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app/.streamlit/secrets.toml
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# OpenAI API Key
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OPENAI_API_KEY = "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"
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app/__pycache__/dataclean_hf.cpython-310.pyc
CHANGED
Binary files a/app/__pycache__/dataclean_hf.cpython-310.pyc and b/app/__pycache__/dataclean_hf.cpython-310.pyc differ
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app/__pycache__/openai_chat_completion.cpython-310.pyc
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Binary files a/app/__pycache__/openai_chat_completion.cpython-310.pyc and b/app/__pycache__/openai_chat_completion.cpython-310.pyc differ
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app/openai_chat_completion.py
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import os
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import pandas as pd
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from dotenv import load_dotenv
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load_dotenv()
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@@ -6,7 +7,7 @@ import openai
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import streamlit as st
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# # set OPENAI_API_KEY environment variable from .streamlit/secrets.toml file
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openai.api_key = st.secrets
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# # set OPENAI_API_KEY environment variable from .env file
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# openai.api_key = os.getenv("OPENAI_API_KEY")
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@@ -48,8 +49,8 @@ class OpenAIChatCompletions:
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# function to use test data to predict completions
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def predict_jsonl(
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self,
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path_or_buf='~/data/cookies_train.jsonl',
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n_samples=None,
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n_shot=None
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):
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@@ -83,10 +84,15 @@ class OpenAIChatCompletions:
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# a method that samples n rows from a jsonl file, returning a pandas dataframe
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@staticmethod
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def _sample_jsonl(
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path_or_buf='~/data/cookies_train.jsonl',
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n_samples=5
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):
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jsonObj = pd.read_json(path_or_buf=path_or_buf, lines=True)
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return jsonObj.sample(n_samples, random_state=42)
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import os
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from io import BytesIO
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import pandas as pd
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from dotenv import load_dotenv
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load_dotenv()
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import streamlit as st
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# # set OPENAI_API_KEY environment variable from .streamlit/secrets.toml file
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openai.api_key = st.secrets["OPENAI_API_KEY"]
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# # set OPENAI_API_KEY environment variable from .env file
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# openai.api_key = os.getenv("OPENAI_API_KEY")
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# function to use test data to predict completions
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def predict_jsonl(
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self,
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path_or_buf='../data/cookies_train.jsonl',
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# path_or_buf='~/data/cookies_train.jsonl',
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n_samples=None,
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n_shot=None
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):
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# a method that samples n rows from a jsonl file, returning a pandas dataframe
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@staticmethod
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def _sample_jsonl(
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path_or_buf='../data/cookies_train.jsonl',
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# path_or_buf='~/data/cookies_train.jsonl',
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n_samples=5
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):
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# jsonObj = pd.read_json(path_or_buf=path_or_buf, lines=True)
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file_path = os.path.join(os.getcwd(), "../data/cookies_train.jsonl")
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print(file_path)
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with open(file_path, "r") as file:
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jsonl_str = file.read()
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jsonObj = pd.read_json(BytesIO(jsonl_str.encode()), lines=True, engine="pyarrow")
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return jsonObj.sample(n_samples, random_state=42)
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requirements.txt
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datasets==2.13.0
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numpy==1.24.3
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openai==0.27.8
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pandas==2.0.
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python-dotenv==1.0.0
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scikit_learn==1.2.2
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SQLAlchemy==2.0.18
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datasets==2.13.0
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numpy==1.24.3
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openai==0.27.8
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pandas==2.0.3
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python-dotenv==1.0.0
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scikit_learn==1.2.2
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SQLAlchemy==2.0.18
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