app.py

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, TextClassificationPipeline
import torch
import gradio as gr
from openpyxl import load_workbook
from numpy import mean

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")
# Load the fine-tuned model and tokenizer
new_model = AutoModelForSequenceClassification.from_pretrained('roberta-rating')
new_tokenizer = AutoTokenizer.from_pretrained('roberta-rating')


# Create a classification pipeline
classifier = TextClassificationPipeline(model=new_model, tokenizer=new_tokenizer, device=device)

# Add label mapping for sentiment analysis
label_mapping = {1: '1/5', 2: '2/5', 3: '3/5', 4: '4/5', 5: '5/5'}

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

def evaluate(file):
    reviews = parse_xl(file)
    ratings = []
    text = ""

    for review in reviews:
        ratings.append(int(classifier(review)[0]['label'].split('_')[1]))
        text += review
        text += " "
            
    inputs = tokenizer([text], max_length=1024, truncation=True, return_tensors="pt")
    summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=50, max_length=1000)
    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

    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

iface = gr.Interface(
    fn=evaluate,
    inputs=gr.File(label="Reviews", file_types=[".xlsx", ".xlsm", ".xltx", ".xltm"]),
    outputs=[gr.Textbox(label="Rating"), gr.Textbox(label="Summary"), gr.Textbox(label="Keywords")],
    title='Summarize Reviews',
    description="Evaluate and summarize collection of reviews. Reviews are submitted as an Excel file, where each reviews is in its own cell."
)

iface.launch(share=True)