Update app.py

app.py

@@ -6,7 +6,6 @@ from transformers import DistilBertTokenizer, DistilBertModel, AutoModel, AutoTo
 from langdetect import detect
 from huggingface_hub import snapshot_download
 import os
-from typing import List
 
 
 # Device
@@ -86,45 +85,35 @@ app.add_middleware(
 
 
 class TextIn(BaseModel):
-    texts: List[str]
+    text: str
 
 
 @app.post("/api/predict")
 @app.post("/api/predict")
 def predict(data: TextIn):
-    results = []
-
-    for text in data.texts:
-        try:
-            lang = detect(text)
-        except:
-            lang = "unknown"
-
-        if lang == "en":
-            tokenizer = english_tokenizer
-            model = english_model
-            inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
-            with torch.no_grad():
-                outputs = model(**inputs)
-                probs = torch.sigmoid(outputs).squeeze().cpu().tolist()
-            predictions = dict(zip(english_labels, probs))
-        else:
-            tokenizer = hinglish_tokenizer
-            model = hinglish_model
-            inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
-            with torch.no_grad():
-                outputs = model(**inputs)
-                probs = torch.softmax(outputs, dim=1).squeeze().cpu().tolist()
-            predictions = dict(zip(hinglish_labels, probs))
-
-        results.append({
-            "text": text,
-            "language": lang if lang in ["en", "hi"] else "unknown",
-            "predictions": predictions
-        })
-
-    return {"results": results}
-
+    text = data.text
+    try:
+        lang = detect(text)
+    except:
+        lang = "unknown"
+
+    if lang == "en":
+        tokenizer = english_tokenizer
+        model = english_model
+        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.sigmoid(outputs).squeeze().cpu().tolist()
+        return {"language": "English", "predictions": dict(zip(english_labels, probs))}
+
+    else:
+        tokenizer = hinglish_tokenizer
+        model = hinglish_model
+        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.softmax(outputs, dim=1).squeeze().cpu().tolist()
+        return {"language": "Hinglish", "predictions": dict(zip(hinglish_labels, probs))}
 
 @app.get("/")
 def root():