Update app.py

app.py

@@ -1,36 +1,36 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-
-# Set page config
-st.set_page_config(page_title="Fake News Detector", page_icon="📰")
-
-# Hugging Face model path (change this to your actual repo ID)
-MODEL_DIR = "ragkasi/bert-fake-news"
-
-@st.cache_resource
-def load_pipeline():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
-    return pipeline("text-classification", model=model, tokenizer=tokenizer)
-
-classifier = load_pipeline()
-
-# UI
-st.title("📰 Fake News Detector")
-st.markdown("Enter a news **headline** or **statement**, and this app will predict if it's **real** or **fake**.")
-
-news_input = st.text_area("✏️ News Text", height=150)
-
-if st.button("🔍 Check News"):
-    if news_input.strip():
-        result = classifier(news_input)[0]
-        label = result["label"]
-        score = result["score"]
-
-        # Adjust label display
-        if label == "LABEL_1":
-            st.error(f"🚨 Likely **Fake News** (Confidence: `{score:.2f}`)")
-        else:
-            st.success(f"✅ Likely **Real News** (Confidence: `{score:.2f}`)")
-    else:
-        st.warning("⚠️ Please enter a news statement.")
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from pathlib import Path
+
+# Set page config
+st.set_page_config(page_title="Fake News Detector")
+
+# Use local model directory relative to app.py
+MODEL_DIR = Path("models/bert-fake-news")
+
+@st.cache_resource
+def load_pipeline():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
+    return pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+classifier = load_pipeline()
+
+# UI
+st.title("Fake News Detector")
+st.markdown("Enter a news **headline** or **statement**, and this app will predict if it's **real** or **fake**.")
+
+news_input = st.text_area("News Text", height=150)
+
+if st.button("Check News"):
+    if news_input.strip():
+        result = classifier(news_input)[0]
+        label = result["label"]
+        score = result["score"]
+
+        if label == "LABEL_1":
+            st.error(f"Likely Fake News (Confidence: `{score:.2f}`)")
+        else:
+            st.success(f"Likely Real News (Confidence: `{score:.2f}`)")
+    else:
+        st.warning("Please enter a news statement.")