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import torch
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline
import tempfile
import os
# ---------------- LOAD MODEL ----------------
print("Loading sentiment model...")
# model_path = (
# "../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
# "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13"
# )
analyzer = pipeline(
"text-classification",
model= "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
device = -1 # CPU
)
print("Model loaded.")
# ---------------- SINGLE REVIEW ----------------
def sentiment_analyzer(review):
result = analyzer(review)[0]
return f"{result['label']} (Confidence: {round(result['score'], 4)})"
# ---------------- EXCEL + VISUALIZATION ----------------
def analyze_excel(file):
df = pd.read_excel(file.name)
if "review" not in df.columns:
return None, None, None, None
sentiments = []
scores = []
for review in df["review"]:
result = analyzer(review)[0]
sentiments.append(result["label"])
scores.append(round(result["score"], 4))
df["sentiment"] = sentiments
df["confidence_score"] = scores
# ---- Sentiment counts ----
counts = df["sentiment"].value_counts()
# ---- Bar Chart ----
fig_bar = plt.figure()
counts.plot(
kind="bar",
color=["#22c55e", "#ef4444"],
xlabel="Sentiment",
ylabel="Number of Reviews",
title="Sentiment Distribution (Bar Chart)"
)
plt.xticks(rotation=0)
plt.tight_layout()
# ---- Pie Chart ----
fig_pie = plt.figure()
counts.plot(
kind="pie",
autopct="%1.1f%%",
startangle=90,
colors=["#22c55e", "#ef4444"],
ylabel="",
title="Sentiment Distribution (Pie Chart)"
)
plt.tight_layout()
# ---- Save Excel for download ----
tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx")
df.to_excel(tmp_file.name, index=False)
return df, fig_bar, fig_pie, tmp_file.name
# ---------------- GRADIO UI ----------------
with gr.Blocks(theme=gr.themes.Soft(), title="Gen AI Project 3: Sentiment Analyzer") as demo:
gr.Markdown("""
Gen AI Project 3: Sentiment Analyzer
Analyze **individual reviews** or **bulk Excel reviews** using a fine-tuned **DistilBERT** model.
Get **AI insights + visual analytics + downloadable results**.
""")
with gr.Tabs():
# -------- SINGLE REVIEW TAB --------
with gr.Tab("📝 Single Review"):
with gr.Row():
review_input = gr.Textbox(
label="Enter Review Text",
placeholder="e.g. I absolutely loved this product!",
lines=4
)
sentiment_output = gr.Textbox(label="Predicted Sentiment")
gr.Button("Analyze Sentiment 🚀").click(
fn=sentiment_analyzer,
inputs=review_input,
outputs=sentiment_output
)
# -------- EXCEL TAB --------
with gr.Tab("📊 Excel Upload & Analysis"):
file_input = gr.File(
label="Upload Excel File (.xlsx)",
file_types=[".xlsx"]
)
analyze_btn = gr.Button("Analyze Excel 📈")
df_output = gr.Dataframe(label="Sentiment Results Table")
bar_output = gr.Plot(label="Bar Chart")
pie_output = gr.Plot(label="Pie Chart")
download_output = gr.File(label="Download Analyzed Excel")
analyze_btn.click(
fn=analyze_excel,
inputs=file_input,
outputs=[df_output, bar_output, pie_output, download_output]
)
gr.Markdown("""
---
✅ **Tech Stack:** Python, Transformers, DistilBERT, Pandas, Matplotlib, Gradio
🚀 **Use Case:** Product reviews, customer feedback, social media sentiment
""")
demo.launch()
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