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import streamlit as st
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model_name = "amiguel/fintune_naming_model" # Replace with your model repo
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def classify_review(text, model, tokenizer, device, max_length=512):
model.eval()
inputs = tokenizer.encode_plus(
text,
truncation=True,
padding='max_length',
max_length=max_length,
return_tensors="pt"
)
input_ids = inputs['input_ids'].to(device)
attention_mask = inputs['attention_mask'].to(device)
with torch.no_grad():
outputs = model(input_ids, attention_mask=attention_mask)
logits = outputs.logits
predicted_label = torch.argmax(logits, dim=-1).item()
return "Proper Naming otfcn" if predicted_label == 1 else "Wrong Naming notfcn"
def main():
st.title("Notifications Naming Classifier")
input_option = st.radio("Select input option", ("Single Text Query", "Upload Table"))
if input_option == "Single Text Query":
text_query = st.text_input("Enter text query")
if st.button("Classify"):
if text_query:
predicted_label = classify_review(text_query, model, tokenizer, device)
st.write("Predicted Label:")
st.write(predicted_label)
else:
st.warning("Please enter a text query.")
elif input_option == "Upload Table":
uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx"])
if uploaded_file is not None:
import pandas as pd
if uploaded_file.name.endswith(".csv"):
df = pd.read_csv(uploaded_file)
else:
df = pd.read_excel(uploaded_file)
text_column = st.selectbox("Select the text column", df.columns)
predicted_labels = [classify_review(text, model, tokenizer, device) for text in df[text_column]]
df["Predicted Label"] = predicted_labels
st.write(df)
if __name__ == "__main__":
main()
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