Upload app.py

app.py

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+
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModel
+import gradio as gr
+
+class CustomTinyBERTClassifier(nn.Module):
+    def __init__(self, model_name='huawei-noah/TinyBERT_General_4L_312D', extra_feat_dim=4, num_labels=2):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained(model_name)
+        self.dropout = nn.Dropout(0.3)
+        hidden_size = self.bert.config.hidden_size
+        self.classifier = nn.Linear(hidden_size + extra_feat_dim, num_labels)
+
+    def forward(self, input_ids, attention_mask, additional_features):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        cls_output = outputs.last_hidden_state[:, 0]
+        combined = torch.cat((cls_output, additional_features), dim=1)
+        combined = self.dropout(combined)
+        logits = self.classifier(combined)
+        return logits
+
+model_name = "huawei-noah/TinyBERT_General_4L_312D"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = CustomTinyBERTClassifier(model_name=model_name, extra_feat_dim=4, num_labels=2)
+model.load_state_dict(torch.load("custom_fake_job_model.pt", map_location=torch.device("cpu")))
+model.eval()
+
+def predict_job(text, telecommuting, has_logo, has_questions, employment_type):
+    encoded = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    input_ids = encoded["input_ids"]
+    attention_mask = encoded["attention_mask"]
+    additional_features = torch.tensor([[telecommuting, has_logo, has_questions, employment_type]], dtype=torch.float32)
+
+    with torch.no_grad():
+        logits = model(input_ids=input_ids, attention_mask=attention_mask, additional_features=additional_features)
+        probs = torch.softmax(logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][pred].item()
+
+    label = "🚨 Fake Job" if pred == 1 else "✅ Legit Job"
+    result = (
+        f"{label} (Confidence: {confidence:.2f})\n"
+        f"Probabilities - Legit: {probs[0][0]:.3f}, Fake: {probs[0][1]:.3f}\n"
+        f"Raw logits: {logits.squeeze().tolist()}"
+    )
+    return result
+
+demo = gr.Interface(
+    fn=predict_job,
+    inputs=[
+        gr.Textbox(lines=5, label="Job Description"),
+        gr.Slider(0, 1, step=1, label="Telecommuting (0 = No, 1 = Yes)"),
+        gr.Slider(0, 1, step=1, label="Has Company Logo (0 = No, 1 = Yes)"),
+        gr.Slider(0, 1, step=1, label="Has Questions (0 = No, 1 = Yes)"),
+        gr.Slider(0, 5, step=1, label="Employment Type (0–5)")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Fake Job Detector",
+    description="Detects fake job postings using TinyBERT and additional features"
+)
+
+demo.launch()