Update app.py

app.py

@@ -1,111 +1,112 @@
-import re
-import nltk
-import pickle
-import numpy as np
-import pandas as pd
-import streamlit as st
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from keras.models import load_model
-from keras.preprocessing.text import Tokenizer
-from keras.preprocessing.sequence import pad_sequences
-
-# -----------------------------
-# NLTK Requirements
-# -----------------------------
-try:
-    nltk.data.find('tokenizers/punkt')
-except:
-    nltk.download('punkt')
-
-try:
-    nltk.data.find('corpora/stopwords')
-except:
-    nltk.download('stopwords')
-
-stop_english = set(stopwords.words("english"))
-
-# -----------------------------
-# Streamlit UI
-# -----------------------------
-st.title("Ticket Classification App")
-
-col1, col2 = st.columns(2)
-with col1:
-    subject = st.text_input("Enter your subject:")
-with col2:
-    body = st.text_input("Enter your body:")
-
-# -----------------------------
-# Load Model
-# -----------------------------
-model_path = "model.h5"
-model = load_model(model_path)
-
-# -----------------------------
-# Load Tokenizer (IMPORTANT)
-# -----------------------------
-with open("tokenizer.pkl", "rb") as f:
-    tokenizer = pickle.load(f)
-
-MAX_SEQ_LEN = 107   # ← Must match training
-
-
-# -----------------------------
-# Clean Text
-# -----------------------------
-def clean_text(t):
-    if pd.isna(t):
-        return ""
-
-    t = t.lower()
-    tokens = word_tokenize(t)
-    tokens = [w for w in tokens if w not in stop_english and len(w) > 2]
-    t = " ".join(tokens)
-
-    # regex cleaning
-    t = re.sub(r"<.*?>", " ", t)
-    t = re.sub(r"\\n", " ", t)
-    t = re.sub(r"http\S+|www\.\S+", " ", t)
-    t = re.sub(r"\S+@\S+", " ", t)
-    t = re.sub(r"[%\[\]_\\<\(\]#\?\'\":\)\-\;\+\!\/,>\.\n\r]", " ", t)
-    t = re.sub(r"\s+", " ", t).strip()
-
-    return t
-
-
-# -----------------------------
-# Convert Text → Sequence
-# -----------------------------
-def convert_to_sequence(txt):
-    seq = tokenizer.texts_to_sequences([txt])  # must be list
-    padded = pad_sequences(seq, maxlen=MAX_SEQ_LEN, padding="pre", truncating="pre")
-    return padded
-
-
-# -----------------------------
-# Example text for display
-# -----------------------------
-st.write("Account Disruption")
-st.write("""Dear Customer Support Team,
-
-I am writing to report a significant problem with the centralized account management portal, 
-which currently appears to be offline. 
-This outage is blocking access to account settings...
-
-""")
-
-# -----------------------------
-# Prediction
-# -----------------------------
-if st.button("Submit"):
-    # combine subject & body
-    raw_text = subject + " " + body
-
-    cleaned = clean_text(raw_text)
-    st.write("Cleaned Text:", cleaned)
-
-    seq = convert_to_sequence(cleaned)
-
-    preds = model.predict(seq)
-    st.write("Model Output:", preds)
+import re
+import nltk
+import pickle
+import numpy as np
+import pandas as pd
+import streamlit as st
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from keras.models import load_model
+from keras.preprocessing.text import Tokenizer
+from keras.utils import pad_sequences
+
+
+# -----------------------------
+# NLTK Requirements
+# -----------------------------
+try:
+    nltk.data.find('tokenizers/punkt')
+except:
+    nltk.download('punkt')
+
+try:
+    nltk.data.find('corpora/stopwords')
+except:
+    nltk.download('stopwords')
+
+stop_english = set(stopwords.words("english"))
+
+# -----------------------------
+# Streamlit UI
+# -----------------------------
+st.title("Ticket Classification App")
+
+col1, col2 = st.columns(2)
+with col1:
+    subject = st.text_input("Enter your subject:")
+with col2:
+    body = st.text_input("Enter your body:")
+
+# -----------------------------
+# Load Model
+# -----------------------------
+model_path = "model.h5"
+model = load_model(model_path)
+
+# -----------------------------
+# Load Tokenizer (IMPORTANT)
+# -----------------------------
+with open("tokenizer.pkl", "rb") as f:
+    tokenizer = pickle.load(f)
+
+MAX_SEQ_LEN = 107   # ← Must match training
+
+
+# -----------------------------
+# Clean Text
+# -----------------------------
+def clean_text(t):
+    if pd.isna(t):
+        return ""
+
+    t = t.lower()
+    tokens = word_tokenize(t)
+    tokens = [w for w in tokens if w not in stop_english and len(w) > 2]
+    t = " ".join(tokens)
+
+    # regex cleaning
+    t = re.sub(r"<.*?>", " ", t)
+    t = re.sub(r"\\n", " ", t)
+    t = re.sub(r"http\S+|www\.\S+", " ", t)
+    t = re.sub(r"\S+@\S+", " ", t)
+    t = re.sub(r"[%\[\]_\\<\(\]#\?\'\":\)\-\;\+\!\/,>\.\n\r]", " ", t)
+    t = re.sub(r"\s+", " ", t).strip()
+
+    return t
+
+
+# -----------------------------
+# Convert Text → Sequence
+# -----------------------------
+def convert_to_sequence(txt):
+    seq = tokenizer.texts_to_sequences([txt])  # must be list
+    padded = pad_sequences(seq, maxlen=MAX_SEQ_LEN, padding="pre", truncating="pre")
+    return padded
+
+
+# -----------------------------
+# Example text for display
+# -----------------------------
+st.write("Account Disruption")
+st.write("""Dear Customer Support Team,
+
+I am writing to report a significant problem with the centralized account management portal, 
+which currently appears to be offline. 
+This outage is blocking access to account settings...
+
+""")
+
+# -----------------------------
+# Prediction
+# -----------------------------
+if st.button("Submit"):
+    # combine subject & body
+    raw_text = subject + " " + body
+
+    cleaned = clean_text(raw_text)
+    st.write("Cleaned Text:", cleaned)
+
+    seq = convert_to_sequence(cleaned)
+
+    preds = model.predict(seq)
+    st.write("Model Output:", preds)