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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import torch.nn.functional as F | |
| import re | |
| # Load model + tokenizer | |
| MODEL_NAME = "yiyanghkust/finbert-tone" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) | |
| id2label = model.config.id2label # e.g. {0: 'negative', 1: 'neutral', 2: 'positive'} | |
| num_labels = len(id2label) | |
| def split_into_sentences(text: str): | |
| """Basic sentence splitter: split on ., !, ? and keep non-empty pieces.""" | |
| text = text.replace("\n", " ") | |
| sentences = re.split(r'(?<=[\.\?\!])\s+', text) | |
| return [s.strip() for s in sentences if len(s.strip()) > 0] | |
| def analyze_sentiment(text: str) -> str: | |
| text = text.strip() | |
| if not text: | |
| return "Please enter some text." | |
| segments = split_into_sentences(text) | |
| if not segments: | |
| segments = [text] | |
| all_probs = [] | |
| with torch.no_grad(): | |
| for seg in segments: | |
| inputs = tokenizer(seg, return_tensors="pt", truncation=True, max_length=512) | |
| outputs = model(**inputs) | |
| probs = F.softmax(outputs.logits, dim=1)[0] # shape [num_labels] | |
| all_probs.append(probs) | |
| # Average probabilities across segments | |
| avg_probs = torch.stack(all_probs, dim=0).mean(dim=0) # [num_labels] | |
| best_idx = int(torch.argmax(avg_probs)) | |
| overall_label = id2label[best_idx] | |
| # Build nice markdown output | |
| lines = [] | |
| lines.append("### Sentiment Analysis Result") | |
| lines.append("") | |
| lines.append("**Full Input Text:** ") | |
| lines.append(text) | |
| lines.append("") | |
| lines.append(f"**Segments Analyzed ({len(segments)}):**") | |
| lines.append(str(segments)) | |
| lines.append("") | |
| lines.append(f"**Overall Prediction (averaged):** {overall_label.capitalize()}") | |
| lines.append("") | |
| lines.append("**Probabilities:**") | |
| # Print probabilities in a stable, readable order | |
| for i in range(num_labels): | |
| label_name = id2label[i].capitalize() | |
| lines.append(f"- {label_name}: {float(avg_probs[i]):.4f}") | |
| return "\n".join(lines) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 📘 FinBERT Financial Sentiment Analyzer") | |
| gr.Markdown( | |
| "Paste financial news, earnings reports, or company statements below. " | |
| "The tool splits the text into sentences, runs FinBERT on each, and averages the sentiment." | |
| ) | |
| input_box = gr.Textbox(lines=4, label="Input Text") | |
| output_box = gr.Markdown(label="Analysis Result") | |
| analyze_btn = gr.Button("Analyze") | |
| analyze_btn.click(fn=analyze_sentiment, inputs=input_box, outputs=output_box) | |
| if __name__ == "__main__": | |
| demo.launch() | |