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import streamlit as st
import torch
from transformers import GPT2Tokenizer
import pandas as pd
# Load the tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Define the classification function
def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):
model.eval()
# Prepare inputs to the model
input_ids = tokenizer.encode(text)
supported_context_length = model.pos_emb.weight.shape[1]
# Truncate sequences if they are too long
input_ids = input_ids[:min(max_length, supported_context_length)]
# Pad sequences to the longest sequence
input_ids += [pad_token_id] * (max_length - len(input_ids))
input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension
# Model inference
with torch.no_grad():
logits = model(input_tensor)[:, -1, :] # Logits of the last output token
predicted_label = torch.argmax(logits, dim=-1).item()
# Return the classified result
return "Proper Naming Notfcn" if predicted_label == 1 else "Wrong Naming Notificn"
# Load the trained model from the local directory
model_path = "clv__classifier_774M.pth"
model = torch.load(model_path)
model.eval()
# Set the device to run the model on (GPU if available, else CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Streamlit app
def main():
st.title("Text Classification App")
# Input options
input_option = st.radio("Select input option", ("Single Text Query", "Upload Table"))
if input_option == "Single Text Query":
# Single text query input
text_query = st.text_input("Enter text query")
if st.button("Classify"):
if text_query:
# Classify the text query
predicted_label = classify_review(text_query, model, tokenizer, device, max_length=train_dataset.max_length)
st.write("Predicted Label:")
st.write(predicted_label)
else:
st.warning("Please enter a text query.")
elif input_option == "Upload Table":
# Table upload
uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx"])
if uploaded_file is not None:
# Read the uploaded file
if uploaded_file.name.endswith(".csv"):
df = pd.read_csv(uploaded_file)
else:
df = pd.read_excel(uploaded_file)
# Select the text column
text_column = st.selectbox("Select the text column", df.columns)
# Classify the texts in the selected column
predicted_labels = []
for text in df[text_column]:
predicted_label = classify_review(text, model, tokenizer, device, max_length=train_dataset.max_length)
predicted_labels.append(predicted_label)
# Add the predicted labels to the DataFrame
df["Predicted Label"] = predicted_labels
# Display the DataFrame with predicted labels
st.write(df)
if __name__ == "__main__":
main() |