Create app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import pandas as pd
+import os
+
+# Theme configuration - MUST BE FIRST STREAMLIT COMMAND
+st.set_page_config(
+    page_title="QDF Classifier",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="collapsed",
+    menu_items=None
+)
+
+MODEL_ID = "dejanseo/QDF"
+HF_TOKEN = os.getenv("HF_TOKEN")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, token=HF_TOKEN)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model.to(device)
+model.eval()
+
+def classify(prompt: str):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=-1).squeeze().cpu()
+        pred = torch.argmax(probs).item()
+        confidence = probs[pred].item()
+        return pred, confidence
+
+# Font and style overrides
+st.markdown("""
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap');
+
+        html, body, div, span, input, label, textarea, button, h1, h2, p, table {
+            font-family: 'Montserrat', sans-serif !important;
+        }
+
+        [class^="css-"], [class*=" css-"] {
+            font-family: 'Montserrat', sans-serif !important;
+        }
+
+        header {visibility: hidden;}
+    </style>
+""", unsafe_allow_html=True)
+
+# UI
+st.title("QDF Classifier")
+st.write("Built by [**Dejan AI**](https://dejan.ai)")
+st.write("This classifier determines whether query deserves freshness.")
+
+# Placeholder example prompts
+example_text = """how would a cat describe a dog
+how to reset a Nest thermostat
+write a poem about time
+is there a train strike in London today
+summarize the theory of relativity
+who won the champions league last year
+explain quantum computing to a child
+weather in tokyo tomorrow
+generate a social media post for Earth Day
+what is the latest iPhone model"""
+
+user_input = st.text_area(
+    "Enter one search query per line:",
+    placeholder=example_text
+)
+
+if st.button("Classify"):
+    raw_input = user_input.strip()
+    if raw_input:
+        prompts = [line.strip() for line in raw_input.split("\n") if line.strip()]
+    else:
+        prompts = [line.strip() for line in example_text.split("\n")]
+
+    if not prompts:
+        st.warning("Please enter at least one prompt.")
+    else:
+        info_box = st.info("Processing... results will appear below one by one.")
+        table_placeholder = st.empty()
+        results = []
+
+        for p in prompts:
+            with st.spinner(f"Classifying: {p[:50]}..."):
+                label, conf = classify(p)
+                results.append({
+                    "Prompt": p,
+                    "Grounding": "Yes" if label == 1 else "No",
+                    "Confidence": round(conf, 4)
+                })
+                df = pd.DataFrame(results)
+                table_placeholder.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,
+                )
+        info_box.empty()
+
+        # Promo message shown only after results
+        st.subheader("Working together.")
+        st.write("[**Schedule a call**](https://dejan.ai/call/) to see how we can help you.")