app.py

import gradio as gr
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import re
from io import BytesIO
import tempfile
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import plotly.express as px
from PIL import Image

categories_keywords = {
    "Application Status": ["application status", "application", "status", "submitted", "processing", "pending", "approval", "rejected", "accepted"],
    "Volunteering": ["volunteer", "volunteering", "help out", "assist", "volunteer work", "volunteer opportunities"],
    "Certificates": ["certificate", "certificates", "completion", "certification", "accreditation", "proof", "document", "certified"],
    "Job Opportunities": ["job", "opportunity", "career", "vacancy", "position", "employment", "hiring", "recruitment", "internship"],
    "Surveys and Forms": ["survey", "form", "forms", "questionnaire", "feedback form", "response", "fill out", "submission"],
    "General Queries": ["hello", "hi", "help", "general", "query", "question", "info", "information", "inquiry", "ask"],
    "Spam": ["spam", "subscribe", "remove", "stop", "junk", "block", "opt-out", "watch my video", "click on this link", "win prize"],
    "Rescheduling and Postponing": ["reschedule", "postpone", "delay", "change date", "new time", "rearrange", "shift", "adjust timing"],
    "Contact and Communication Issues": ["contact", "communicate", "communication", "reach out", "phone", "email", "address", "details"],
    "Email and Credentials Issues": ["email", "credentials", "login", "password", "access", "username", "account", "verification", "reset"],
    "Timing and Scheduling": ["timing", "schedule", "scheduling", "time", "appointment", "availability", "calendar", "book", "slot"],
    "Salary and Benefits": ["salary", "benefits", "pay", "compensation", "wages", "earnings", "package", "remuneration", "incentives"],
    "Technical Issues": ["technical", "issue", "problem", "error", "bug", "glitch", "fix", "troubleshoot", "support"],
    "End of Conversation": ["bye", "thank you", "thanks", "goodbye", "see you", "later", "end", "close", "sign off"],
    "Start of Conversation": ["start", "begin", "hello", "hi", "initiate", "greet", "greeting", "open", "commence"],
    "Feedback": ["feedback", "comments", "review", "opinion", "suggestion", "critique", "rating"],
    "Online Meetings": ["meeting", "meeting code", "passcode", "join meeting", "zoom", "zoom link"],
    "Event Inquiries": ["event", "webinar", "meeting", "conference", "session", "seminar", "workshop", "invitation"],
    "Payment Issues": ["payment", "billing", "transaction", "charge", "fee", "invoice", "refund", "receipt"],
    "Registration Issues": ["registration", "register", "sign up", "enroll", "join", "signup", "enrollment"],
    "Service Requests": ["service", "support", "request", "assistance", "help", "aid", "maintenance"],
    "Account Issues": ["account", "profile", "update", "activation", "deactivation", "credentials", "reset"],
    "Product Information": ["product", "service", "details", "info", "information", "specifications", "features"],
    "Order Status": ["order", "status", "tracking", "shipment", "delivery", "purchase", "dispatch"],
    "Miscellaneous": []
}

def categorize_question(question):
    words = question.split()
    if len(words) == 1:
        single_word = words[0].lower()
        if any(single_word in keyword for keyword in categories_keywords["Start of Conversation"]):
            return "Start of Conversation"
        else:
            return "End of Conversation"
    
    for category, keywords in categories_keywords.items():
        if any(keyword.lower() in question.lower() for keyword in keywords):
            return category
    return "Miscellaneous"

def preprocess_data(df):
    df.rename(columns={'Question Asked': 'texts'}, inplace=True)
    df['texts'] = df['texts'].astype(str).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)

    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"
            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"]

    for spam_phrase in spam_list:
        pattern = r"\b" + re.escape(spam_phrase) + r"\b"
        df = df[~df['texts'].str.contains(pattern)]

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

    df['texts'] = df['texts'].apply(remove_punctuations)
    df['texts'] = df['texts'].str.strip()
    df = df[df['texts'] != '']

    df['Category'] = df['texts'].apply(categorize_question)

    return df

def cluster_data(df, num_clusters):
    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_

    return df, kmeans

def generate_wordcloud(df):
    text = " ".join(df['texts'].tolist())
    stopwords = set(STOPWORDS)
    wordcloud = WordCloud(
        width=800,
        height=400,
        background_color='white',
        max_words=300,
        collocations=False,
        min_font_size=10,
        max_font_size=200,
        stopwords=stopwords,
        prefer_horizontal=1.0,
        scale=2,
        relative_scaling=0.5,
        random_state=42
    ).generate(text[:1000000])  # Limit the text length to 1,000,000 characters to avoid memory issues
    
    plt.figure(figsize=(15, 7))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    buf = BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    img = Image.open(buf)
    return img

def generate_bar_chart(df, num_clusters_to_display):
    common_words = {'i', 'you', 'thanks', 'thank', 'ok', 'okay', 'sure', 'done'}
    
    top_categories = df['Category'].value_counts().index[:num_clusters_to_display]
    df_top_categories = df[df['Category'].isin(top_categories)]
    
    def extract_top_words(texts, n=1):
        vec = TfidfVectorizer(stop_words='english').fit(texts)
        sum_words = vec.transform(texts).sum(axis=0)
        words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
        sorted_words = sorted(words_freq, key=lambda x: x[1], reverse=True)
        return [word for word, freq in sorted_words if word not in common_words][:n]

    category_top_words = df_top_categories.groupby('Category')['texts'].apply(lambda texts: extract_top_words(texts, 3)).reset_index()
    category_top_words['top_word'] = category_top_words['texts'].apply(lambda words: ', '.join(words))
    
    category_sizes = df_top_categories['Category'].value_counts().reset_index()
    category_sizes.columns = ['Category', 'Count']
    category_sizes = category_sizes.merge(category_top_words[['Category', 'top_word']], on='Category')
    
    fig = px.bar(category_sizes, x='Category', y='Count', text='top_word', title='Category Frequency with Top Words')
    fig.update_traces(textposition='outside')
    fig.update_layout(xaxis_title='Category', yaxis_title='Frequency', showlegend=False)
    
    buf = BytesIO()
    fig.write_image(buf, format='png')
    buf.seek(0)
    img = Image.open(buf)
    return img

def main(file, num_clusters_to_display):
    try:
        df = pd.read_csv(file)
        
        df = df[df['Answer'] == 'Fallback Message shown']
        
        df = preprocess_data(df)
        
        wordcloud_img = generate_wordcloud(df)
        bar_chart_img = generate_bar_chart(df, num_clusters_to_display)

        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmpfile:
            df.to_csv(tmpfile.name, index=False)
            csv_file_path = tmpfile.name

        return csv_file_path, wordcloud_img, bar_chart_img
    except Exception as e:
        print(f"Error: {e}")
        return str(e), None, None

interface = gr.Interface(
    fn=main,
    inputs=[
        gr.File(label="Upload CSV File (.csv)"),
        gr.Slider(label="Number of Categories to Display", minimum=1, maximum=15, step=1, value=5)
    ],
    outputs=[
        gr.File(label="Categorized Data CSV"),
        gr.Image(label="Word Cloud"),
        gr.Image(label="Bar Chart")
    ],
    title="Unanswered User Queries Categorization",
    description="Categorize unanswered user queries into predefined categories"
)

interface.launch(share=True)