Delete app(0,1).py

app(0,1).py

@@ -1,118 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, TextClassificationPipeline
-import torch
-import gradio as gr
-from openpyxl import load_workbook
-from numpy import mean
-
-# Load tokenizers and models
-tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
-model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
-
-tokenizer_keywords = AutoTokenizer.from_pretrained("transformer3/H2-keywordextractor")
-model_keywords = AutoModelForSeq2SeqLM.from_pretrained("transformer3/H2-keywordextractor")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-new_model = AutoModelForSequenceClassification.from_pretrained('roberta-rating')
-new_tokenizer = AutoTokenizer.from_pretrained('roberta-rating')
-
-classifier = TextClassificationPipeline(model=new_model, tokenizer=new_tokenizer, device=device)
-
-label_mapping = {1: '1/5', 2: '2/5', 3: '3/5', 4: '4/5', 5: '5/5'}
-
-# Function to parse Excel file
-def parse_xl(file_path):
-    cells = []
-
-    workbook = load_workbook(filename=file_path)
-    for sheet in workbook.worksheets:
-        for row in sheet.iter_rows():
-            for cell in row:
-                if cell.value != None:
-                    cells.append(cell.value)
-
-    return cells
-
-# Function to evaluate reviews from Excel file
-def evaluate(file):
-    reviews = parse_xl(file)
-    ratings = []
-    text = ""
-    sentiments = []
-
-    for review in reviews:
-        rating = int(classifier(review)[0]['label'].split('_')[1])
-        ratings.append(rating)
-        text += review
-        text += " "
-        
-        sentiment = classifier(review)[0]['label']
-        sentiment_label = "Positive" if sentiment == "LABEL_4" or sentiment == "LABEL_5" else "Negative" if sentiment == "LABEL_1" or sentiment == "LABEL_2" else "Neutral"
-        sentiments.append(sentiment_label)
-    
-    overall_sentiment = "Positive" if sentiments.count("Positive") > sentiments.count("Negative") else "Negative" if sentiments.count("Negative") > sentiments.count("Positive") else "Neutral"
-            
-    inputs = tokenizer([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=10, max_length=50)
-    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-
-    # Modify the summary to third person
-    summary = summary.replace("I", "He/She").replace("my", "his/her").replace("me", "him/her")
-
-    inputs_keywords = tokenizer_keywords([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids_keywords = model_keywords.generate(inputs_keywords["input_ids"], num_beams=2, min_length=0, max_length=100)
-    keywords = tokenizer_keywords.batch_decode(summary_ids_keywords, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]   
-
-    return round(mean(ratings), 2), summary, keywords, overall_sentiment
-
-# Function to test a single text input
-def test_area(text):
-    inputs = tokenizer([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=10, max_length=50)
-    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-
-    # Modify the summary to third person
-    summary = summary.replace("I", "He/She").replace("my", "his/her").replace("me", "him/her")
-
-    inputs_keywords = tokenizer_keywords([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids_keywords = model_keywords.generate(inputs_keywords["input_ids"], num_beams=2, min_length=0, max_length=100)
-    keywords = tokenizer_keywords.batch_decode(summary_ids_keywords, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]   
-
-    sentiment = classifier(text)[0]['label']
-    sentiment_label = "Positive" if sentiment == "LABEL_4" or sentiment == "LABEL_5" else "Negative" if sentiment == "LABEL_1" or sentiment == "LABEL_2" else "Neutral"
-    
-    rating = int(classifier(text)[0]['label'].split('_')[1])
-
-    return rating, summary, keywords, sentiment_label
-
-# Main interface
-main_interface = gr.Interface(
-    fn=evaluate,
-    inputs=gr.File(label="Reviews"),
-    outputs=[gr.Textbox(label="Overall Rating"), gr.Textbox(label="Summary"), gr.Textbox(label="Keywords"), gr.Textbox(label="Overall Sentiment")],
-    title='Summarize Reviews',
-    description="Evaluate and summarize collection of reviews. Reviews are submitted as an Excel file, where each review is in its own cell."
-)
-
-# Testing area interface
-testing_interface = gr.Interface(
-    fn=test_area,
-    inputs=gr.Textbox(label="Input Text"),
-    outputs=[gr.Textbox(label="Rating"), gr.Textbox(label="Summary"), gr.Textbox(label="Keywords"), gr.Textbox(label="Sentiment")],
-    title='Testing Area',
-    description="Test the summarization, keyword extraction, sentiment analysis, and rating on custom text input."
-)
-
-# Combine interfaces into a tabbed interface with a sidebar
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("## Sidebar")
-            gr.Button("Button 1")
-            gr.Button("Button 2")
-        with gr.Column(scale=4):
-            iface = gr.TabbedInterface(
-                [main_interface, testing_interface],
-                ["Summarize Reviews", "Testing Area"]
-            )
-
-demo.launch(share=True)