| from pathlib import Path |
| import sys |
| from types import SimpleNamespace |
|
|
| import gradio as gr |
|
|
| try: |
| import spaces |
| except ImportError: |
| def _gpu_decorator(fn=None, *args, **kwargs): |
| def decorator(func): |
| return func |
|
|
| if fn is None: |
| return decorator |
| if callable(fn): |
| return fn |
| return decorator |
|
|
| spaces = SimpleNamespace(GPU=_gpu_decorator) |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent / "src")) |
|
|
| from multilingual_sentiment_analysis.infer import predict, predict_batch |
|
|
|
|
| @spaces.GPU |
| def analyze_single(text: str): |
| if not text or not text.strip(): |
| return "⚠️ Please enter some text", "" |
| try: |
| result = predict(text.strip()) |
| emoji = {"positive": "🟢", "neutral": "🟡", "negative": "🔴"}.get(result["label"], "⚪") |
| return f"{emoji} {result['label'].upper()}", f"{result['confidence'] * 100:.1f}%" |
| except Exception as error: |
| return f"❌ Error: {error}", "" |
|
|
|
|
| def analyze_batch(batch_text: str): |
| if not batch_text or not batch_text.strip(): |
| return "⚠️ Please enter texts (one per line)", [] |
| texts = [line.strip() for line in batch_text.splitlines() if line.strip()] |
| if not texts: |
| return "⚠️ Please enter at least one text", [] |
| try: |
| results = predict_batch(texts) |
| emojis = {"positive": "🟢", "neutral": "🟡", "negative": "🔴"} |
| rows = [ |
| [text[:70] + "..." if len(text) > 70 else text, |
| f"{emojis.get(result['label'], '⚪')} {result['label'].upper()}", |
| f"{result['confidence'] * 100:.1f}%"] |
| for text, result in zip(texts, results) |
| ] |
| sentiments = [result["label"] for result in results] |
| summary = ( |
| f"✅ Analyzed {len(texts)} texts | 😊 Positive: {sentiments.count('positive')} | " |
| f"😐 Neutral: {sentiments.count('neutral')} | 😞 Negative: {sentiments.count('negative')}" |
| ) |
| return summary, rows |
| except Exception as error: |
| return f"❌ Error: {error}", [] |
|
|
|
|
| custom_theme = gr.themes.Base(primary_hue="cyan", secondary_hue="slate").set( |
| body_background_fill="#000000", body_text_color="#00FFFF", |
| button_primary_background_fill="#00FFFF", button_primary_text_color="#000000", |
| button_primary_background_fill_hover="#00DDDD", block_title_text_color="#00FFFF", |
| block_label_text_color="#00FFFF", input_background_fill="#111111", |
| input_border_color="#00FFFF", input_placeholder_color="#666666", border_color_primary="#00FFFF", |
| ) |
|
|
| with gr.Blocks(title="🌍 Multilingual Sentiment Analysis", theme=custom_theme) as demo: |
| gr.Markdown("# 🌍 Multilingual Sentiment Analysis") |
| gr.Markdown("Analyze sentiment using a fine-tuned XLM-RoBERTa model.") |
| with gr.Tabs(): |
| with gr.TabItem("📝 Single Text"): |
| with gr.Row(): |
| with gr.Column(scale=3): |
| text_input = gr.Textbox(label="Enter text to analyze", placeholder="Type something to analyze...", lines=4) |
| with gr.Column(scale=1): |
| analyze_btn = gr.Button("🔍 Analyze", size="lg", variant="primary") |
| with gr.Row(): |
| sentiment_output = gr.Textbox(label="Sentiment", interactive=False) |
| confidence_output = gr.Textbox(label="Confidence", interactive=False) |
| analyze_btn.click(analyze_single, inputs=text_input, outputs=[sentiment_output, confidence_output]) |
| with gr.TabItem("📚 Batch Analysis"): |
| batch_input = gr.Textbox(label="Enter multiple texts (one per line)", placeholder="Text 1...\nText 2...", lines=8) |
| batch_btn = gr.Button("🚀 Batch Analyze", size="lg", variant="primary") |
| batch_summary = gr.Textbox(label="Summary", interactive=False) |
| batch_results = gr.Dataframe(headers=["Text", "Sentiment", "Confidence"], label="Results", interactive=False) |
| batch_btn.click(analyze_batch, inputs=batch_input, outputs=[batch_summary, batch_results]) |
| gr.Markdown("---\nBuilt with ❤️ using Gradio • XLM-RoBERTa") |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
