| from datasets import load_from_disk, load_dataset | |
| import pandas as pd | |
| import os | |
| import gradio as gr | |
| ds_with_embeddings = load_dataset("svjack/bloom-dialogue-generate-ds-en", split="train") | |
| ds_with_embeddings.add_faiss_index(column='embeddings') | |
| from sentence_transformers import SentenceTransformer | |
| encoder = SentenceTransformer("sentence-transformers/LaBSE") | |
| def retrieve_search_df(question = "Which diet you want to eat?", top_k = 10): | |
| question_embedding = encoder.encode(question) | |
| scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', question_embedding, k=top_k) | |
| sdf = pd.DataFrame(retrieved_examples) | |
| sdf["scores"] = scores | |
| return sdf[["question", "dialogue_text", "dialogue", "repo", "scores"]] | |
| example_sample = [ | |
| ["Which diet you want to eat?", 3], | |
| ["Do you like this film?", 5], | |
| ] | |
| def demo_func(prefix, max_length): | |
| max_length = max(int(max_length), 3) | |
| l = retrieve_search_df(prefix, max_length)["dialogue"].values.tolist() | |
| assert type(l) == type([]) | |
| return { | |
| "Dialogue Context": l | |
| } | |
| demo = gr.Interface( | |
| fn=demo_func, | |
| inputs=[gr.Text(label = "Prefix"), | |
| gr.Number(label = "Top K", value = 10) | |
| ], | |
| outputs="json", | |
| title=f"Bloom English Daliy Dialogue Generator 🦅🌸 sample search demonstration", | |
| description = 'This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/Daliy-Dialogue](https://github.com/svjack/Daliy-Dialogue)</h4></b>\n', | |
| examples=example_sample if example_sample else None, | |
| cache_examples = False | |
| ) | |
| demo.launch(server_name=None, server_port=None) | |