Update app.py

app.py

@@ -10,463 +10,6 @@ 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):
-    # Split the question into words
-    words = question.split()
-    
-    # Check if the question has only one word
-    if len(words) == 1:
-        single_word = words[0].lower()
-        # Check if the single word is in the Start of Conversation category
-        if any(single_word in keyword for keyword in categories_keywords["Start of Conversation"]):
-            return "Start of Conversation"
-        else:
-            return "End of Conversation"
-    
-    # Categorization of other queries
-    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'] != '']
-
-    # Categorize the 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)
-    
-    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):
-    # Exclude common words from the top words
-    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)]
-    
-    category_top_words = df_top_categories.groupby('Category', observed=False)['texts'].apply(lambda x: ' '.join(x)).reset_index()
-    category_top_words['top_word'] = category_top_words['texts'].apply(lambda x: ' '.join([word for word in pd.Series(x.split()).value_counts().index if word not in common_words][:3]))
-    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)
-        
-        # Filter by 'Fallback Message shown'
-        df = df[df['Answer'] == 'Fallback Message shown']
-        
-        df = preprocess_data(df)
-        
-        # Get category sizes and sort by size in ascending order
-        category_sizes = df['Category'].value_counts().reset_index()
-        category_sizes.columns = ['Category', 'Count']
-        sorted_categories = category_sizes.sort_values(by='Count', ascending=True)['Category'].tolist()
-        
-        # Get the largest x categories as specified by num_clusters_to_display
-        largest_categories = sorted_categories[:num_clusters_to_display]
-        
-        # Filter the dataframe to include only the largest categories
-        filtered_df = df[df['Category'].isin(largest_categories)]
-        
-        # Sort the dataframe by Category
-        filtered_df = filtered_df.sort_values(by='Category')
-        
-        wordcloud_img = generate_wordcloud(filtered_df)
-        bar_chart_img = generate_bar_chart(filtered_df, num_clusters_to_display)
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmpfile:
-            filtered_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)
-
-
-In this code, why does the selection of 'number of categories' (slider) affect the quality of the output? I want this slider to ONLY change the number of cateogies visible, not the actual categorization itself. However, when this slider is changed, it does not accurately display only the x largest categories. Additionally, the bar graph and the word cloud should be INDEPENDENT of this slider and should act on the data as a whole regardless of the slider.
-ChatGPT
-Memory updated
-The issue you're experiencing with the slider affecting the output is due to the way the filtering is implemented before generating the visualizations. To ensure that the slider only affects the number of categories displayed and not the actual categorization or the visualizations, we need to modify the code accordingly.
-
-Here’s the updated code:
-
-python
-Copy code
-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)
-    
-    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)]
-    
-    category_top_words = df_top_categories.groupby('Category', observed=False)['texts'].apply(lambda x: ' '.join(x)).reset_index()
-    category_top_words['top_word'] = category_top_words['texts'].apply(lambda x: ' '.join([word for word in pd.Series(x.split()).value_counts().index if word not in common_words][:3]))
-    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)
-Changes made:
-
-Categorization: The preprocess_data function now categorizes the data without any filtering based on the slider value.
-Visualizations: The word cloud and bar chart functions use the entire categorized data to generate the visuals.
-Filtering: The filtering based on the slider value is only applied within the generate_bar_chart function to limit the number of categories displayed in the bar chart.
-With these changes, the slider will only affect the number of categories displayed in the bar chart, and the word cloud will be generated using the entire dataset regardless of the slider value. The CSV output will also include all categorized data.
-
-Now the barchart works as intended, but the word cloud doesn't. Can we restore the old word cloud mechanism while still maintaining the new bar chart funcitonality, so both work as intended?
-ChatGPT
-Sure, we can restore the old word cloud mechanism to ensure it functions as intended while keeping the new bar chart functionality. Here is the updated code:
-
-python
-Copy code
-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"],
@@ -626,7 +169,7 @@ def generate_bar_chart(df, num_clusters_to_display):
     img = Image.open(buf)
     return img
 
-def main(file, num_categories_to_display):
+def main(file, num_clusters_to_display):
     try:
         df = pd.read_csv(file)
         
@@ -634,19 +177,16 @@ def main(file, num_categories_to_display):
         df = df[df['Answer'] == 'Fallback Message shown']
         
         df = preprocess_data(df)
-
-        df = df[df['Category'] != 'Miscellaneous']
         
-        # Get category sizes and sort by size in descending order
+        # Get category sizes and sort by size in ascending order
         category_sizes = df['Category'].value_counts().reset_index()
         category_sizes.columns = ['Category', 'Count']
         sorted_categories = category_sizes.sort_values(by='Count', ascending=False)['Category'].tolist()
         sorted_categories_sm = category_sizes.sort_values(by='Count', ascending=True)['Category'].tolist()
-
         
-        # Get the largest x categories as specified by num_categories_to_display
-        largest_categories = sorted_categories[:num_categories_to_display]
-        smallest_categories = sorted_categories_sm[:num_categories_to_display]
+        # Get the largest x categories as specified by num_clusters_to_display
+        largest_categories = sorted_categories[:num_clusters_to_display]
+        smallest_categories = sorted_categories[:num_clusters_to_display]
         
         # Filter the dataframe to include only the largest categories
         filtered_df = df[df['Category'].isin(largest_categories)]
@@ -656,8 +196,8 @@ def main(file, num_categories_to_display):
         filtered_df = filtered_df.sort_values(by='Category')
         filtered_cloud_df = filtered_cloud_df.sort_values(by='Category')
         
-        wordcloud_img = generate_wordcloud(filtered_cloud_df)  # Word cloud based on the entire dataframe
-        bar_chart_img = generate_bar_chart(df, num_categories_to_display)  # Bar chart based on the filtered dataframe
+        wordcloud_img = generate_wordcloud(filtered_cloud_df)
+        bar_chart_img = generate_bar_chart(filtered_df, num_clusters_to_display)
 
         with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmpfile:
             filtered_df.to_csv(tmpfile.name, index=False)
@@ -668,8 +208,6 @@ def main(file, num_categories_to_display):
         print(f"Error: {e}")
         return str(e), None, None
 
-
-
 interface = gr.Interface(
     fn=main,
     inputs=[
@@ -686,3 +224,4 @@ interface = gr.Interface(
 )
 
 interface.launch(share=True)
+