Update app.py

app.py

@@ -1,15 +1,48 @@
 from DistilBERT import model_DB
 import streamlit as st
-from transformers import AutoTokenizer, AutoModel
+from transformers import DistilBertTokenizer, DistilBertModel
+import logging
+logging.basicConfig(level=logging.ERROR)
 import torch
 
+MAX_LEN = 100
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased', truncation=True, do_lower_case=True)
 
 def sentiment_analysis_DB(input):
-    encoded_input = tokenizer(text, return_tensors='pt').to(device)
-    model.to(device)
-    ids = 
-    mask = 
-    token_type_ids = 
-    output = 
+    inputs = tokenizer.encode_plus(
+        input,
+        None,
+        add_special_tokens=True,
+        max_length=MAX_LEN,
+        pad_to_max_length=True,
+        return_token_type_ids=True
+    )
+    ids = inputs['input_ids']
+    mask = inputs['attention_mask']
+    token_type_ids = inputs["token_type_ids"]
+    output = model_DB(ids, mask, token_type_ids)
+    final_outputs = np.array(output)
+    final_outputs = final_outputs[0]
+    if final_outputs == True:
+        result = 1
+    else:
+        result = 0
+    return result
+
+# Streamlit app
+st.title("Sentiment Analysis App")
+
+# User input
+user_input = st.text_area("Enter some text:")
+
+# Button to trigger sentiment analysis
+if st.button("Analyze Sentiment"):
+    # Perform sentiment analysis
+    result = sentiment_analysis_DB(user_input)
+
+    # Display result
+    if result == 1:
+        st.success("Positive sentiment detected!")
+    else:
+        st.error("Negative sentiment detected.")