oled version

app.py

@@ -1,43 +1,51 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from peft import PeftModel
 import torch
-# Load model and tokenizer with adapter 
+
+# Load model and tokenizer with adapter
 @st.cache_resource
 def load_model():
     base_model = "Qwen/Qwen3-0.6B"
     adapter_path = "faizabenatmane/Qwen-3-0.6B"
 
     tokenizer = AutoTokenizer.from_pretrained(base_model)
-    base = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
+
+    base = AutoModelForSequenceClassification.from_pretrained(
+        base_model,
+        num_labels=2,
+        device_map="cpu"
+    )
 
     model = PeftModel.from_pretrained(base, adapter_path)
     model = model.merge_and_unload()
 
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    # ✅ Text classification pipeline
+    pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
     return pipe
 
-generator = load_model()
+# Load pipeline once
+classifier = load_model()
 
-# Streamlit UI 
-st.title("📰 Fake News Detection (Text Generation)")
+# Streamlit UI
+st.title("📰 Fake News Detection")
 text = st.text_area("Enter a news statement or claim:", height=200)
 
 if st.button("Check"):
     with st.spinner("Analyzing..."):
-        prompt = f"Is the following statement real or fake?\n\n{text}\n\nAnswer:"
-        output = generator(prompt, max_length=50, do_sample=False)[0]['generated_text']
-        answer = output.split("Answer:")[-1].strip().split()[0].lower()
+        # Get classification result
+        result = classifier(text)[0]
+        label = result['label']
+        score = result['score']
 
-        if "real" in answer:
-            emoji = "✅"
+        # Optional: format label nicely
+        if "1" in label or "POSITIVE" in label.upper():
             verdict = "Real"
-        elif "fake" in answer:
-            emoji = "❌"
-            verdict = "Fake"
+            emoji = "✅"
         else:
-            emoji = "🤔"
-            verdict = f"Unclear: {answer}"
+            verdict = "Fake"
+            emoji = "❌"
 
+        # Show result
         st.subheader("Prediction")
-        st.success(f"{emoji} The statement is likely: **{verdict}**")
+        st.success(f"{emoji} The statement is likely: **{verdict}** (confidence: {score:.2f})")