app.py

"""
Multilingual Sentiment Analysis (English • Urdu • Roman Urdu)
-------------------------------------------------------------
Features:
• Single text sentiment analysis with language hint.
• Batch analysis from CSV/XLSX file.
• 3-class output (Positive / Neutral / Negative) aggregated from 5-star scores.
• Saves logs to sentiment_logs.xlsx.
"""

import os
from datetime import datetime
import pandas as pd
import gradio as gr
from transformers import pipeline

# -------- Model & Pipeline --------
MODEL_NAME = "nlptown/bert-base-multilingual-uncased-sentiment"
clf = pipeline("sentiment-analysis", model=MODEL_NAME)

# -------- Logging setup --------
LOG_PATH = "sentiment_logs.xlsx"
if not os.path.exists(LOG_PATH):
    pd.DataFrame(columns=[
        "timestamp", "language_hint", "text",
        "predicted_label_3class", "confidence_3class",
        "stars_probs", "top_star_label"
    ]).to_excel(LOG_PATH, index=False)

# -------- Helper function: aggregate 5★ → 3-class --------
def _aggregate_to_3class(star_scores):
    scores = {d["label"].lower(): float(d["score"]) for d in star_scores}
    s1, s2, s3, s4, s5 = (
        scores.get("1 star", 0.0),
        scores.get("2 stars", 0.0),
        scores.get("3 stars", 0.0),
        scores.get("4 stars", 0.0),
        scores.get("5 stars", 0.0),
    )

    neg, neu, pos = s1 + s2, s3, s4 + s5
    probs3 = {"Negative": neg, "Neutral": neu, "Positive": pos}
    pred_label = max(probs3, key=probs3.get)
    confidence = probs3[pred_label]

    top_star_label = max(
        ["1 star", "2 stars", "3 stars", "4 stars", "5 stars"],
        key=lambda k: {"1 star": s1, "2 stars": s2, "3 stars": s3, "4 stars": s4, "5 stars": s5}[k]
    )
    return pred_label, confidence, probs3, top_star_label

# -------- Single text analysis --------
def analyze_single(text, lang_hint):
    if not text or not text.strip():
        return "❌ Please enter some text.", "", "", LOG_PATH

    star_results = clf(text, return_all_scores=True)[0]
    pred_label, conf, probs3, top_star = _aggregate_to_3class(star_results)

    polarity = {
        "Positive": "😊 Positive",
        "Neutral": "😐 Neutral",
        "Negative": "☹️ Negative",
    }[pred_label]

    # Log
    try:
        df = pd.read_excel(LOG_PATH)
    except Exception:
        df = pd.DataFrame(columns=[
            "timestamp", "language_hint", "text",
            "predicted_label_3class", "confidence_3class",
            "stars_probs", "top_star_label"
        ])

    new_row = {
        "timestamp": datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC"),
        "language_hint": lang_hint,
        "text": text,
        "predicted_label_3class": pred_label,
        "confidence_3class": round(conf, 4),
        "stars_probs": str({d["label"]: round(float(d["score"]), 4) for d in star_results}),
        "top_star_label": top_star,
    }
    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
    df.to_excel(LOG_PATH, index=False)

    return f"Sentiment: {pred_label}", f"Confidence: {conf:.3f}", f"Polarity: {polarity}", LOG_PATH

# -------- Batch analysis --------
def analyze_batch(file, lang_hint):
    if file is None:
        return "❌ Please upload a CSV/XLSX file.", None

    ext = os.path.splitext(file.name)[-1].lower()
    if ext == ".csv":
        df = pd.read_csv(file.name)
    elif ext in [".xls", ".xlsx"]:
        df = pd.read_excel(file.name)
    else:
        return "❌ Only CSV or Excel files are supported.", None

    if "text" not in df.columns:
        return "❌ The file must contain a 'text' column.", None

    results = []
    for t in df["text"]:
        if not isinstance(t, str) or not t.strip():
            results.append(("N/A", 0.0, "Invalid text"))
            continue
        star_results = clf(t, return_all_scores=True)[0]
        pred_label, conf, probs3, top_star = _aggregate_to_3class(star_results)
        results.append((pred_label, conf, top_star))

    df["predicted_label_3class"], df["confidence_3class"], df["top_star_label"] = zip(*results)
    out_path = "batch_results.xlsx"
    df.to_excel(out_path, index=False)

    return "✅ Batch analysis complete.", out_path

# -------- Gradio UI --------
with gr.Blocks() as demo:
    gr.Markdown(
        "## 🌍 Multilingual Sentiment Analysis (Positive • Neutral • Negative)\n"
        "**Languages:** English, Urdu, Roman Urdu  \n"
        "Model: `nlptown/bert-base-multilingual-uncased-sentiment` (mapped from 5★ → 3 classes)"
    )

    with gr.Tab("🔹 Single Text"):
        user_text = gr.Textbox(label="Enter text", placeholder="Type in English, Urdu, or Roman Urdu...")
        lang_dropdown = gr.Dropdown(["English", "Urdu", "Roman Urdu"], label="Language Hint", value="English")
        btn = gr.Button("Analyze")

        out_sent = gr.Textbox(label="Sentiment")
        out_conf = gr.Textbox(label="Confidence (0–1)")
        out_pol  = gr.Textbox(label="Polarity")
        out_file = gr.File(label="Download logs (.xlsx)")

        btn.click(analyze_single, inputs=[user_text, lang_dropdown],
                  outputs=[out_sent, out_conf, out_pol, out_file])

    with gr.Tab("🔹 Batch Upload"):
        gr.Markdown("Upload a CSV/XLSX file with a **'text'** column for batch sentiment analysis.")
        file_in = gr.File(label="Upload CSV/XLSX", file_types=[".csv", ".xlsx"])
        lang_dropdown_batch = gr.Dropdown(["English", "Urdu", "Roman Urdu"],
                                          label="Language Hint", value="English")
        btn_batch = gr.Button("Analyze Batch")

        batch_status = gr.Textbox(label="Status")
        batch_file   = gr.File(label="Download Batch Results")

        btn_batch.click(analyze_batch, inputs=[file_in, lang_dropdown_batch],
                        outputs=[batch_status, batch_file])

if __name__ == "__main__":
    demo.launch()