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)