| from transformers import AutoModel | |
| import gradio as gr | |
| import fasttext | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| repo_id = "tevykuch/zeroshot-reptile" | |
| filename = "metalearn_wordy.bin" | |
| model_path = hf_hub_download(repo_id=repo_id, filename=filename) | |
| fasttext_model = fasttext.load_model(model_path) | |
| model = AutoModel.from_pretrained(repo_id, force_download=True) | |
| def predict(input_text): | |
| words = input_text.split() | |
| embeddings = torch.tensor([fasttext_model.get_word_vector(word) for word in words]) | |
| avg_embedding = embeddings.mean(dim=0).unsqueeze(0) | |
| with torch.no_grad(): | |
| output = model(avg_embedding) | |
| predicted_class = output.argmax(dim=1).item() | |
| return f"Predicted class: {predicted_class}" | |
| iface = gr.Interface(fn=predict, | |
| inputs="text", | |
| outputs="text", | |
| title="My Model Demo", | |
| description="Enter some text to see the model prediction.") | |
| iface.launch() |