Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the tokenizer and model
+model_name = "roberta-large"  # Replace with your trained model if uploaded
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the prediction function
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    outputs = model(**inputs)
+    probabilities = torch.softmax(outputs.logits, dim=-1).tolist()[0]
+    labels = ["Speculating War Outcomes", "Discrediting Ukraine", "Praise of Russia"]  # Replace with your actual labels
+    predictions = {label: prob for label, prob in zip(labels, probabilities)}
+    return predictions
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text to classify..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="Narrative Classification",
+    description="Classify text into predefined narrative categories."
+)
+
+# Launch the app
+demo.launch()