Create app.py

app.py

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+import pandas as pd
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import re
+import torch
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+def analyze_text(text):
+    text = re.sub(r"[^\w\s]", "", text)
+    text = text.lower()
+    encoded_text = tokenizer(text, truncation=True, padding=True, return_tensors='pt')
+
+    with torch.no_grad():
+        output = model(**encoded_text)
+        predictions = output.logits.argmax(-1).item()
+
+    if predictions == 0:
+        return "Job Interview Related"
+    else:
+        return "Not Job Interview Related"
+st.title("Job Interview Message Analyzer")
+
+uploaded_file = st.file_uploader("Upload CSV file")
+user_input = st.text_input("Enter text")
+
+if uploaded_file:
+    data = pd.read_csv(uploaded_file)
+    results = []
+    for message in data["message"]:
+        result = analyze_text(message)
+        results.append(result)
+    data["Job_Interview_Related"] = results
+    st.dataframe(data)
+elif user_input:
+    result = analyze_text(user_input)
+    st.write(f"Message Classification: {result}")
+else:
+    st.write("Please upload a CSV file or enter text to analyze.")