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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)
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