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import gradio as gr |
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from tensorflow.keras.models import load_model |
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from sentence_transformers import SentenceTransformer |
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import numpy as np |
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embedder = SentenceTransformer('all-MiniLM-L6-v2') |
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model = load_model("Model.h5") |
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def classify_sentiment(text): |
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embedding = embedder.encode(text, show_progress_bar=False) |
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embedding = np.expand_dims(embedding, axis=0) |
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pred = model.predict(embedding)[0][0] |
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label = "Positive" if pred > 0.5 else "Negative" |
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return f"Prediction: {label} (Score: {pred:.2f})" |
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interface = gr.Interface( |
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fn=classify_sentiment, |
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inputs=gr.Textbox(lines=2, placeholder="Enter a tweet..."), |
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outputs=gr.Textbox(), |
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title="Tweet Sentiment Classifier", |
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description="Uses all-MiniLM-L6-v2 to convert your text into a meaningful vector and then classifies it as positive or negative sentiment using a trained deep Sequential model. 👉 [View Source on GitHub](https://github.com/nishantksingh0/Twitter-Sentiment-Analysis)", |
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) |
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interface.launch() |
