Delete app.py

app.py

@@ -1,84 +0,0 @@
-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 Hugging Face Model Hub
-model_name = 'dejanseo/Query-Quality-Classifier'
-tokenizer = AlbertTokenizer.from_pretrained(model_name)
-model = AlbertForSequenceClassification.from_pretrained(model_name)
-
-# 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.")