Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,8 +3,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import torch.nn.functional as F
 
-# Load model and tokenizer from Hugging Face
-MODEL_NAME = "nlptown/bert-base-multilingual-uncased-sentiment"
+MODEL_NAME = "imrgurmeet/fine-tuned-sentiment-model"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
 
@@ -14,14 +13,22 @@ user_input = st.text_area("Enter text for sentiment analysis:")
 
 if st.button("Analyze"):
     if user_input.strip() != "":
-        # Tokenize input
         inputs = tokenizer(user_input, return_tensors="pt")
-        # Forward pass
         outputs = model(**inputs)
         probs = F.softmax(outputs.logits, dim=-1)
-        # Get predicted sentiment
+
+        # Dynamically create label list based on model
+        num_labels = model.config.num_labels
+        if num_labels == 3:
+            labels = ["Negative", "Neutral", "Positive"]
+        elif num_labels == 2:
+            labels = ["Negative", "Positive"]
+        else:
+            labels = [f"Class {i}" for i in range(num_labels)]
+
         pred_class = torch.argmax(probs).item()
-        sentiment = ["Negative", "Neutral", "Positive"][pred_class]
+        sentiment = labels[pred_class]
+
         st.write(f"**Sentiment:** {sentiment}")
         st.write(f"**Confidence:** {probs[0][pred_class]:.2f}")
     else: