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| import gradio as gr | |
| import pandas as pd | |
| import nltk | |
| nltk.download('punkt_tab') | |
| from lowerated.rate.entity import Entity | |
| def rate_movies(reviews_text, review_file, column_name): | |
| reviews = [] | |
| if reviews_text: | |
| reviews = reviews_text.split("\n") | |
| elif review_file is not None: | |
| try: | |
| for file in review_file: | |
| if file.name.endswith('.csv'): | |
| df = pd.read_csv(file) | |
| elif file.name.endswith('.xlsx'): | |
| df = pd.read_excel(file) | |
| if column_name in df.columns: | |
| reviews.extend(df[column_name].tolist()) | |
| except Exception as e: | |
| return f"Error processing file: {str(e)}" | |
| if not reviews: | |
| return "No reviews provided." | |
| entity = Entity(name="Movie") | |
| ratings = entity.rate(reviews=reviews) | |
| ratings_df = pd.DataFrame([ratings]) | |
| # Extract LM6 score and format it for display | |
| lm6_score = ratings.get('LM6', 0) | |
| formatted_lm6 = f"<div style='color: orange; font-size: 48px; font-weight: bold; text-align: center;'>LM6 Rating: {lm6_score:.2f}</div>" | |
| return ratings_df, formatted_lm6 | |
| # Interface components | |
| with gr.Blocks(css=".gradio app { font-family: Arial; }") as demo: | |
| gr.Markdown("### Movie Ratings Calculator") | |
| with gr.Row(): | |
| text_input = gr.Textbox(label="Enter Reviews (line-separated)", placeholder="Enter one review per line", lines=10) | |
| file_input = gr.File( | |
| label="Upload a CSV or Excel file with reviews", | |
| file_types=["csv", "xlsx"], | |
| file_count="multiple" | |
| ) | |
| column_input = gr.Textbox(label="Column Name", placeholder="Enter the column name that contains the reviews") | |
| output_table = gr.Dataframe() | |
| output_lm6 = gr.HTML() | |
| button = gr.Button("Calculate Ratings") | |
| button.click( | |
| fn=rate_movies, | |
| inputs=[text_input, file_input, column_input], | |
| outputs=[output_table, output_lm6] | |
| ) | |
| demo.launch() | |