Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Define model paths based on uploaded repo files
+MODEL_PATH = "trained_model2/distilroberta_model.pth"
+TOKENIZER_DIR = "trained_model2/distilroberta_tokenizer"
+
+# Load tokenizer
+tokenizer_rl = AutoTokenizer.from_pretrained(TOKENIZER_DIR)
+
+# Load model
+model_rl = AutoModelForSequenceClassification.from_pretrained('distilroberta-base', num_labels=2)
+model_rl.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
+model_rl.eval()
+
+# RL model classification function
+def classify_with_rl(text):
+    inputs = tokenizer_rl(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = model_rl(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        return {"spam_probability": max(0, min(1, float(probs[0][1])))}
+
+# Create API
+iface = gr.Interface(fn=classify_with_rl, inputs=gr.Textbox(), outputs="json")
+
+# Launch API
+if __name__ == "__main__":
+    iface.launch()