manually call model

app.py

@@ -1,19 +1,16 @@
 import streamlit as st
-from transformers import pipeline, AutoTokenizer
-import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
 
 @st.cache_resource()
 def load_model():
-    pipe = pipeline(
-        "text-classification",
-        model="alxvlsv/rubert-emotions",
-        tokenizer="alxvlsv/rubert-emotions",
-        top_k=None
-    )
     tokenizer = AutoTokenizer.from_pretrained("alxvlsv/rubert-emotions")
-    return pipe, tokenizer
+    model = AutoModelForSequenceClassification.from_pretrained("alxvlsv/rubert-emotions")
+    return model, tokenizer
 
 model, tokenizer = load_model()
+model.eval()
 
 label_map = {
     "LABEL_0": "admiration",
@@ -83,9 +80,23 @@ st.write("Введите текст на русском, и мы покажем,
 text_input = st.text_area("Введите текст здесь:")
 
 if text_input:
-    # Ограничиваем до 256 токенов
-    encoded = tokenizer(text_input, truncation=True, max_length=256, return_tensors="pt")
-    results = model(encoded)[0]
+    inputs = tokenizer(
+        text_input,
+        max_length=256,
+        truncation=True,
+        return_tensors="pt"
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1)[0]
+
+    results = []
+    for i, score in enumerate(probs):
+        results.append({
+            "label": f"LABEL_{i}",
+            "score": score.item()
+        })
 
     # Нормализуем вероятности до 100%
     total_score = sum(r["score"] for r in results)