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| import gradio as gr | |
| from transformers import pipeline | |
| import os | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-sentiment") | |
| def sentiment_analysis_generate_text(text): | |
| # Define the model | |
| model_name = "gsar78/HellenicSentimentAI" | |
| # Create the pipeline | |
| nlp = pipeline("sentiment-analysis", model=model_name) | |
| # Split the input text into individual sentences | |
| sentences = text.split('|') | |
| # Run the pipeline on each sentence and collect the results | |
| results = nlp(sentences) | |
| output = [] | |
| for sentence, result in zip(sentences, results): | |
| output.append(f"Text: {sentence.strip()}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n") | |
| # Join the results into a single string to return | |
| return "\n".join(output) | |
| def sentiment_analysis_generate_table(text): | |
| # Define the model | |
| model_name = "gsar78/HellenicSentimentAI" | |
| # Create the pipeline | |
| nlp = pipeline("sentiment-analysis", model=model_name) | |
| # Split the input text into individual sentences | |
| sentences = text.split('|') | |
| # Generate the HTML table with enhanced colors and bold headers | |
| html = """ | |
| <html> | |
| <head> | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/css/bootstrap.min.css"> | |
| <style> | |
| .label { | |
| transition: .15s; | |
| border-radius: 8px; | |
| padding: 5px 10px; | |
| font-size: 14px; | |
| text-transform: uppercase; | |
| } | |
| .positive { | |
| background-color: rgb(54, 176, 75); | |
| color: white; | |
| } | |
| .negative { | |
| background-color: rgb(237, 83, 80); | |
| color: white; | |
| } | |
| .neutral { | |
| background-color: rgb(255, 165, 0); | |
| color: white; | |
| } | |
| th { | |
| font-weight: bold; | |
| color: rgb(106, 38, 198); | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <table class="table table-striped"> | |
| <thead> | |
| <tr> | |
| <th scope="col">Text</th> | |
| <th scope="col">Score</th> | |
| <th scope="col">Sentiment</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| """ | |
| for sentence in sentences: | |
| result = nlp(sentence.strip())[0] | |
| text = sentence.strip() | |
| score = f"{result['score']:.4f}" | |
| sentiment = result['label'] | |
| # Determine the sentiment class | |
| if sentiment.lower() == "positive": | |
| sentiment_class = "positive" | |
| elif sentiment.lower() == "negative": | |
| sentiment_class = "negative" | |
| else: | |
| sentiment_class = "neutral" | |
| # Generate table rows | |
| html += f'<tr><td>{text}</td><td>{score}</td><td><span class="label {sentiment_class}">{sentiment}</span></td></tr>' | |
| html += """ | |
| </tbody> | |
| </table> | |
| </body> | |
| </html> | |
| """ | |
| return html | |
| if __name__ == "__main__": | |
| iface = gr.Interface( | |
| fn=sentiment_analysis_generate_table, | |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), | |
| outputs=gr.HTML(), | |
| title="Hellenic Sentiment AI", | |
| description="A sentiment analysis model, primarily for the Greek language.<br>" | |
| "Type in some text to see its sentiment classification: positive, neutral, or negative.<br>" | |
| "Multiple sentences can be classified when separated by the | character.<br>" | |
| "For Emotion & Sentiment Classification visit Version 2.0: <a href='https://gsar78-hellenicsentimentai-v2.hf.space' target='_blank'>Hellenic Sentiment AI v2</a><br>" | |
| "Version 1.1 - Developed by GeoSar", | |
| examples=[ | |
| ["Η πικάντικη γεύση αυτής της σούπας λαχανικών ήταν ακριβώς αυτό που χρειαζόμουν σήμερα. Είχε μια ωραία γαργαλιστική αίσθηση χωρίς να είναι πολύ καυτερή."], | |
| ["Η πίτσα ήταν καμένη και τα υλικά φθηνής ποιότητας. Σίγουρα δεν θα ξαναπαραγγείλω από εκεί."] | |
| ], | |
| allow_flagging="manual", | |
| flagging_options=["Incorrect", "Ambiguous"], | |
| flagging_callback=hf_writer, | |
| examples_per_page=2, | |
| allow_duplication=False, | |
| concurrency_limit="default" | |
| ) | |
| iface.launch(share=True) |