Upload app-4.py

app-4.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+
+# Load the data and the trained model
+products = pd.read_csv("products_master.csv")
+model    = joblib.load("rf_model.joblib")
+
+# --- NEW: build short, readable labels for the dropdown ---
+def shorten(name, max_len=55):
+    # Use only the first comma-separated chunk (drops "8 HD Display, Wi-Fi, ..." stuff)
+    short = name.split(",")[0].strip()
+    # Keep color/edition if it appears at the end (e.g. "Tangerine", "Blue", "Pink")
+    parts = name.split(",")
+    if len(parts) > 1:
+        last = parts[-1].split("-")[0].strip()
+        # If the last chunk is short and looks like a color/variant, append it
+        if 0 < len(last) <= 25 and last.lower() != short.lower().split()[-1]:
+            short = f"{short} – {last}"
+    if len(short) > max_len:
+        short = short[:max_len-1] + "…"
+    return short
+
+# Build (label, value) pairs: label = short, value = full original name
+dropdown_choices = sorted(
+    [(shorten(n), n) for n in products["name"].tolist()],
+    key=lambda x: x[0]
+)
+# -----------------------------------------------------------
+
+def make_recommendation(product_name):
+    row = products[products["name"] == product_name].iloc[0]
+
+    # Build sentiment chart
+    fig, ax = plt.subplots(figsize=(5, 3))
+    counts = [row["pct_positive"], row["pct_neutral"], row["pct_negative"]]
+    colors = ["#2ecc71", "#95a5a6", "#e74c3c"]
+    ax.bar(["Positive", "Neutral", "Negative"], counts, color=colors, edgecolor="black")
+    ax.set_ylabel("% of reviews")
+    ax.set_title("Review sentiment breakdown")
+    ax.set_ylim(0, 1)
+
+    action = row["model_prediction"]
+    emoji  = {"Raise": "⬆️", "Hold": "➡️", "Drop": "⬇️"}[action]
+
+    summary = f"""
+### {shorten(row["name"], max_len=80)}
+
+**Brand:** {row["brand"]}  
+**Reviews analyzed:** {int(row["n_reviews"]):,}  
+**Average rating:** {row["avg_rating"]:.2f} / 5  
+**Average sentiment (VADER):** {row["avg_compound"]:.3f}
+
+---
+
+### {emoji} Recommendation: **{action} the price**
+
+| Metric | Current | Recommended |
+|---|---|---|
+| Price | ${row["current_price"]:.2f} | ${row["recommended_price"]:.2f} |
+| Profit per unit | ${row["current_profit_per_unit"]:.2f} | ${row["recommended_profit_per_unit"]:.2f} |
+
+**Estimated monthly profit change:** **${row["monthly_profit_change"]:,.2f}**
+"""
+    return summary, fig
+
+
+with gr.Blocks(title="Amazon Electronics Pricing Recommender") as demo:
+    gr.Markdown("# 🛒 Amazon Electronics Pricing Recommender")
+    gr.Markdown("Pick a product to see its sentiment breakdown and a price recommendation based on a Random Forest model trained on real reviews.")
+
+    product_dropdown = gr.Dropdown(
+        choices=dropdown_choices,
+        label="Select a product",
+        value=dropdown_choices[0][1],
+    )
+    btn = gr.Button("Get Recommendation", variant="primary")
+
+    with gr.Row():
+        output_text  = gr.Markdown()
+        output_chart = gr.Plot()
+
+    btn.click(make_recommendation, inputs=product_dropdown, outputs=[output_text, output_chart])
+    demo.load (make_recommendation, inputs=product_dropdown, outputs=[output_text, output_chart])
+
+demo.launch()