app.py

import gradio as gr
import numpy as np
import openai

from sentence_transformers import SentenceTransformer
from langchain.prompts import PromptTemplate
from collections import Counter


def process(api, caption, category, asr, ocr):
    openai.api_key = api
    preference = "兴趣标签"
    example = "例如，给定一个视频，它的\"标题\"为\"长安系最便宜的轿车，4W起很多人都看不上它，但我知道车只是代步工具，又需要什么面子呢" \
              "！\"，\"类别\"为\"汽车\"，\"ocr\"为\"长安系最便宜的一款轿车\"，\"asr\"为\"我不否认现在的国产和合资还有一定的差距，" \
              "但确实是他们让我们5万开了MP V8万开上了轿车，10万开张了ICV15万开张了大七座。\"，\"{}\"生成机器人推断出合理的\"{}\"为\"" \
              "长安轿车报价、最便宜的长安轿车、新款长安轿车\"。".format(preference, preference)

    prompt = PromptTemplate(
        input_variables=["preference", "caption", "ocr", "asr", "category", "example"],
        template="你是一个视频的\"{preference}\"生成机器人，根据输入的视频标题、类别、ocr、asr推理出合理的\"{preference}\"，以多个多"
                 "于两字的标签形式进行表达，以顿号隔开。{example}那么，给定一个新的视频，它的\"标题\"为\"{caption}\"，\"类别\"为"
                 "\"{category}\"，\"ocr\"为\"{ocr}\"，\"asr\"为\"{asr}\"，请推断出该视频的\"{preference}\"："
    )

    text = prompt.format(preference=preference, caption=caption, category=category, ocr=ocr, asr=asr, example=example)

    try:
        completion = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": text}],
            temperature=1.5,
            n=5
        )

        res = []
        for j in range(5):
            ans = completion.choices[j].message["content"].strip()
            ans = ans.replace("\n", "")
            ans = ans.replace("。", "")
            ans = ans.replace("，", "、")
            res += ans.split('、')

        tag_count = Counter(res)
        tag_count = sorted(tag_count.items(), key=lambda x: x[1], reverse=True)[:10]

        tags_embed = np.load('./tag_data/tags_embed.npy')
        tags_dis = np.load('./tag_data/tags_dis.npy')

        candidate_tags = [_[0] for _ in tag_count]
        encoder = SentenceTransformer("hfl/chinese-roberta-wwm-ext-large")
        candidate_tags_embed = encoder.encode(candidate_tags)
        candidate_tags_dis = [np.sqrt(np.dot(_, _.T)) for _ in candidate_tags_embed]

        scores = np.dot(candidate_tags_embed, tags_embed.T)
        f = open('./tag_data/tags.txt', 'r')
        all_tags = []
        for line in f.readlines():
            all_tags.append(line.strip())
        f.close()

        final_ans = []
        for i in range(scores.shape[0]):
            for j in range(scores.shape[1]):
                score = scores[i][j] / (candidate_tags_dis[i] * tags_dis[j])
                if score > 0.8:
                    final_ans.append(all_tags[j])

        print(final_ans)

        final_ans = Counter(final_ans)
        final_ans = sorted(final_ans.items(), key=lambda x: x[1], reverse=True)[:5]
        final_ans = [_[0] for _ in final_ans]

        return "、".join(final_ans)

    except:
        return 'api error'


with gr.Blocks() as demo:
    text_api = gr.Textbox(label='OpenAI API key')
    text_caption = gr.Textbox(label='Caption')
    text_category = gr.Textbox(label='Category')
    text_asr = gr.Textbox(label='ASR')
    text_ocr = gr.Textbox(label='OCR')

    text_output = gr.Textbox(value='', label='Output')

    btn = gr.Button(value='Submit')
    btn.click(process, inputs=[text_api, text_caption, text_category, text_asr, text_ocr], outputs=[text_output])


if __name__ == "__main__":
    demo.launch(share=True)