Update app.py

app.py

@@ -1,7 +1,57 @@
 from fastapi import FastAPI
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+import torch
 
 app = FastAPI()
 
+
+
+model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(model_name)
+sentiment_tokenizer = AutoTokenizer.from_pretrained(model_name)
+sentiment_model.config.id2label[3] = "mixed"
+
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}
+
+
+@app.post(/sentiment_score)
+async def sentiment_score(text: str):
+    inputs = sentiment_tokenizer(text[:2500], return_tensors='pt')
+    
+    with torch.no_grad():
+        logits = sentiment_model(**inputs).logits #+ 1
+    
+    
+    print(logits)
+    
+    logits = logits + logits[0,1].abs()
+    
+    # print(torch.nn.functional.sigmoid(logits))
+    
+    # logits = logits / 10
+    
+    # print(logits)
+    
+    # print(torch.abs(logits[0,0] - logits[0,-1]))
+    # print(logits[0,1]//torch.max(torch.abs(logits[0,::2])))
+    
+    logits = torch.cat(
+        (
+            logits, (
+                # ( logits[0,1] + torch.sign(logits[0,0] - logits[0,-1]) * (logits[0,0] - logits[0,-1])/2 )/2 + 
+                # (logits[0,0] + logits[0,-1])/20
+                (1 - torch.abs(logits[0,0] - logits[0,-1])*(2+(logits[0,1]//torch.max(torch.abs(logits[0,::2])))))
+                ).unsqueeze(0).unsqueeze(0)
+        ), dim=-1
+    )
+    
+    softmax = torch.nn.functional.softmax(
+        logits, 
+        dim=-1
+    )
+    
+    return [{"label":model.config.id2label[predicted_class_id.tolist()], "score":softmax[0, predicted_class_id].tolist()} for predicted_class_id in softmax.argsort(dim=-1, descending=True)[0]]
+    
+