Update app.py

app.py

@@ -45,60 +45,60 @@ async def predict(request: TextRequest):
         raise HTTPException(status_code=500, detail=f"Prediction failed: {e}")
 
 
-# @app.post("/batch_predict")
-# async def batch_predict(request: BatchTextRequest):
-#     try:
-#         model.eval()
-#         results = []
-#         for idx, text in enumerate(request.texts):
-#             inputs = tokenizer.encode_plus(
-#                 text,
-#                 add_special_tokens=True,
-#                 max_length=64,
-#                 truncation=True,
-#                 padding='max_length',
-#                 return_attention_mask=True,
-#                 return_tensors='pt'
-#             )
-#             with torch.no_grad():
-#                 logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
-#                 prediction = torch.argmax(logits, dim=1).item()
-#                 results.append({"id": idx + 1, "text": text, "prediction": "Spam" if prediction == 1 else "Ham"})
-#         return {"results": results}
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=f"Batch prediction failed: {e}")
 @app.post("/batch_predict")
 async def batch_predict(request: BatchTextRequest):
     try:
         model.eval()
+        results = []
+        for idx, text in enumerate(request.texts):
+            inputs = tokenizer.encode_plus(
+                text,
+                add_special_tokens=True,
+                max_length=64,
+                truncation=True,
+                padding='max_length',
+                return_attention_mask=True,
+                return_tensors='pt'
+            )
+            with torch.no_grad():
+                logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
+                prediction = torch.argmax(logits, dim=1).item()
+                results.append({"id": idx + 1, "text": text, "prediction": "Spam" if prediction == 1 else "Ham"})
+        return {"results": results}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Batch prediction failed: {e}")
+# @app.post("/batch_predict")
+# async def batch_predict(request: BatchTextRequest):
+#     try:
+#         model.eval()
         
-        # Batch encode all texts in the request at once
-        inputs = tokenizer(
-            request.texts,
-            add_special_tokens=True,
-            max_length=64,
-            truncation=True,
-            padding='max_length',
-            return_attention_mask=True,
-            return_tensors='pt'
-        )
+#         # Batch encode all texts in the request at once
+#         inputs = tokenizer(
+#             request.texts,
+#             add_special_tokens=True,
+#             max_length=64,
+#             truncation=True,
+#             padding='max_length',
+#             return_attention_mask=True,
+#             return_tensors='pt'
+#         )
 
-        # Run batch inference
-        with torch.no_grad():
-            logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
-            predictions = torch.argmax(logits, dim=1).tolist()
+#         # Run batch inference
+#         with torch.no_grad():
+#             logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
+#             predictions = torch.argmax(logits, dim=1).tolist()
         
-        # Format results
-        results = [
-            {"id": idx + 1, "text": text, "prediction": "Spam" if pred == 1 else "Ham"}
-            for idx, (text, pred) in enumerate(zip(request.texts, predictions))
-        ]
+#         # Format results
+#         results = [
+#             {"id": idx + 1, "text": text, "prediction": "Spam" if pred == 1 else "Ham"}
+#             for idx, (text, pred) in enumerate(zip(request.texts, predictions))
+#         ]
         
-        return {"results": results}
+#         return {"results": results}
 
-    except Exception as e:
-        logging.error(f"Batch prediction failed: {e}")
-        raise HTTPException(status_code=500, detail="Batch prediction failed. Please try again.")
+#     except Exception as e:
+#         logging.error(f"Batch prediction failed: {e}")
+#         raise HTTPException(status_code=500, detail="Batch prediction failed. Please try again.")
 
 
 @app.get("/")