Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load fine-tuned model & tokenizer
+model_path = "bert_resume_classifier"  # Change if saved elsewhere
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Label mapping
+label_map = {
+    0: "Advocate", 1: "Arts", 2: "Automation Testing", 3: "Blockchain",
+    4: "Business Analyst", 5: "Civil Engineer", 6: "Data Science", 7: "Database",
+    8: "DevOps Engineer", 9: "DotNet Developer", 10: "ETL Developer",
+    11: "Electrical Engineering", 12: "HR", 13: "Hadoop", 14: "Health and fitness",
+    15: "Java Developer", 16: "Mechanical Engineer", 17: "Network Security Engineer",
+    18: "Operations Manager", 19: "PMO", 20: "Python Developer",
+    21: "SAP Developer", 22: "Sales", 23: "Testing", 24: "Web Designing"
+}
+
+# Prediction Function
+def predict_resume_category(resume_text):
+    inputs = tokenizer(resume_text, truncation=True, padding=True, max_length=512, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    return f"Predicted Job Category: {label_map[predicted_class]}"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_resume_category,
+    inputs=gr.Textbox(lines=10, placeholder="Paste resume text here..."),
+    outputs="text",
+    title="Resume Job Category Predictor",
+    description="Enter resume text to classify the job category using BERT.",
+)
+
+# Launch
+iface.launch(share=True)  # Use share=True to get a public Gradio link