Delete interfaces/classification.py

interfaces/classification.py

@@ -1,119 +0,0 @@
-from gliner import GLiNER
-import gradio as gr
-
-model = GLiNER.from_pretrained("knowledgator/gliner-multitask-v1.0").to("cpu")
-
-PROMPT_TEMPLATE = """Classify the given text having the following classes: {}"""
-classification_examples = [
-    [
-        """
-"I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.
-
-The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.
-
-I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.
-
-However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.
-
-Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.""",
-        "positive review, negative review, neutral review",
-        0.5
-    ],
-    [
-        "I really enjoyed the pizza we had for dinner last night.",
-        "Food, Weather, Sports",
-        0.5
-    ],
-    [
-        "Das Kind spielt im Park und genießt die frische Luft.",
-        "Nature, Technology, Politics",
-        0.5
-    ],
-        [
-        """
-"Last night, we visited the new Italian restaurant downtown. The Margherita pizza was absolutely delightful, with a perfectly crisp crust and fresh basil. 
-
-However, the service was slow; it took over 20 minutes to take our order. The pasta arrived lukewarm, which was disappointing given the hype around this place. 
-
-On the bright side, the ambiance was cozy, and the wine selection was impressive. Overall, it was a mixed experience, but I might give it another try on a quieter evening."
-        """,
-        "Food Quality, Technology, Politics",
-        0.5
-    ],
-    [
-        """
-"Das Kind verbrachte den Nachmittag im Park und entdeckte einen kleinen Teich mit Enten. Es war wunderschön zu sehen, wie es die Natur erkundete. 
-
-Doch plötzlich störten die Geräusche einer Baustelle die ruhige Atmosphäre. Trotzdem spielte das Kind weiter, und ich genoss die frische Luft.
-
-Solche Momente zeigen, wie wichtig es ist, Kinder in der Natur aufwachsen zu lassen."
-        """,
-        "Outdoor Activities, Gaming, Artificial Intelligence",
-        0.5
-    ],
-    [
-        """
-"I recently attended a healthcare technology conference. The keynote speaker demonstrated how AI is revolutionizing diagnostics, making it possible to detect rare diseases with incredible accuracy.
-
-However, concerns about data privacy and ethical implications were also heavily discussed. Despite these challenges, the energy in the room was palpable as experts envisioned a future where AI saves millions of lives.
-
-It was an inspiring event that showcased the potential of combining technology and healthcare innovation."
-        """,
-        "Artificial Intelligence, Music, Sports",
-        0.5
-    ]
-]
-
-def prepare_prompts(text, labels):
-    labels_str = ', '.join(labels)
-    return PROMPT_TEMPLATE.format(labels_str) + "\n" + text
-
-def process(text, labels, threshold):
-    if not text.strip() or not labels.strip():
-        return {"text": text, "entities": []}
-    
-    labels = [label.strip() for label in labels.split(",")]
-    prompt = prepare_prompts(text, labels)
-    
-    predictions = model.run([prompt], ["match"], threshold=threshold)
-    entities = []
-
-    if predictions and predictions[0]:
-        for pred in predictions[0]:
-            entities.append({
-                "entity": "match",
-                "word": pred["text"],
-                "start": pred["start"],
-                "end": pred["end"],
-                "score": pred["score"]
-            })
-    
-    return {"text": prompt, "entities": entities}
-
-with gr.Blocks(title="Text Classification with Highlighted Labels") as classification_interface:
-    gr.Markdown("# Text Classification with Highlighted Labels")
-    
-    input_text = gr.Textbox(label="Input Text", placeholder="Enter text for classification")
-    input_labels = gr.Textbox(label="Labels (Comma-Separated)", placeholder="Enter labels separated by commas (e.g., Positive, Negative, Neutral)")
-    threshold = gr.Slider(0, 1, value=0.5, step=0.01, label="Threshold")
-    
-    output = gr.HighlightedText(label="Classification Results")
-    
-    submit_btn = gr.Button("Classify")
-    
-    examples = gr.Examples(
-        examples=classification_examples,
-        inputs=[input_text, input_labels, threshold],
-        outputs=output,
-        fn=process,
-        cache_examples=True
-    )
-    theme=gr.themes.Base()
-
-
-    input_text.submit(fn=process, inputs=[input_text, input_labels, threshold], outputs=output)
-    threshold.release(fn=process, inputs=[input_text, input_labels, threshold], outputs=output)
-    submit_btn.click(fn=process, inputs=[input_text, input_labels, threshold], outputs=output)
-
-if __name__ == "__main__":
-    classification_interface.launch()