Update modelV1app.py

modelV1app.py

@@ -1,53 +1,53 @@
-import streamlit as st
-from transformers import BertForSequenceClassification, BertTokenizer
-import torch
-
-# Load model and tokenizer from Hugging Face
-@st.cache_resource()
-def load_model():
-    model = BertForSequenceClassification.from_pretrained("rrroby/insensitive-language-bert")
-    tokenizer = BertTokenizer.from_pretrained("rrroby/insensitive-language-bert")
-    return model, tokenizer
-
-model, tokenizer = load_model()
-
-# App title and description
-st.title("Disability Insensitive Language Detection V1.2")
-st.write(
-    """
-    Paste your abstract or academic text below.
-    It will be analyzed and flagged if any disability-insensitive language is detected.\n
-    NOTE: The current model was trained on very little data and is still in the early stages, therefore, it is prone to inaccuracies.
-    """
-)
-
-# User input box
-text = st.text_area("Enter text here:", height=300)
-
-# Analyze button
-if st.button("Analyze"):
-    if text.strip() == "":
-        st.warning("Some text required for analysis")
-    else:
-        # Split text into sentences to handle for multiple
-        sentences = [s.strip() for s in text.split(".") if s.strip()]
-
-        # Tokenize as batch
-        inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512)
-
-        # Predict cureent set of sentences
-        with torch.no_grad():
-            outputs = model(**inputs)
-            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-            pred_classes = torch.argmax(probs, dim=-1)
-
-        for idx, sentence in enumerate(sentences):
-            prob_not_insensitive = probs[idx][0].item() * 100
-            prob_insensitive = probs[idx][1].item() * 100
-
-            if pred_classes[idx] == 1:
-                st.error(f"**Insensitive:** {sentence}")
-            else:
-                st.success(f"**Not insensitive:** {sentence}")
-
-            st.caption(f"Model's Confidence — Not insensitive: {prob_not_insensitive:.2f}%, Insensitive: {prob_insensitive:.2f}%")
+import streamlit as st
+from transformers import BertForSequenceClassification, BertTokenizer
+import torch
+import nltk
+
+# Download sentence tokenizer data
+nltk.download('punkt')
+from nltk.tokenize import sent_tokenize
+
+# Load model and tokenizer
+@st.cache_resource()
+def load_model():
+    model = BertForSequenceClassification.from_pretrained("rrroby/insensitive-language-bert")
+    tokenizer = BertTokenizer.from_pretrained("rrroby/insensitive-language-bert")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Page title and instructions
+st.title("Disability Insensitive Language Detection V1.2")
+st.write(
+    """
+    Paste your abstract or academic text below.
+    Each sentence will be analyzed and flagged if any disability-insensitive language is detected.\n
+    NOTE: The current model was trained on very little data and is still in the early stages, therefore, it is prone to inaccuracies.
+    """
+)
+
+text = st.text_area("Enter text here:", height=250)
+
+if st.button("Analyze"):
+    if text.strip() == "":
+        st.warning("Some text required for analysis")
+    else:
+        sentences = sent_tokenize(text)
+
+        with st.spinner("Analyzing..."):
+            inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512)
+            with torch.no_grad():
+                outputs = model(**inputs)
+                probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+                pred_classes = torch.argmax(probs, dim=-1)
+
+        for idx, sentence in enumerate(sentences):
+            prob_not_insensitive = probs[idx][0].item() * 100
+            prob_insensitive = probs[idx][1].item() * 100
+
+            if pred_classes[idx] == 1:
+                st.error(f"**Insensitive:** {sentence}")
+            else:
+                st.success(f"**Not insensitive:** {sentence}")
+
+            st.caption(f"Model's Confidence — Not insensitive: {prob_not_insensitive:.2f}%, Insensitive: {prob_insensitive:.2f}%")