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Greek_Sentiment_Analysis
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gsar78 commited on
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1 Parent(s): 2ed3a46

Update app.py

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  1. app.py +112 -47
app.py CHANGED
@@ -1,50 +1,115 @@
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  import gradio as gr
 
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- def predict(text):
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- # Tokenize the input text
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- inputs = tokenizer(text, return_tensors="pt")
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- # Get the model outputs
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- outputs = model(**inputs)
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- # Apply softmax to the logits to get probabilities
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- scores = torch.nn.functional.softmax(outputs.logits, dim=1)
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- # Get the predicted label
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- predicted_label_idx = scores.argmax(dim=1).item()
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- labels = ["negative", "neutral", "positive"]
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- predicted_label = labels[predicted_label_idx]
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- confidence_score = scores[0, predicted_label_idx].item()
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- # Create a dictionary with the prediction and scores
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- result = {
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- "text": text,
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- "label": predicted_label,
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- "score": confidence_score,
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- "scores": {
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- "positive": scores[0, 2].item(),
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- "neutral": scores[0, 1].item(),
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- "negative": scores[0, 0].item()
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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- return result
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-
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- iface = gr.Interface(
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- fn=predict,
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- inputs="text",
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- outputs=[
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- gr.Textbox(label="Prediction"),
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- gr.Label(label="Label Confidence")
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- ],
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- title="Hellenic Sentiment AI",
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- description=None,
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- article=None,
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- theme="default",
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- flagging_dir=None,
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- css=None,
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- analytics_script=None,
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- allow_flagging="never",
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- allow_screenshot=True,
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- enable_queue=True,
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- show_input=True,
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- show_output=True,
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- footer="Development by Geo Sar"
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- )
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-
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- iface.launch(share=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  import gradio as gr
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+ from transformers import pipeline
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+ def sentiment_analysis_generate_text(text):
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+ # Define the model
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+ model_name = "gsar78/HellenicSentimentAI"
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+
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+ # Create the pipeline
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+ nlp = pipeline("sentiment-analysis", model=model_name)
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+ # Split the input text into individual sentences
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+ sentences = text.split('|')
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+ # Run the pipeline on each sentence and collect the results
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+ results = nlp(sentences)
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+ output = []
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+ for sentence, result in zip(sentences, results):
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+ output.append(f"Text: {sentence.strip()}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n")
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+
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+ # Join the results into a single string to return
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+ return "\n".join(output)
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+
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+
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+ def sentiment_analysis_generate_table(text):
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+ # Define the model
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+ model_name = "gsar78/HellenicSentimentAI"
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+ # Create the pipeline
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+ nlp = pipeline("sentiment-analysis", model=model_name)
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+ # Split the input text into individual sentences
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+ sentences = text.split('|')
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+
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+ # Generate the HTML table with enhanced colors and bold headers
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+ html = """
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+ <html>
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+ <head>
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+ <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/css/bootstrap.min.css">
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+ <style>
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+ .label {
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+ transition: .15s;
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+ border-radius: 8px;
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+ padding: 5px 10px;
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+ font-size: 14px;
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+ text-transform: uppercase;
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+ }
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+ .positive {
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+ background-color: rgb(54, 176, 75);
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+ color: white;
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+ }
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+ .negative {
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+ background-color: rgb(237, 83, 80);
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+ color: white;
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+ }
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+ .neutral {
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+ background-color: rgb(52, 152, 219);
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+ color: white;
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  }
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+ th {
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+ font-weight: bold;
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+ color: rgb(106, 38, 198);
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+ }
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+ </style>
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+ </head>
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+ <body>
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+ <table class="table table-striped">
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+ <thead>
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+ <tr>
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+ <th scope="col">Text</th>
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+ <th scope="col">Score</th>
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+ <th scope="col">Sentiment</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ """
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+ for sentence in sentences:
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+ result = nlp(sentence.strip())[0]
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+ text = sentence.strip()
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+ score = f"{result['score']:.4f}"
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+ sentiment = result['label']
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+
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+ # Determine the sentiment class
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+ if sentiment == "Positive":
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+ sentiment_class = "positive"
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+ elif sentiment == "Negative":
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+ sentiment_class = "negative"
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+ else:
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+ sentiment_class = "neutral"
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+
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+ # Generate table rows
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+ html += f'<tr><td>{text}</td><td>{score}</td><td><span class="label {sentiment_class}">{sentiment}</span></td></tr>'
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+
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+ html += """
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+ </tbody>
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+ </table>
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+ </body>
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+ </html>
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+ """
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+
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+ return html
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+
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+
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+ if __name__ == "__main__":
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+ iface = gr.Interface(
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+ sentiment_analysis_generate_table,
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+ gr.Textbox(placeholder="Enter sentence here..."),
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+ ["html"],
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+ title="Hellenic Sentiment AI",
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+ description="<p>A sentiment analysis model for primarily the Greek language</p>"
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+ "<p>Enter some text to see whether the sentiment is positive, neutral or negative.</p>",
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+ examples=[
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+ ['Η πικάντικη γεύση αυτής της σούπας λαχανικών ήταν ακριβώς αυτό που χρειαζόμουν σήμερα. Είχε μια ωραία γαργαλιστική αίσθηση χωρίς να είναι πολύ καυτερή.'],
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+ ['Η πίτσα ήταν καμένη και τα υλικά φθηνής ποιότητας. Σίγουρα δεν θα ξαναπαραγγείλω απ�� εκεί.']
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+ ],
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+ allow_flagging=False,
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+ examples_per_page=2,
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+ )
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+
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+ iface.launch()