Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the pre-trained model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=5)  # Adjust num_labels
+
+# Define the function to get article suggestions
+def suggest_articles(case_details):
+    inputs = tokenizer(case_details, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    prediction = outputs.logits.argmax(dim=1).item()
+    return f"Suggested Article ID: {prediction}"
+
+# Build the Gradio interface
+interface = gr.Interface(
+    fn=suggest_articles,
+    inputs="text",
+    outputs="text",
+    title="Knowledge Article Suggestion",
+    description="Enter case details to get relevant article suggestions."
+)
+
+interface.launch()