Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import pandas as pd
+
+# Constants
+MODEL_ID = "dejanseo/substance"
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+    model.eval()
+    return tokenizer, model
+
+def classify(text, tokenizer, model):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=1).squeeze()
+        pred = torch.argmax(probs).item()
+        confidence = probs[pred].item()
+        return pred, confidence
+
+# --- UI ---
+st.set_page_config(layout="wide")
+st.title("Content Substance Classifier")
+st.markdown("This tool estimates the likelihood that content is thin or lacking in substance, using our [deep learning model](https://dejan.ai/blog/content-substance-classification/).")
+
+tokenizer, model = load_model()
+
+pasted_text = st.text_area("Enter full text for granular page analysis:", height=100)
+run = st.button("🚀 Run Analysis", use_container_width=True)
+
+if not pasted_text.strip():
+    st.info("Enter some text above, then click **Run Analysis**.")
+
+# --- Classify Text ---
+if run and pasted_text.strip():
+    st.markdown("### Results: Text Classification")
+    lines = [line.strip() for line in pasted_text.strip().split("\n") if line.strip()]
+    results = []
+    for line in lines:
+        label, conf = classify(line, tokenizer, model)
+        results.append({
+            "Text": line,
+            "Contains Thin Content": "Yes" if label == 0 else "No",
+            "Confidence": round(conf, 4)
+        })
+    df = pd.DataFrame(results)
+    st.data_editor(
+        df,
+        column_config={
+            "Confidence": st.column_config.ProgressColumn(
+                label="Confidence",
+                min_value=0.0,
+                max_value=1.0,
+                format="%.4f"
+            )
+        },
+        hide_index=True,
+        use_container_width=True
+    )