| import os |
| import numpy as np |
| import torch |
| from huggingface_hub import hf_hub_download |
| from safetensors.torch import load_file as load_safetensors |
| from transformers import AutoTokenizer |
| from openvino.runtime import Core |
| import gradio as gr |
| import pandas as pd |
|
|
| |
| HF_MODEL = "Kaiyeee/goemotions-multilabel" |
| tokenizer = AutoTokenizer.from_pretrained("roberta-base") |
| core = Core() |
| onnx_path = hf_hub_download(repo_id=HF_MODEL, filename="goemotions_multilabel.onnx") |
| ov_model = core.read_model(model=onnx_path) |
| compiled = core.compile_model(model=ov_model, device_name="CPU") |
|
|
| emotion_labels = [ |
| "admiration","amusement","anger","annoyance","approval","caring","confusion", |
| "curiosity","desire","disappointment","disapproval","disgust","embarrassment", |
| "excitement","fear","gratitude","grief","joy","love","nervousness","optimism", |
| "pride","realization","relief","remorse","sadness","surprise","neutral" |
| ] |
|
|
| def predict(texts, threshold=0.3): |
| toks = tokenizer(texts, padding="max_length", truncation=True, max_length=128, return_tensors="np") |
| outs = compiled([toks["input_ids"], toks["attention_mask"]]) |
| logits = outs[compiled.output(0)] |
| probs = 1 / (1 + np.exp(-logits)) |
| preds = (probs > threshold).astype(int) |
|
|
| results = [] |
| for i, ps in enumerate(preds): |
| fired = [emotion_labels[j] for j, flag in enumerate(ps) if flag] |
| results.append(", ".join(fired) or "none") |
| return results |
|
|
| |
| def predict_bulk(texts_str, threshold=0.3): |
| texts = [line.strip() for line in texts_str.split("\n") if line.strip()] |
| results = predict(texts, threshold) |
| formatted = "\n\n".join(f"{t}: " + ", ".join(f"**{e}**" for e in r.split(", ")) for t, r in zip(texts, results)) |
| return formatted |
|
|
|
|
|
|
| |
| def predict_file(file_obj, threshold=0.3): |
| df = pd.read_csv(file_obj.name) |
| if 'text' not in df.columns: |
| return "CSV must have a 'text' column." |
| texts = df['text'].astype(str).tolist() |
| results = predict(texts, threshold) |
| df['emotions'] = results |
| out_path = "predictions.csv" |
| df.to_csv(out_path, index=False) |
| return out_path |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# 🦄 Sentiment Analyzer for 28 different emotions!") |
|
|
| thr = gr.Slider(0.1, 0.9, 0.3, label="Threshold") |
|
|
| with gr.Tab("Paste Text (one per line)"): |
| inp = gr.Textbox(label="Enter texts (one per line)", lines=10, placeholder="Enter sentences here") |
| out = gr.Markdown(label="Predicted emotions") |
| btn = gr.Button("Analyze") |
| btn.click(fn=predict_bulk, inputs=[inp, thr], outputs=out) |
|
|
| with gr.Tab("Upload CSV"): |
| file_inp = gr.File(label="Upload CSV with a 'text' column") |
| out_file = gr.File(label="Download CSV with emotions") |
| file_btn = gr.Button("Analyze CSV") |
| file_btn.click(fn=predict_file, inputs=[file_inp, thr], outputs=out_file) |
|
|
| demo.launch() |
