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Browse files- .gitattributes +1 -0
- TA_embeddings.csv +3 -0
- TaBot.py +56 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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TA_embeddings.csv filter=lfs diff=lfs merge=lfs -text
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TA_embeddings.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:8afcfddb6b6aa42dd3513a2e801d676c012da0d9199825064123c6a5812048af
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size 244603667
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TaBot.py
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import gradio as gr
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import openai
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import pandas as pd
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import numpy as np
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import csv
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openai.api_key="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"
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from openai.embeddings_utils import get_embedding
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from openai.embeddings_utils import cosine_similarity
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df = pd.read_csv("TA_embeddings.csv")
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df["embedding"]=df["embedding"].apply(eval).apply(np.array)
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def reply(input):
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input = input
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input_vector = get_embedding(input, engine="text-embedding-ada-002")
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df["similiarities"]=df["embedding"].apply(lambda x: cosine_similarity(x,input_vector))
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data = df.sort_values("similiarities", ascending=False).head(20)
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data.to_csv("sorted.csv")
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context = []
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for i, row in data.iterrows():
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context.append(row['text'])
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context
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text = "\n".join(context)
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context = text
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prompt = f"""
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Answer the following question If you don't know the answer for certain, say I don't know.
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Context: {context}
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Q: {input}
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"""
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return openai.Completion.create(
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prompt=prompt,
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temperature=1,
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max_tokens=500,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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model="text-davinci-003"
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)["choices"][0]["text"].strip(" \n")
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input_text = gr.inputs.Textbox(label="Enter your text here")
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text_output = gr.outputs.Textbox(label="Most similar text")
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ui = gr.Interface(fn=reply,
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inputs=input_text,
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outputs=[text_output],
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theme="compact",
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layout="vertical",
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inputs_layout="stacked",
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outputs_layout="stacked",
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allow_flagging=False)
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ui.launch()
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