app.py

from huggingface_hub import hf_hub_download
import joblib

repo_id = "DevBhojani/Classification-SamsumDataset"
model_filename = "random_forest_classifier_model.joblib"

model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
loaded_classifier_model = joblib.load(model_path)

import joblib
from sklearn.feature_extraction.text import TfidfVectorizer

repo_id = "DevBhojani/Classification-SamsumDataset"
model_filename = "random_forest_classifier_model.joblib"
vectorizer_filename = "tfidf_vectorizer.joblib"

model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
vectorizer_path = hf_hub_download(repo_id=repo_id, filename=vectorizer_filename)

loaded_classifier_model = joblib.load(model_path)
loaded_tfidf_vectorizer = joblib.load(vectorizer_path)

import gradio as gr
from transformers import pipeline, AutoTokenizer
import re
import contractions
# Assuming loaded_classifier_model and loaded_tfidf_vectorizer are already loaded from the previous cell

def remove_html_tags(text):
    pattern = r'[^a-zA-Z0-9\s]'
    text = re.sub(pattern, '', str(text))
    return text

def remove_url(text):
    pattern = re.compile(r'https?://\S+|www\.\S+')
    return pattern.sub(r'', str(text))

def remove_emojis(text):
    emoji_pattern = re.compile(
        "["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags
        u"\U00002700-\U000027BF"  # miscellaneous symbols
        u"\U0001F900-\U0001F9FF"  # supplemental symbols
        u"\U00002600-\U000026FF"  # weather & other symbols
        u"\U0001FA70-\U0001FAFF"  # extended symbols
        "]+",
        flags=re.UNICODE
    )
    return emoji_pattern.sub(r'', str(text))

def expand_contractions(text):
    return contractions.fix(text)

def remove_special_and_numbers(text):
    return re.sub(r'[^a-zA-Z\s]', '', str(text))

def clean_text(text):
    text = remove_url(text)
    text = remove_emojis(text)
    text = expand_contractions(text)
    text = text.lower()
    return text

summarizer = pipeline("summarization", model="luisotorres/bart-finetuned-samsum")
# summarizer2 = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
tokenizer = AutoTokenizer.from_pretrained("luisotorres/bart-finetuned-samsum")

def split_into_chunks(conversation, n=15):
    lines = conversation.strip().split('\n')
    chunk_size = max(1, len(lines) // n)
    return ['\n'.join(lines[i:i+chunk_size]) for i in range(0, len(lines), chunk_size)]

def truncate_chunk(text, max_tokens=1024):
    tokens = tokenizer.encode(text, truncation=True, max_length=max_tokens)
    return tokenizer.decode(tokens, skip_special_tokens=True)

def summarize_chunks(chunks, model):
    summaries = []
    for chunk in chunks:
        chunk = chunk.strip()
        if not chunk:
            continue
        try:
            truncated_chunk = truncate_chunk(chunk)
            summary = model(truncated_chunk, max_length=1024, min_length=20, do_sample=False)[0]['summary_text']
            summaries.append(summary)
        except Exception as e:
            print(f"Error summarizing chunk: {e}")
    return summaries

def combine_summaries(summaries):
    return ' '.join(summaries)

def summarize_dialogue(conversation, model):
    chunks = split_into_chunks(conversation, n=1)
    summaries = summarize_chunks(chunks, model)
    final_summary = combine_summaries(summaries)
    return final_summary

def analyze_meeting_transcript(user_input):
    if not user_input.strip():
        return "Please enter some text to summarize.", ""

    cleaned_input = clean_text(user_input)
    summary1 = summarize_dialogue(cleaned_input, summarizer)

    # Use the loaded vectorizer to transform the input
    cleaned_input_vectorized = loaded_tfidf_vectorizer.transform([cleaned_input])

    intent_classification = loaded_classifier_model.predict(cleaned_input_vectorized)[0]
    # print(intent_classification)
    # print(cleaned_input_vectorized)
    # intent_classification = "Transactional Inquiry & Information Exchange"

    # Format the intent classification output
    formatted_intent = intent_classification.replace("__label__", "").replace("_", " ")


    return summary1, formatted_intent

interface = gr.Interface(
    fn=analyze_meeting_transcript,
    inputs=gr.Textbox(label="Enter dialogue here", lines=12, placeholder="Paste your meeting transcript..."),
    outputs=[
        gr.Textbox(label="Summary (Luis Torres BART)"),
        # gr.Textbox(label="Summary 2 (KN Karthick MEETING_SUMMARY)"),
        gr.Textbox(label="Intent Classification") # Removed "Placeholder"
    ],
    title="Meeting Transcript Analyzer",
    description="Summarizes meeting dialogues and classifies the intent.",
    allow_flagging="never",
    examples=[
        [
            '''
Amanda: guess what!
Chris: hey ;) ur pregnant!
Amanda: I'm so proud of myself! Remember I go to these dancing classes with Michael?
Chris: Yeah?
Amanda: So we went yesterday and the instructor needed a partner to show the steps we had so far
Chris: so there's only one guy teaching you? without a female partner?
Amanda: Well, this time he was alone, BUT THAT'S NOT THE POINT! Listen!
Chris: yeah, sorry :D tell me!
Amanda: So he needed a partner and noone really knew the steps like perfectly
Amanda: and obviously noone wanted to be mocked
Amanda: so I thought, aaaah :D
Chris: u volunteered? really? you??
Amanda: yeah!
Chris: whooa! that's so great! #therapy #worthit :D
Amanda: yeah i know :D maybe one day i'll actually stop being so shy
Chris: that's definitely the first step! :D congrats!
Amanda: tx ^_^
Chris: what dance was it?
Amanda: English waltz
Chris: isn't it, like, SO difficult?
Amanda: yeah it is! but everyone said I looked like a pro :D
Chris: Well done!!
'''
        ],
        ["I have some exciting news to share!"],
        [
            '''
Beryl: Hello guys! How are you doing? We've lost contact for a few months now. Hope you are well.
Anton: A happy hello to you Beryl! Great to hear from you. We are fine, thanks. And yourself?
Beryl: I'm very well indeed. Thank you. Any changes in your setup?
Anton: Not really. SOS. Same Old Soup ;) But we are happy for that.
Beryl: Are you still running your lovely airbnb?
Anton: Oh yes, we are. We had a few months off during summer, our summer, but now bookings start flowing in. Well... Are you planning to visit us? You two are always welcome!
Beryl: You caught me here. I'm vaguely considering going down to Onrus again, most likely in January. What does it look like with vacancies then?
Anton: Perfect! Just give me your dates and I'll keep it booked for you.
Beryl: Would you prefer me to do it via airbnb website or just like this directly with you?
Anton: I think it'll be more advantageous for both of us to do it directly. Do you know exactly when you'll be coming?
Beryl: Not so much. Can I get back to you in 2, 3 days' time?
Anton: ASAP really. As I say we've been receiving bookings daily now.
Beryl: Well, no big deal. I'll be staying in Cape Town for a longer time and am quite flexible in my dates.
Anton: Will you be coming with Tino, if I may ask?
Beryl: No. I am single again. Hurray! So pls make it single occupancy any week in January, Anton.
Anton: Great! 4th till 12th?
Beryl: Very good. I'll call you beforehand from Cape Town. Greetings to you both!
Anton: Take care!'''
        ],
    ]
)

if __name__ == "__main__":
    interface.launch(debug=True, share=True)