Delete app.py

app.py

@@ -1,182 +0,0 @@
-!pip install transformers joblib contractions gradio re huggingface_hub scikit-learn
-
-import gradio as gr
-from transformers import pipeline, AutoTokenizer
-import re
-import contractions
-import joblib
-from sklearn.feature_extraction.text import TfidfVectorizer
-from huggingface_hub import hf_hub_download
-
-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)
-
-
-vectorizer_filename = "tfidf_vectorizer.joblib"
-
-vectorizer_path = hf_hub_download(repo_id=repo_id, filename=vectorizer_filename)
-loaded_tfidf_vectorizer = joblib.load(vectorizer_path)
-
-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: noo ;) but close enough! 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)