Create app.py
Browse files
app.py
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import streamlit as st
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import torch
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from transformers import AlbertTokenizer, AlbertForSequenceClassification, AlbertConfig
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import plotly.graph_objects as go
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# URL of the logo
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logo_url = "https://dejan.ai/wp-content/uploads/2024/02/dejan-300x103.png"
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# Display the logo at the top using st.logo
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st.logo(logo_url, link="https://dejan.ai")
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# Streamlit app title and description
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st.title("Search Query Form Classifier")
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st.write("Ambiguous search queries are candidates for query expansion. Our model identifies such queries with an 80 percent accuracy and is deployed in a batch processing pipeline directly connected with Google Search Console API. In this demo you can test the model capability by testing individual queries.")
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st.write("Enter a query to check if it's well-formed:")
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# Load the model and tokenizer from the /model/ directory
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model_dir = 'model'
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tokenizer = AlbertTokenizer.from_pretrained(model_dir)
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config = AlbertConfig.from_pretrained(model_dir)
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model = AlbertForSequenceClassification.from_pretrained(model_dir, config=config)
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# Set the model to evaluation mode
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model.eval()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model.to(device)
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# User input
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user_input = st.text_input("Query:", "What is?")
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st.write("Developed by [Dejan AI](https://dejan.ai/blog/search-query-quality-classifier/)")
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def classify_query(query):
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# Tokenize input
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inputs = tokenizer.encode_plus(
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query,
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add_special_tokens=True,
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max_length=32,
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padding='max_length',
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truncation=True,
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return_attention_mask=True,
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return_tensors='pt'
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)
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input_ids = inputs['input_ids'].to(device)
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attention_mask = inputs['attention_mask'].to(device)
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# Perform inference
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with torch.no_grad():
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outputs = model(input_ids, attention_mask=attention_mask)
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logits = outputs.logits
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softmax_scores = torch.softmax(logits, dim=1).cpu().numpy()[0]
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confidence = softmax_scores[1] * 100 # Confidence for well-formed class
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return confidence
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# Check and display classification
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if user_input:
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confidence = classify_query(user_input)
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# Plotly gauge
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fig = go.Figure(go.Indicator(
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mode="gauge+number",
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value=confidence,
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title={'text': "Well-formedness Confidence"},
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gauge={
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'axis': {'range': [0, 100]},
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'bar': {'color': "darkblue"},
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'steps': [
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{'range': [0, 50], 'color': "red"},
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{'range': [50, 100], 'color': "green"}
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],
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'threshold': {
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'line': {'color': "black", 'width': 4},
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'thickness': 0.75,
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'value': confidence
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}
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}
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))
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st.plotly_chart(fig)
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if confidence >= 50:
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st.success(f"The query is likely well-formed with {confidence:.2f}% confidence.")
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else:
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st.error(f"The query is likely not well-formed with {100 - confidence:.2f}% confidence.")
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