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import gradio as gr |
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import torch |
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import torch.nn.functional as F |
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from sentence_transformers import SentenceTransformer |
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revision = None |
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model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) |
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ref_texts = [ |
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"Theatro App: Hello John. Hey John. Hi John. Call John", |
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"Theatro App: Message John. Message for John. Leave a message for John", |
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"Theatro App: Play messages. Listen to messages", |
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"Theatro App: What time is it?", |
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"Theatro App: What time is it?", |
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"Theatro App: Cashier Backup. Backup Cashier. Register backup. Register assistance.", |
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"Theatro App: repeat", |
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"Theatro App: Check inventory", |
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"Theatro App: Check Sales", |
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"Theatro App: Curbside Pickup", |
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"Theatro App: Replay last message.", |
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"Theatro App: Post it. Post it for group" |
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"Theatro App: Announcement. Announcement for the group", |
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"Open question: This is about products sold in TractorSupply.", |
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"Open question: This is about pet care.", |
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"Open question: What is the weather like?", |
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"Open question: What's 15% off from $79.99?", |
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"Open question: Can you look up the skew for 1091784?", |
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] |
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ref_embeddings = model.encode(ref_texts, convert_to_tensor=True) |
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def find_query_type(query): |
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query_embeddings = model.encode([query], convert_to_tensor=True) |
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scores = F.cosine_similarity(query_embeddings, ref_embeddings, dim=-1) |
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max_index = torch.argmax(scores).item() |
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ref_text = ref_texts[max_index] |
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query_type = ref_text.split(": ")[0] |
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return query_type |
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import gradio as gr |
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def predict(query): |
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query_type = find_query_type(query) |
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return query_type |
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iface = gr.Interface(fn=predict, |
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inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."), |
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outputs="text", |
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title="Query Type Classifier", |
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description="This model classifies the type of your query. Just input your query and get the predicted category.") |
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iface.launch() |
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