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7cfd42e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | 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()
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