Upload app-5.py

app-5.py

@@ -0,0 +1,111 @@
+import gradio as gr
+import pandas as pd
+import joblib
+import re
+import matplotlib.pyplot as plt
+
+products = pd.read_csv("products_master.csv")
+model    = joblib.load("rf_model.joblib")
+
+def shorten(name, max_len=85):
+    # Base product = everything before first comma
+    base = name.split(",")[0].strip()
+
+    # Extract storage like "16 GB", "32 GB"
+    storage_match = re.search(r"(\d+)\s*GB", name)
+    storage = storage_match.group(0) if storage_match else ""
+
+    # Detect colour / variant (longer phrases first)
+    colors = [
+        "Marine Blue", "Pink Kid-Proof Case", "Blue Kid-Proof Case",
+        "Green Kid-Proof Case", "Tangerine", "Magenta",
+        "Black", "Blue", "Pink", "Green", "White", "Yellow", "Red"
+    ]
+    found_color = ""
+    for c in colors:
+        if c.lower() in name.lower():
+            found_color = c
+            break
+
+    # Detect ads vs ad-free
+    n_lower = name.lower()
+    if "without special offer" in n_lower or "no special offer" in n_lower:
+        offers = "Ad-Free"
+    elif "special offer" in n_lower:
+        offers = "with Ads"
+    else:
+        offers = ""
+
+    extras = [x for x in [storage, found_color, offers] if x]
+    label = base + (" – " + " / ".join(extras) if extras else "")
+
+    if len(label) > max_len:
+        label = label[:max_len-1] + "…"
+    return label
+
+# Build (label, value) pairs and de-duplicate any remaining collisions
+raw = [(shorten(n), n) for n in products["name"].tolist()]
+seen = {}
+dropdown_choices = []
+for label, full in raw:
+    if label in seen:
+        seen[label] += 1
+        label = f"{label} (v{seen[label]})"
+    else:
+        seen[label] = 1
+    dropdown_choices.append((label, full))
+dropdown_choices.sort(key=lambda x: x[0])
+
+def make_recommendation(product_name):
+    row = products[products["name"] == product_name].iloc[0]
+
+    fig, ax = plt.subplots(figsize=(5, 3))
+    counts = [row["pct_positive"], row["pct_neutral"], row["pct_negative"]]
+    ax.bar(["Positive", "Neutral", "Negative"],
+           counts, color=["#2ecc71", "#95a5a6", "#e74c3c"], 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=110)}
+
+**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()