app.py

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()