app.py

import gradio as gr
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
import re
from io import BytesIO
import tempfile

def preprocess_data(df):
    df.rename(columns={'Queries': 'texts'}, inplace=True)
    df['texts'] = df['texts'].astype(str)
    df['texts'] = df['texts'].str.lower()
    df['texts'] = df['texts'].apply(lambda text: re.sub(r'https?://\S+|www\.\S+', '', text))

    def remove_emoji(string):
        emoji_pattern = re.compile("["
                               u"\U0001F600-\U0001F64F"
                               u"\U0001F300-\U0001F5FF"
                               u"\U0001F680-\U0001F6FF"
                               u"\U0001F1E0-\U0001F1FF"
                               u"\U00002702-\U000027B0"
                               u"\U000024C2-\U0001F251"
                               "]+", flags=re.UNICODE)
        return emoji_pattern.sub(r'', string) if isinstance(string, str) else string

    df['texts'] = df['texts'].apply(remove_emoji)

    custom_synonyms = {
        'application': ['form'],
        'apply': ['fill', 'applied'],
        'work': ['job'],
        'salary': ['stipend', 'pay', 'payment', 'paid'],
        'test': ['online test', 'amcat test', 'exam', 'assessment'],
        'pass': ['clear', 'selected', 'pass or not'],
        'result': ['outcome', 'mark', 'marks'],
        'thanks': ["thanks a lot to you", "thankyou so much", "thank you so much", "tysm", "thank you",
                   "okaythank", "thx", "ty", "thankyou", "thank", "thank u"],
        'interview': ["pi"]
    }

    for original_word, synonym_list in custom_synonyms.items():
        for synonym in synonym_list:
            pattern = r"\b" + synonym + r"\b(?!\s*\()"
            df['texts'] = df['texts'].str.replace(pattern, original_word, regex=True)
            pattern = r"\b" + synonym + r"\s+you" + r"\b(?!\s*\()"
            df['texts'] = df['texts'].str.replace(pattern, original_word + ' ', regex=True)

    spam_list = ["click here", "free", "recharge", "limited", "discount", "money back guarantee", "aaj", "kal", "mein",
                 "how can i help you", "how can we help you", "how we can help you", "follow", "king", "contacting", "gar",
                 "kirke", "subscribe", "youtube", "jio", "insta", "make money", "b2b","sent using truecaller"]

    rows_to_remove = set()
    for spam_phrase in spam_list:
        pattern = r"\b" + re.escape(spam_phrase) + r"\b"
        spam_rows = df['texts'].str.contains(pattern)
        rows_to_remove.update(df.index[spam_rows].tolist())

    df = df.drop(rows_to_remove)

    greet_variations = ["hello", "hy", "hey", "hii", "hi", "heyyy", "bie", "bye"]
    for greet_var in greet_variations:
        pattern = r"(?<!\S)" + greet_var + r"(?!\S)|\b" + greet_var + r"\b"
        df['texts'] = df['texts'].str.replace(pattern, '', regex=True)

    okay_variations = ["ok", "k", "kay", "okay", "okie", "kk", "ohhhk","t","r"]
    for okay_var in okay_variations:
        pattern = r"(?<!\S)" + okay_var + r"(?!\S)|\b" + okay_var + r"\b"
        df['texts'] = df['texts'].str.replace(pattern, '', regex=True)

    yes_variations = ["yes", "yeah", "yep", "yup", "yuh", "ya", "yes got it", "yeah it is", "yesss", "yea","no"]
    for yes_var in yes_variations:
        pattern = r"(?<!\S)" + yes_var + r"(?!\S)|\b" + yes_var + r"\b"
        df['texts'] = df['texts'].str.replace(pattern, '', regex=True)

    remove_phrases = ["i'm all set","ask a question","apply the survey","videos (2-8 min)","long reads (> 8 min)",
                      "short reads (3-8 min)","not a student alumni","mock","share feedback","bite size (< 2 min)",
                      "actually no","next steps","i'm a student alumni","i have questions"]

    for phrase in remove_phrases:
        df['texts'] = df['texts'].str.replace(phrase, '')

    general_variations = ["good morning", "good evening", "good afternoon", "good night", "done", "sorry", "top", "query",
                          "stop", "sir", "sure", "oh", "wow", "aaa", "maam", "mam", "ma&#39;am","i'm all set","ask a question","apply the survey",
                          "videos (2-8 min)","long reads (> 8 min)","short reads (3-8 min)","not a student alumni","mock","share feedback","bite size (< 2 min)",
                          "actually no","next steps","i'm a student alumni","i have questions"]
    for gen_var in general_variations:
        pattern = r"(?<!\S)" + gen_var + r"(?!\S)|\b" + gen_var + r"\b(?=\W|$)"
        df['texts'] = df['texts'].str.replace(pattern, '', regex=True)

    def remove_punctuations(text):
        return re.sub(r'[^\w\s]', '', text)
    df['texts'] = df['texts'].apply(remove_punctuations)

    remove_morephrases = ["short reads 38 min","bite size  2 min","videos 28 min","long reads  8 min"]

    for phrase in remove_morephrases:
        df['texts'] = df['texts'].str.replace(phrase, '')

    df = df[~df['texts'].str.contains(r'\b\d{10}\b')]

    df['texts'] = df['texts'].str.strip()

    df['texts'] = df['texts'].apply(lambda x: x.strip())
    df = df[df['texts'] != '']

    return df

def cluster_data(df):
    num_clusters = 15  # Set the number of clusters to 15
    vectorizer = TfidfVectorizer(stop_words='english')
    X = vectorizer.fit_transform(df['texts'])

    kmeans = KMeans(n_clusters=num_clusters, random_state=0)
    kmeans.fit(X)
    df['Cluster'] = kmeans.labels_

    pca = PCA(n_components=2)
    principal_components = pca.fit_transform(X.toarray())
    df['PCA1'] = principal_components[:, 0]
    df['PCA2'] = principal_components[:, 1]

    return df

def visualize_clusters(df):
    plt.figure(figsize=(10, 6))
    scatter = plt.scatter(df['PCA1'], df['PCA2'], c=df['Cluster'], cmap='viridis')
    plt.legend(*scatter.legend_elements(), title="Clusters")
    plt.title('Clusters of User Queries')
    plt.xlabel('PCA Component 1')
    plt.ylabel('PCA Component 2')
    plt.show()

def main(file, num_clusters_to_display):
    try:
        df = pd.read_excel(file)
        df = preprocess_data(df)
        df = cluster_data(df)
        visualize_clusters(df)

        cluster_sizes = df['Cluster'].value_counts()
        sorted_clusters = cluster_sizes.index.tolist()
        df['Cluster'] = pd.Categorical(df['Cluster'], categories=sorted_clusters, ordered=True)
        df = df.sort_values('Cluster')

        # Filter out cluster 0 and get the largest clusters
        filtered_clusters = [cluster for cluster in sorted_clusters if cluster != 0]
        top_clusters = filtered_clusters[:num_clusters_to_display]

        df = df[df['Cluster'].isin(top_clusters)]
        df['Cluster'] = pd.Categorical(df['Cluster'], categories=top_clusters, ordered=True)
        df = df.sort_values('Cluster')

        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmpfile:
            df.to_csv(tmpfile.name, index=False)
            return tmpfile.name
    except Exception as e:
        return str(e)

interface = gr.Interface(
    fn=main,
    inputs=[
        gr.File(label="Upload Excel File (.xlsx)"),
        gr.Slider(1, 10, step=1, label="Number of Largest Clusters to Display")
    ],
    outputs=gr.File(label="Clustered Data CSV"),
    title="Unanswered User Queries Clustering",
    description="Upload an Excel file (.xlsx) and select the number of largest clusters to display (excluding cluster 0)"
)

interface.launch()