Update app.py

app.py

@@ -51,25 +51,39 @@ def preprocess_text(text):
 def predict_sentiment(text):
     if not text:
         return 0.0
-    encoded_input = tokenizer(
-        text.split(),
-        return_tensors='pt',
-        padding=True,
-        truncation=True,
-        max_length=512
-    )
-    input_ids, attention_mask = encoded_input["input_ids"], encoded_input["attention_mask"]
-    with torch.no_grad():
-        score = score_model(input_ids, attention_mask)[0].item()
-
-    scaled=score*100
-    k = 20   
-    midpoint = 0.7 
-
-    scaled_score = 1 / (1 + np.exp(-k * (score - midpoint)))
-    final_output = scaled_score * 100 
-
-    return final_output
+    # encoded_input = tokenizer(
+    #     text.split(),
+    #     return_tensors='pt',
+    #     padding=True,
+    #     truncation=True,
+    #     max_length=512
+    # )
+    # input_ids, attention_mask = encoded_input["input_ids"], encoded_input["attention_mask"]
+    # with torch.no_grad():
+    #     score = score_model(input_ids, attention_mask)[0].item()
+
+
+    # k = 20   
+    # midpoint = 0.7 
+
+    # scaled_score = 1 / (1 + np.exp(-k * (score - midpoint)))
+    # final_output = scaled_score * 100 
+
+    # return 1-final_output
+    text = preprocess_text(text)
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]
+    negative_id = -1
+    for idx, label in config.id2label.items():
+        if label.lower() == 'positive':
+            negative_id = idx
+            negative_score = scores[negative_id]
+    
+    return (float(negative_score))*100