Update app.py

app.py

@@ -51,21 +51,25 @@ def preprocess_text(text):
 def predict_sentiment(text):
     if not text:
         return 0.0
-    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_index = None
-    for i in range(scores.shape[0]):
-        if config.id2label[ranking[i]] == 'positive':
-            negative_index = ranking[i]
-            break
-    negative_score = scores[negative_index]
-
-    return float(negative_score.item())*100
+    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