Update app.py

app.py

@@ -1,40 +1,40 @@
-import streamlit as st
-import torch
-from Models.All_Model import *
-
-# model init
-model = BertForMultiLabel() 
-# Load fine-tuned weights
-state_dict = torch.load(BERT_MODEL_PATH, map_location="cpu")
-model.load_state_dict(state_dict)
-model.eval()
-
-# -------------------------------
-#  Streamlit App
-# -------------------------------
-st.title("Emotion Classification with fine‑tuned  BERT")
-
-# Input text box
-text = st.text_area("Enter text to analyze five different emotions:")
-
-if st.button("Predict"):
-    if text.strip():
-        # Tokenize input
-        inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-
-        with torch.no_grad():
-            # logits = model(**inputs) for Ro berta
-            logits = model(input_ids=inputs["input_ids"],
-                           attention_mask=inputs["attention_mask"])
-            
-            probs = torch.sigmoid(logits).cpu().numpy().tolist()[0]
-
-        emotions = ["anger", "fear", "joy", "sadness", "surprise"]
-        result = dict(zip(emotions, probs))
-
-        # Display results
-        st.subheader("Predicted Emotion Probabilities")
-        for emotion, prob in result.items():
-            st.write(f"{emotion} :  {prob:.4f}")
-    else:
-        st.warning("Please enter some text before predicting.")
+import streamlit as st
+import torch
+from All_Model import  BertForMultiLabel ,bert_tokenizer
+
+# model init
+model = BertForMultiLabel() 
+# Load fine-tuned weights
+state_dict = torch.load(BERT_MODEL_PATH, map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+
+# -------------------------------
+#  Streamlit App
+# -------------------------------
+st.title("Emotion Classification with fine‑tuned  BERT")
+
+# Input text box
+text = st.text_area("Enter text to analyze five different emotions:")
+
+if st.button("Predict"):
+    if text.strip():
+        # Tokenize input
+        inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+        with torch.no_grad():
+            # logits = model(**inputs) for Ro berta
+            logits = model(input_ids=inputs["input_ids"],
+                           attention_mask=inputs["attention_mask"])
+            
+            probs = torch.sigmoid(logits).cpu().numpy().tolist()[0]
+
+        emotions = ["anger", "fear", "joy", "sadness", "surprise"]
+        result = dict(zip(emotions, probs))
+
+        # Display results
+        st.subheader("Predicted Emotion Probabilities")
+        for emotion, prob in result.items():
+            st.write(f"{emotion} :  {prob:.4f}")
+    else:
+        st.warning("Please enter some text before predicting.")