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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()
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