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app.py

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+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()

requirements.txt

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+gradio==3.24.1
+sentence-transformers==2.2.2
+openai==0.27.4
+langchain==0.0.133

tag_data/tags_dis.npy

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+oid sha256:ac67bff7196a2e1f8349a5f4e8efad564c521670c3ce4ac4d162e570241534b8
+size 141908

tag_data/tags_embed.npy

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+oid sha256:7ed5b1c3ed770571fe690bff611041e4d87bfc9bc0fa50e9c4f9b48273f5eb39
+size 145182848