Create app.py

app.py

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