app.py

app.py

@@ -0,0 +1,34 @@
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
+import gradio as gr
+# Load the fine-tuned model and tokenizer
+new_model = AutoModelForSequenceClassification.from_pretrained('TeamQuad-fine-tuned-bert')
+new_tokenizer = AutoTokenizer.from_pretrained('TeamQuad-fine-tuned-bert')
+
+# Create a classification pipeline
+classifier = TextClassificationPipeline(model=new_model, tokenizer=new_tokenizer)
+
+# Add label mapping for fake news detection (assuming LABEL_0 = 'fake' and LABEL_1 = 'true')
+label_mapping = {'LABEL_0': 'fake', 'LABEL_1': 'true'}
+
+# Function to classify input text
+def classify_news(text):
+    result = classifier(text)
+    # Extract the label and score
+    label = result[0]['label']  # 'LABEL_0' or 'LABEL_1'
+    score = result[0]['score']  # Confidence score
+    mapped_result = {'label': label_mapping[label], 'score': score}
+    return f"Label: {mapped_result['label']}, Score: {mapped_result['score']:.4f}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_news,  # The function to process the input
+    inputs=gr.Textbox(lines=10, placeholder="Enter a news headline or article to classify..."),
+    outputs="text",  # Output will be displayed as text
+    title="Fake News Detection",
+    description="Enter a news headline or article and see whether the model classifies it as 'Fake News' or 'True News'.",
+   
+)
+
+# Launch the interface
+iface.launch(share=True)