app.py

app.py

@@ -0,0 +1,43 @@
+# app.py
+
+# Install necessary libraries (This line is for local setup; Gradio Spaces should have dependencies set in the config)
+# !pip install transformers datasets scikit-learn accelerate gradio
+
+# Importing necessary libraries
+import gradio as gr
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+# Load the dataset
+ds = load_dataset("GonzaloA/fake_news")
+
+# Load pre-trained tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = AutoModelForSequenceClassification.from_pretrained('TeamQuad-fine-tuned-bert')
+
+# Create a classification pipeline
+classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
+# Define label mapping for fake news detection
+label_mapping = {0: 'fake', 1: 'true'}
+
+# Function to classify input text
+def classify_news(text):
+    result = classifier(text)
+    label = result[0]['label'].split('_')[1]  # Extract label from the model's output
+    score = result[0]['score']  # Confidence score
+    mapped_result = {'label': label_mapping[int(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
+if __name__ == "__main__":
+    iface.launch(share=True)