Fixing Tensor item Issue

app.py

@@ -1,32 +1,29 @@
-import streamlit as st 
-import pandas as pd 
-import numpy as np 
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline, AutoConfig 
-model_ckpt = "AbhishekBhavnani/TweetClassification" 
-
-model = AutoModelForSequenceClassification.from_pretrained(
-    model_ckpt,
-    low_cpu_mem_usage=False 
-)
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch.nn.functional as F
 
+# Load tokenizer and model
+model_ckpt = "AbhishekBhavnani/TweetClassification"
 tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
-
-
-# Use the pipeline for text classification
-classifier = pipeline(
-    'text-classification',
-    model=model,
-    tokenizer=tokenizer,
-    device=-1  # -1 for CPU, set to GPU index if available (e.g., device=0)
-) 
-
-st.title("Tweet Classification")
-
-text = st.text_area("Enter Your Tweet Here")  
-
-if st.button("Predict"):  
-    result = classifier(text)
-    st.write(result)   
-
-
+model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
+model.eval()  # Important for inference mode
+
+# App UI
+st.title("Tweet Emotion Classifier")
+text = st.text_area("Enter your tweet here")
+
+if st.button("Predict"):
+    if text.strip():
+        with torch.no_grad():
+            # Tokenize input
+            inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+            outputs = model(**inputs)
+            probs = F.softmax(outputs.logits, dim=-1)
+            top = torch.argmax(probs, dim=1).item()
+            label = model.config.id2label[str(top)]
+            score = probs[0][top].item()
+            
+            st.success(f"**Prediction**: {label} ({score:.4f})")
+    else:
+        st.warning("Please enter a tweet.")