if key not in a:
+            temp[key] = b[key]
+    return a
+
+class Chatbot:
+    def __init__(self, name=None, letter_replace: bool = True, data: dict = None, frequency_weight: float = 0, div_by_len: bool = False):
+        self.name = name
+        self.letter_replace = letter_replace
+        self.frequency_weight = frequency_weight
+        self.div_by_len = div_by_len
+        self.model = {}
+        if data is not None:
+            self.train(data)
+    def tokenize(self, text: str):
+        preprocess = ""
+        for x in text.lower():
+            if x in letters:
+                if x in similar_letters and self.letter_replace:
+                    preprocess += similar_letters[x]
+                else:
+                    preprocess += x
+        return preprocess.split()
+    def train(self, data: dict):
+        lendata = len(data)
+        lendata_div = 1/lendata
+        for x in data:
+            if data[x] not in self.model:
+                self.model[data[x]] = {"word count": countwords(self.tokenize(x)), "probabilities": {}, "weight count": 1, "weight": 0}
+            else:
+                self.model[data[x]]["word count"] = add_dict(countwords(self.tokenize(x)), self.model[data[x]]["word count"])
+                self.model[data[x]]["weight count"] += 1
+        for x in self.model:
+            probabilities = {}
+            div = 1/math.fsum(list(self.model[x]["word count"].values()))
+            for word in self.model[x]["word count"]:
+                probabilities[word] = self.model[x]["word count"][word]*div
+            self.model[x]["probabilities"] = probabilities
+            self.model[x]["weight"] = self.model[x]["weight count"] * lendata_div
+    def get_responses(self, text: str):
+        tokens = self.tokenize(text)
+        lentokens = len(tokens)
+        lentokens_div = 1/lentokens
+        scores = []
+        for choice in self.model:
+            score = 0
+            for token in tokens:
+                if token in self.model[choice]["probabilities"]:
+                    score += self.model[choice]["probabilities"][token]
+            if self.div_by_len:
+                score *= lentokens_div
+            score *= self.frequency_weight*self.model[choice]["weight"] + (1-self.frequency_weight)
+            scores.append((choice, score))
+        return sorted(scores, key=lambda x: x[1], reverse=True)
+    def __call__(self, text: str):
+        return self.get_responses(text)[0][0]
+
+if __name__ == "__main__":
+    import json
+
+    with open("dataset.json", "r") as file:
+        data = json.load(file)
+
+    cb = Chatbot(data=data)
+    while True:
+        message = input("User: ").lower()
+        print("Chatbot:", cb(message))
+        if "пока" in message:
+            break