Update app.py

app.py

@@ -2,63 +2,55 @@ import gradio as gr
 import pandas as pd
 from lowerated.rate.entity import Entity
 
-def clean_reviews(text_reviews):
-    # Clean and split the reviews
-    reviews = text_reviews.strip().replace("\n", ", ").split(",")
-    reviews = [review.strip() for review in reviews if review.strip()]
-    return reviews
-
-def scale_rating(value, min_rating=-1, max_rating=10):
-    # Scale the rating to the range -1 to 10
-    return ((value - min_rating) / (max_rating - min_rating)) * 200 - 100
-
-def rate_reviews(file, review_column, text_reviews):
+def rate_movies(reviews_text, review_file, column_name):
     reviews = []
-    if file is not None and review_column:
-        df = pd.read_csv(file.name)
-        if review_column in df.columns:
-            df[review_column] = df[review_column].dropna()
-            reviews.extend(df[review_column].astype(str).tolist())
-        else:
-            return "Invalid column name", "| Aspect | Rating |\n| --- | --- |\n| No data | No data |"
-
-    if text_reviews:
-        reviews.extend(clean_reviews(text_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 to rate", "| Aspect | Rating |\n| --- | --- |\n| No data | No data |"
-
+        return "No reviews provided."
+    
     entity = Entity(name="Movie")
-    try:
-        rating = entity.rate(reviews=reviews)
-    except Exception as e:
-        return f"Error in rating: {e}", "| Aspect | Rating |\n| --- | --- |\n| No data | No data |"
-
-    lm6_rating = rating.get("LM6")
-    lm6_rating_scaled = scale_rating(lm6_rating) if lm6_rating is not None else "No LM6 rating available"
-    aspects_output = "| Aspect | Rating |\n| --- | --- |\n"
-    for aspect, value in rating.items():
-        if aspect != "LM6":
-            scaled_value = scale_rating(value)
-            aspects_output += f"| {aspect} | {scaled_value:.2f}% |\n"
-
-    return f"{lm6_rating_scaled:.2f}%", aspects_output
-
-with gr.Blocks(css=".input, .output {padding: 10px;} .label {font-weight: bold;}") as app:
+    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():
-        with gr.Column(scale=1):
-            file_input = gr.File(label="Upload CSV")
-            column_input = gr.Textbox(label="Enter Review Column Name")
-        with gr.Column(scale=1):
-            text_reviews = gr.Textbox(lines=10, placeholder="Enter comma-separated reviews here...", label="Text Reviews")
-
-    output_lm6 = gr.Textbox(label="LM6 Rating")
-    output_aspects = gr.Markdown(label="Aspect Ratings")
-
-    gr.Button("Rate Reviews").click(
-        fn=rate_reviews,
-        inputs=[file_input, column_input, text_reviews],
-        outputs=[output_lm6, output_aspects]
+        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]
     )
 
-app.launch(share=True)
+demo.launch()