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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("nebiyu29/fintunned-v2-roberta_GA") | |
| model = AutoModelForSequenceClassification.from_pretrained("nebiyu29/fintunned-v2-roberta_GA") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| # Define a function to split a text into segments of 512 tokens | |
| def split_text(text): | |
| # Tokenize the text | |
| tokens = tokenizer.tokenize(text) | |
| # Initialize an empty list for segments | |
| segments = [] | |
| # Initialize an empty list for current segment | |
| current_segment = [] | |
| # Initialize a counter for tokens | |
| token_count = 0 | |
| # Loop through the tokens | |
| for token in tokens: | |
| # Add the token to the current segment | |
| current_segment.append(token) | |
| # Increment the token count | |
| token_count += 1 | |
| # If the token count reaches 512 or the end of the text, add the current segment to the segments list | |
| if token_count == 512 or token == tokens[-1]: | |
| # Convert the current segment to a string and add it to the segments list | |
| segments.append(tokenizer.convert_tokens_to_string(current_segment)) | |
| # Reset the current segment and the token count | |
| current_segment = [] | |
| token_count = 0 | |
| # Return the segments list | |
| return segments | |
| # Define a function to extract predictions from model output (adjust as needed) | |
| def extract_predictions(outputs): | |
| # Assuming outputs contain logits and labels (adapt based on your model's output format) | |
| logits = outputs.logits | |
| probs = logits.softmax(dim=1) | |
| preds = torch.argmax(probs, dim=1) | |
| return probs, preds # Return all probabilities and predicted labels | |
| # a function that classifies text | |
| def classify_text(text): | |
| # Split text into segments using split_text | |
| segments = split_text(text) | |
| # Initialize empty list for predictions | |
| predictions = [] | |
| # Loop through segments, process, and store predictions | |
| for segment in segments: | |
| inputs = tokenizer([segment], padding=True, 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) | |
| # Extract predictions for each segment | |
| probs, preds = extract_predictions(outputs) # Define this function based on your model's output | |
| pred_label = model.config.id2label[preds[0].item()] | |
| # Append predictions for this segment | |
| predictions.append({ | |
| "segment_text": segment, | |
| "label": pred_label, # Assuming single label prediction | |
| "probability": probs[0][preds[0]].item() # Access probability for the predicted label | |
| }) | |
| return predictions | |
| # Streamlit app | |
| st.title("Text Classification Demo") | |
| st.write("Enter some text, and the model will classify it.") | |
| text_input = st.text_input("Text Input") | |
| if st.button("Classify"): | |
| predictions = classify_text(text_input) | |
| for prediction in predictions: | |
| st.write(f"Segment Text: {prediction['segment_text']}") | |
| st.write(f"Label: {prediction['label']}") | |
| st.write(f"Probability: {prediction['probability']}") |