Update app.py

app.py

@@ -1,61 +1,435 @@
+# from flask import Flask
+# from PIL import Image
+# import numpy as np
+# import tensorflow as tf
+# import requests
+# import io
+
+# import os 
+
+# os.system("python3 -m http.server 7860 -b 0.0.0.0 &")
+# # Initialize the Flask application
+# app = Flask(__name__)
+
+# # Load the trained model
+# model = tf.keras.models.load_model('./save_model.h5',compile=False)
+
+
+# @app.route('/')
+# def aaa():
+#     return "hi"
+    
+# # Route for object detection
+# @app.route('/detect-object/<id>', methods=['POST','GET'])
+# def detect_pothole(id):
+#     # Get the image file from the request
+#     try :
+#         image_file  =  io.BytesIO(requests.get(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0").content)
+
+#         print(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0")
+
+#         # Load and preprocess the image
+#         image = Image.open(image_file)
+#         image = image.resize((64, 64))
+#         image = np.array(image)
+#         image = image / 255.0
+#         image = np.expand_dims(image, axis=0)
+
+#         # Debug statements
+#         print('Image shape:', image.shape)
+#         print('Image data:', image)
+
+#         # Make predictions
+#         result = model.predict(image)
+
+#         # Convert the prediction to a label
+#         if result[0][0] == 1:
+#             prediction = 'pothole'
+#         else:
+#             prediction = 'Normal'
+
+#     except :
+#         prediction = 'error'
+
+#     # Return the prediction as a JSON response
+#     response = {'prediction': prediction}
+
+#     return response
+
+# # Run the Flask application
+# if __name__ == '__main__':
+#     app.run()
+
+
+
 from flask import Flask
-from PIL import Image
-import numpy as np
+from flask import render_template
+import random
+from flask import Flask, request
+import pandas as pd
 import tensorflow as tf
-import requests
-import io
+import tensorflow_hub as hub
+import numpy as np
 
-import os 
+import nltk
+from nltk.corpus import stopwords
 
-os.system("python3 -m http.server 7860 -b 0.0.0.0 &")
-# Initialize the Flask application
-app = Flask(__name__)
+import emoji
+import re
+from sklearn.pipeline import Pipeline
 
-# Load the trained model
-model = tf.keras.models.load_model('./save_model.h5',compile=False)
+MAX_LENGTH =  50
+MAX_CURRENCY_FLAG = 2
+MAX_SPAM_WORDS = 1
+MAX_EMOJI = 2
+MAX_CONATANS = 1
+MAX_EMAIL= 1
+MAX_PHONE = 1
 
 
-@app.route('/')
-def aaa():
-    return "hi"
-    
-# Route for object detection
-@app.route('/detect-object/<id>', methods=['POST','GET'])
-def detect_pothole(id):
-    # Get the image file from the request
-    try :
-        image_file  =  io.BytesIO(requests.get(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0").content)
-
-        print(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0")
-
-        # Load and preprocess the image
-        image = Image.open(image_file)
-        image = image.resize((64, 64))
-        image = np.array(image)
-        image = image / 255.0
-        image = np.expand_dims(image, axis=0)
-
-        # Debug statements
-        print('Image shape:', image.shape)
-        print('Image data:', image)
-
-        # Make predictions
-        result = model.predict(image)
-
-        # Convert the prediction to a label
-        if result[0][0] == 1:
-            prediction = 'pothole'
-        else:
-            prediction = 'Normal'
-
-    except :
-        prediction = 'error'
-
-    # Return the prediction as a JSON response
-    response = {'prediction': prediction}
-
-    return response
-
-# Run the Flask application
-if __name__ == '__main__':
-    app.run()
+class RemoveStopWordsPunctuation:
+    def fit(self, x, y=None):
+        return self
+
+    def __remove_punctuation_stopwords(self, text):
+        pattern = re.compile("[{}]".format(re.escape("!\"#&'()*,-/:;<=>?[\\]^_`{|}~")))
+        text = " ".join(
+            [
+                word.strip()
+                for word in pattern.sub(" ", text.lower()).split()
+                if word not in set(stopwords.words("english"))
+            ]
+        )
+        return text
+
+    def transform(self, df):
+        df["Comment"] = df["Comment"].apply(self.__remove_punctuation_stopwords)
+        return df
+
+
+class AddLengthFlag:
+    def fit(self, x, y=None):
+        return self
+
+    def transform(self, X):
+        X["length"] = X["Comment"].str.len().astype(np.float32) / MAX_LENGTH
+        return X
+
+
+class AddCurrencyFlag:
+    def __init__(self) -> None:
+        self.currency_symbols = ["₤", "₨", "€", "₹", "₿", "$"]
+        self.pattern = "([\$₤₨€₹₿]+ *[0-9]* *[\.,]?[0-9]*)|([0-9]* *[\.,]?[0-9]* *[\$₤₨€₹₿]+)"
+
+    def fit(self, x, y=None):
+        return self
+
+    def __add_currency_count(self, text):
+        return len(re.findall(self.pattern, text)) / MAX_CURRENCY_FLAG
+
+    # def __add_currency_count(self,text):
+    #     return sum(text.count(symbol) for symbol in self.currency_symbols )
+
+    def transform(self, df):
+        df["currency"] = df["Comment"].apply(self.__add_currency_count).astype(np.float32)
+        return df
+
+
+class AddSpamWordsFlag:
+    def __init__(self) -> None:
+        self.spam_words = [
+            "morning",
+            "good"
+            "urgent",
+            "exclusive",
+            "limited time",
+            "free",
+            "guaranteed",
+            "act now",
+            "discount",
+            "special offer",
+            "prize",
+            "instant",
+            "cash",
+            "save",
+            "win",
+            "best",
+            "secret",
+            "incredible",
+            "congratulations",
+            "approved",
+            "risk free",
+            "hidden",
+            "bonus",
+            "sale",
+            "amazing",
+            "extra cash",
+            "opportunity",
+            "easy",
+            "double your",
+            "best price",
+            "cash back",
+            "deal",
+            "earn",
+            "money",
+            "no obligation",
+            "profit",
+            "results",
+            "exciting",
+            "unbelievable",
+            "jackpot",
+            "fantastic",
+            "instant access",
+            "million dollars",
+            "discounted",
+            "last chance",
+            "exclusive offer",
+            "big savings",
+            "limited offer",
+            "free trial",
+            "special promotion",
+            "secret revealed",
+            "valuable",
+            "money-back guarantee",
+            "lowest price",
+            "save money",
+            "make money",
+            "no risk",
+            "exclusive deal",
+            "limited supply",
+            "huge",
+            "incredible offer",
+            "prize winner",
+            "earn extra income",
+            "limited spots",
+            "new offer",
+            "best deal",
+            "don't miss out",
+            "great savings",
+            "top offer",
+            "double your income",
+            "discount code",
+            "fast cash",
+            "top-rated",
+            "best value",
+            "no cost",
+            "elite",
+            "act fast",
+            "unbeatable",
+            "cash prize",
+            "limited availability",
+            "special discount",
+            "quick cash",
+            "no catch",
+            "instant approval",
+            "big discount",
+            "easy money",
+            "insider",
+            "invitation",
+            "free shipping",
+            "huge discount",
+            "extra income",
+            "secret formula",
+            "no strings attached",
+            "money-making",
+            "dream come true",
+            "massive",
+            "free gift",
+            "incredible opportunity",
+            "risk-free trial",
+            "instant money",
+            "special price",
+            "no purchase necessary",
+            "now",
+        ]
+
+    def fit(self, x, y=None):
+        return self
+
+    def __add_currency_count(self, text):
+        return float(sum(text.count(symbol) for symbol in self.spam_words) / MAX_SPAM_WORDS)
+
+    def transform(self, df):
+        df["spam_word"] = df["Comment"].apply(self.__add_currency_count).astype(np.float32)
+        return df
+
+
+class AddEmojiFlag:
+    def __init__(self) -> None:
+        self.emoji_symbols = "[💭|🔝|🆗|🎉|🎊|📯|🙌|😂|💸|👉|📢|🚀|💲|💣|🔱|💼|🆙|⏳|✨|💌|💎|🆕|🔞|💡|💰|👑|⭐|🌟|🎤|⚡|📈|💵|🏆|💪|🔓|🆓|🎰|⌚|🚨|💢|📮|🔥|🎈|🎥|🔔|💯|🎶|🔗|🎁|📚|🔊|👍|👏|📱|📝|🤑|🏅|🔒|📣|💥]"
+
+    def fit(self, x, y=None):
+        return self
+
+    def __add_currency_count(self, text):
+        return float(len(re.findall(self.emoji_symbols, text)) / MAX_EMOJI)
+
+    def transform(self, df):
+        df["emoji"] = df["Comment"].apply(self.__add_currency_count).astype(np.float32)
+        return df
+
+
+class AddContainFlag:
+    def fit(self, x, y=None):
+        return self
+
+    def __add_first_count(self, text):
+        pattern = "[0-9]*%|T&C"
+        return len(re.findall(pattern, text))
+
+    def __add_second_count(self, text):
+        pattern = "(https:\/\/www\.|http:\/\/www\.|https:\/\/|http:\/\/)?[a-zA-Z0-9]{2,}(\.[a-zA-Z0-9]{2,})(\.[a-zA-Z0-9]{2,})?"
+        return len(re.findall(pattern, text))
+
+    def transform(self, df):
+        df["contain"] = df["Comment"].apply(self.__add_first_count)
+        df["contain"] = df["contain"] + df["Comment"].apply(self.__add_second_count)
+        df['contain'] = df['contain'].astype(np.float32) / MAX_CONATANS
+        return df
+
+
+class AddEmailFlag:
+    def fit(self, x, y=None):
+        return self
+
+    def __add_email_count(self, text):
+        pattern = "[\w]+@[\w]+\.\w+"
+        return float(len(re.findall(pattern, text))  /MAX_EMAIL)
+
+    def transform(self, df):
+        df["email"] = df["Comment"].apply(self.__add_email_count).astype(np.float32)
+        return df
+
+
+class AddPhoneFlag:
+    def fit(self, x, y=None):
+        return self
+
+    def __add_phone_no_count(self, text):
+        pattern = "\+?[0-9]?[0-9]? ?0?[0-9]{10}"
+        return len(re.findall(pattern, text))
+
+    def __add_phone_no_count_1(self, text):
+        pattern = "\+?[0-9]?\d{3}[ -]?\d{3}[ -]?\d{4}"
+        return len(re.findall(pattern, text))
+
+    def transform(self, df):
+        df["phone"] = df["Comment"].apply(self.__add_phone_no_count)
+        df["phone"] = df["phone"] + df["Comment"].apply(self.__add_phone_no_count_1)
+        df["phone"] = df["phone"].astype(np.float32) / MAX_PHONE
+
+
+        return df
+
+
+class RemovePhoneLinkEmail:
+    def fit(self, x, y=None):
+        return self
+
+    def __remove(self, text):
+        text = re.sub("\$[0-9]*([\.,][0-9]{2})*\$?", "", text)
+        text = re.sub("\+?[0-9]?[0-9]? ?0?[0-9]{10}", "", text)
+        text = re.sub("\+?[0-9]?\d{3}[ -]?\d{3}[ -]?\d{4}", "", text)
+        text = re.sub(
+            r"(https:\/\/www\.|http:\/\/www\.|https:\/\/|http:\/\/)?[a-zA-Z0-9]{2,}(\.[a-zA-Z0-9]{2,})(\.[a-zA-Z0-9]{2,})?",
+            "",
+            text,
+        )
+        text = re.sub(r"[\w]+@[\w]+\.\w+", "", text)
+        text = emoji.replace_emoji(text)
+        return text
+
+    def transform(self, df):
+        df["Comment"] = df["Comment"].apply(self.__remove)
+        return df
+
+
+class LemmatizeText:
+    def __init__(self):
+        self.lemmatizer = nltk.WordNetLemmatizer()
+
+    def fit(self, X, y=None):
+        return self
+
+    def __lemmatize_text(self, text):
+        return " ".join(
+            [self.lemmatizer.lemmatize(word) for word in re.split("\W+", text)]
+        ).strip()
+
+    def transform(self, df):
+        df["Comment"] = df["Comment"].map(lambda text: self.__lemmatize_text(text))
+        return df
+
+
+
+pipe =  Pipeline([
+
+    ("AddCurrencyFlag",AddCurrencyFlag()),
+    ("AddSpamWordsFlag",AddSpamWordsFlag()),
+    ("AddEmojiFlag",AddEmojiFlag()),
+    ("AddContainFlag",AddContainFlag()),
+    ("AddEmailFlag",AddEmailFlag()),
+    ("AddPhoneFlag",AddPhoneFlag()),
+
+    ("RemovePhoneLinkEmail",RemovePhoneLinkEmail()),
+    ("RemoveStopWordsPunctuation",RemoveStopWordsPunctuation()),
+
+    ("LemmatizeText",LemmatizeText()),
+
+    ("AddLengthFlag",AddLengthFlag()),
+
+
+])
+model = tf.keras.models.load_model('spam-model.h5', custom_objects={'KerasLayer':hub.KerasLayer})
+
+def precidt(msg):
+    if type(msg) is str:
+        df = pd.DataFrame([msg],columns=["Comment"])
+    elif type(msg) is list:
+        df = pd.DataFrame(msg,columns=["Comment"])
+    else:
+        return []
+
+    df = pipe.transform(df)
+    table = df
+    df = {
+        "Comment": tf.convert_to_tensor(df["Comment"],dtype=tf.string),
+        "Length": tf.convert_to_tensor(df["length"], dtype=tf.float32),
+        "Currency": tf.convert_to_tensor(df["currency"], dtype=tf.float32),
+        "Spam Words": tf.convert_to_tensor(df["spam_word"], dtype=tf.float32),
+        "Emoji": tf.convert_to_tensor(df["emoji"], dtype=tf.float32),
+        "Contain": tf.convert_to_tensor(df["contain"], dtype=tf.float32),
+        "Email": tf.convert_to_tensor(df["email"], dtype=tf.float32),
+        "Phone": tf.convert_to_tensor(df["phone"], dtype=tf.float32)
+    }
+    return [ i  for i in model.predict(df).reshape(-1,) ],table
+
+
+
+app = Flask(__name__,template_folder="templates")
+
+@app.route("/")
+def hello():
+    return render_template('index.html')
+
+
+@app.route("/api/data", methods=["POST"])
+def main():
+    data = request.get_json()['text']
+
+    value = precidt(data)[0]
+
+    if (value > 85)
+        score =  "Poor"
+
+    elif (value >50)
+        score = "Okay"
+
+    else
+        score = "Great"
+
+    return {"value": "{:.2f} % Spam".format(value) , "score" : "<span class='text-poor'>{score}</span>"}
+
+
+app.run()
+