Create cap_media2.py

interfaces/cap_media2.py

@@ -0,0 +1,86 @@
+import gradio as gr
+
+import os
+import torch
+import numpy as np
+import pandas as pd
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from huggingface_hub import HfApi
+
+from label_dicts import CAP_MEDIA2_NUM_DICT, CAP_MEDIA2_LABEL_NAMES
+
+from .utils import is_disk_full, release_model
+
+HF_TOKEN = os.environ["hf_read"]
+
+languages = [
+    "Multilingual",
+]
+
+domains = {
+    "media": "media"
+}
+
+def check_huggingface_path(checkpoint_path: str):
+    try:
+        hf_api = HfApi(token=HF_TOKEN)
+        hf_api.model_info(checkpoint_path, token=HF_TOKEN)
+        return True
+    except:
+        return False
+
+def build_huggingface_path(language: str, domain: str):
+    return "poltextlab/xlm-roberta-large-pooled-cap-media2"
+
+def predict(text, model_id, tokenizer_id):
+    device = torch.device("cpu")
+
+    # Load JIT-traced model
+    jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt"
+    model = torch.jit.load(jit_model_path).to(device)
+    model.eval()
+
+    # Load tokenizer (still regular HF)
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
+
+    # Tokenize input
+    inputs = tokenizer(
+        text,
+        max_length=256,
+        truncation=True,
+        padding="do_not_pad",
+        return_tensors="pt"
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        output = model(inputs["input_ids"], inputs["attention_mask"])
+        print(output) # debug
+        logits = output["logits"]
+        
+    release_model(model, model_id)
+
+    probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
+    output_pred = {f"[{CAP_MEDIA2_NUM_DICT[i]}] {CAP_MEDIA2_LABEL_NAMES[CAP_MEDIA2_NUM_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}
+    output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
+    return output_pred, output_info
+
+def predict_cap(text, language, domain):
+    domain = domains[domain]
+    model_id = build_huggingface_path(language, domain)
+    tokenizer_id = "xlm-roberta-large"
+    
+    if is_disk_full():
+        os.system('rm -rf /data/models*')
+        os.system('rm -r ~/.cache/huggingface/hub')
+        
+    return predict(text, model_id, tokenizer_id)
+
+demo = gr.Interface(
+    title="CAP Media2 Topics Babel Demo",
+    fn=predict_cap,
+    inputs=[gr.Textbox(lines=6, label="Input"),
+            gr.Dropdown(languages, label="Language", value=languages[0]),
+            gr.Dropdown(domains.keys(), label="Domain", value=list(domains.keys())[0])],
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])