Delete app (1).py

app (1).py

@@ -1,55 +0,0 @@
-import pandas as pd
-import gradio as gr
-
-# Load final dataset
-df = pd.read_csv("amazon_final_model_dataset.csv")
-
-# Keep only useful columns if they exist
-useful_columns = [
-    "product_name",
-    "avg_rating",
-    "avg_sentiment",
-    "avg_price",
-    "monthly_sales",
-    "monthly_revenue",
-    "monthly_profit",
-    "success_probability",
-    "business_recommendation"
-]
-
-existing_columns = [col for col in useful_columns if col in df.columns]
-df = df[existing_columns].copy()
-
-# Remove duplicates on product name for cleaner dropdown
-df = df.drop_duplicates(subset=["product_name"]).sort_values("product_name")
-
-def analyze_product(product_name):
-    row = df[df["product_name"] == product_name].iloc[0]
-
-    result = f"""
-### Product analysis
-
-**Product:** {row['product_name']}
-
-**Average rating:** {row.get('avg_rating', 'N/A'):.2f}  
-**Average sentiment:** {row.get('avg_sentiment', 'N/A'):.3f}  
-**Average price:** ${row.get('avg_price', 'N/A'):.2f}  
-**Monthly sales:** {int(row.get('monthly_sales', 0))}  
-**Monthly revenue:** ${row.get('monthly_revenue', 0):.2f}  
-**Monthly profit:** ${row.get('monthly_profit', 0):.2f}  
-**Success probability:** {row.get('success_probability', 0):.2%}  
-
-**Business recommendation:**  
-{row.get('business_recommendation', 'No recommendation available')}
-"""
-    return result
-
-demo = gr.Interface(
-    fn=analyze_product,
-    inputs=gr.Dropdown(choices=df["product_name"].tolist(), label="Choose a product"),
-    outputs=gr.Markdown(label="Analysis"),
-    title="Amazon Product Success Analyzer",
-    description="This app analyzes Amazon products using sentiment, ratings, synthetic business metrics, and predicted success probability."
-)
-
-demo.launch()