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add Azure OpenAI Batch API demo
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‎.pre-commit-config.yaml‎

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rev: 24.2.0
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hooks:
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- id: black
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exclude: 'generated/.*|artifacts/.*'
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exclude: 'generated/.*|artifacts/.*|.jsonl'

‎README.md‎

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| [5_streamlit_query_chat_history](./apps/5_streamlit_query_chat_history/README.md) | Search Chat History | ![5_streamlit_query_chat_history](./docs/images/5_streamlit_query_chat_history.main.png) |
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| [6_call_azure_ai_search](./apps/6_call_azure_ai_search/README.md) | Call Azure AI Search from Python | No Image |
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| [7_streamlit_chat_rag](./apps/7_streamlit_chat_rag/README.md) | Add RAG feature to Streamlit chat app | ![7_streamlit_chat_rag](./docs/images/7_streamlit_chat_rag.main.png) |
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| [8_streamlit_azure_openai_batch](./apps/8_streamlit_azure_openai_batch/README.md) | Call Azure OpenAI Batch API with Streamlit | ![8_streamlit_azure_openai_batch](./docs/images/8_streamlit_azure_openai_batch.main.png) |
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| [99_streamlit_examples](./apps/99_streamlit_examples/README.md) | Code samples for Streamlit | ![99_streamlit_examples](./docs/images/99_streamlit_examples.explaindata.png) |

‎apps/8_streamlit_azure_openai_batch/README.md‎

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# Call Azure OpenAI Batch API with Streamlit
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This app demonstrates how to call Azure OpenAI Batch API with Streamlit.
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## Prerequisites
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- Python 3.10 or later
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- Azure OpenAI Service ([Global batch deployment](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/batch?tabs=standard-input&pivots=programming-language-python))
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## Usage
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1. Get Azure OpenAI Service API key
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1. Copy [.env.template](../../.env.template) to `.env` in the same directory
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1. Set credentials in `.env`
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1. Run [main.py](./main.py)
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```shell
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# Create a virtual environment
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$ python -m venv .venv
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# Activate the virtual environment
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$ source .venv/bin/activate
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# Install dependencies
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$ pip install -r requirements.txt
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# Run the script
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$ python -m streamlit run apps/8_streamlit_azure_openai_batch/main.py
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```
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### Example
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![Streamlit Chat](../../docs/images/8_streamlit_azure_openai_batch.main.png)
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## References
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- [Getting started with Azure OpenAI global batch deployments (preview)](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/batch?tabs=standard-input&pivots=programming-language-python)

‎apps/8_streamlit_azure_openai_batch/main.py‎

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import base64
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import json
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from os import getenv
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import streamlit as st
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from dotenv import load_dotenv
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from openai import AzureOpenAI
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load_dotenv()
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with st.sidebar:
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azure_openai_endpoint = st.text_input(
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label="AZURE_OPENAI_ENDPOINT",
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value=getenv("AZURE_OPENAI_ENDPOINT"),
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key="AZURE_OPENAI_ENDPOINT",
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type="default",
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)
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azure_openai_api_key = st.text_input(
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label="AZURE_OPENAI_API_KEY",
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# DEBUG
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value=getenv("AZURE_OPENAI_API_KEY"),
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key="AZURE_OPENAI_API_KEY",
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type="password",
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)
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azure_openai_api_version = st.text_input(
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label="AZURE_OPENAI_API_VERSION",
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value=getenv("AZURE_OPENAI_API_VERSION"),
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key="AZURE_OPENAI_API_VERSION",
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type="default",
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)
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azure_openai_gpt_model = st.text_input(
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label="AZURE_OPENAI_GPT_MODEL",
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value=getenv("AZURE_OPENAI_GPT_MODEL"),
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key="AZURE_OPENAI_GPT_MODEL",
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type="default",
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)
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"[Azure Portal](https://portal.azure.com/)"
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"[Azure OpenAI Studio](https://oai.azure.com/resource/overview)"
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"[View the source code](https://github.com/ks6088ts-labs/workshop-azure-openai/blob/main/apps/8_streamlit_azure_openai_batch/main.py)"
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client = AzureOpenAI(
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api_key=azure_openai_api_key,
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api_version=azure_openai_api_version,
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azure_endpoint=azure_openai_endpoint,
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)
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st.title("8_streamlit_azure_openai_batch")
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if not azure_openai_api_key or not azure_openai_endpoint or not azure_openai_api_version or not azure_openai_gpt_model:
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st.warning("Please fill in the required fields at the sidebar.")
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st.stop()
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# ---------------
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# Upload batch file
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# ---------------
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st.header("Upload batch file")
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st.info("Upload a file in JSON lines format (.jsonl)")
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uploaded_file = st.file_uploader("Upload an input file in JSON lines format", type=("jsonl"))
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if uploaded_file:
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bytes_data = uploaded_file.read()
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st.write(bytes_data.decode().split("\n"))
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submit_button = st.button("Submit", key="submit")
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if submit_button:
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temp_file_path = "tmp.jsonl"
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with open(temp_file_path, "wb") as f:
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f.write(bytes_data)
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with st.spinner("Uploading..."):
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try:
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response = client.files.create(
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# FIXME: hardcoded for now, use uploaded_file
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file=open(temp_file_path, "rb"),
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purpose="batch",
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)
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st.write(response.model_dump())
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except Exception as e:
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st.error(e)
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st.markdown("---")
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# ---------------
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# Track file upload status
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# ---------------
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st.header("Track file upload status")
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st.info("Track the file upload status using the file ID.")
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track_file_id = st.text_input(
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label="File ID",
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key="track_file_id",
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help="Enter the file ID to track the file upload status",
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)
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track_button = st.button("Track")
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if track_file_id != "" and track_button:
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with st.spinner("Tracking..."):
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try:
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response = client.files.retrieve(track_file_id)
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st.write(response.model_dump())
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st.write(f"status: {response.status}")
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except Exception as e:
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st.error(e)
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st.markdown("---")
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# ---------------
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# Create batch job
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# ---------------
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st.header("Create batch job")
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st.info("Create a batch job using the file ID")
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batch_file_id = st.text_input(
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label="File ID",
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key="batch_file_id",
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help="Enter the file ID to track the file upload status",
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)
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batch_button = st.button("Create batch job")
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if batch_file_id != "" and batch_button:
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with st.spinner("Creating..."):
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try:
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response = client.batches.create(
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input_file_id=batch_file_id,
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endpoint="/chat/completions",
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completion_window="24h",
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)
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st.write(response.model_dump())
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except Exception as e:
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st.error(e)
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st.markdown("---")
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# ---------------
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# Track batch job progress
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# ---------------
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st.header("Track batch job progress")
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st.info("Track the batch job progress using the job ID")
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track_batch_job_id = st.text_input(
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label="Batch job ID",
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key="track_batch_job_id",
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help="Enter the batch job ID to track the job progress",
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)
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track_batch_job_button = st.button("Track batch job")
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if track_batch_job_id != "" and track_batch_job_button:
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with st.spinner("Tracking..."):
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try:
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response = client.batches.retrieve(track_batch_job_id)
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st.write(response.model_dump())
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st.write(f"status: {response.status}")
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st.write(f"output_file_id: {response.output_file_id}")
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except Exception as e:
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st.error(e)
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st.markdown("---")
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# ---------------
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# Retrieve batch job output file
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# ---------------
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st.header("Retrieve batch job output file")
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st.info("Retrieve the batch job output file using the job ID")
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output_file_id = st.text_input(
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label="output_file_id",
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key="retrieve_batch_job_id",
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help="Enter the batch job ID to retrieve the output file",
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)
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retrieve_batch_job_button = st.button("Retrieve batch job output file")
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if output_file_id != "" and retrieve_batch_job_button:
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with st.spinner("Retrieving..."):
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try:
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file_response = client.files.content(output_file_id)
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raw_responses = file_response.text.strip().split("\n")
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for raw_response in raw_responses:
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json_response = json.loads(raw_response)
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st.write(json_response)
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output_encoded = base64.b64encode(json.dumps(json_response, indent=2).encode()).decode()
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# Generate a link to download the result
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st.markdown(
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f'<a href="data:file/txt;base64,{output_encoded}" download="{output_file_id}.json">Download Result</a>',
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unsafe_allow_html=True,
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)
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except Exception as e:
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st.error(e)

‎apps/8_streamlit_azure_openai_batch/test.jsonl‎

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{"custom_id": "task-0", "method": "POST", "url": "/chat/completions", "body": {"model": "gpt-4o", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "When was Microsoft founded?"}]}}
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{"custom_id": "task-1", "method": "POST", "url": "/chat/completions", "body": {"model": "gpt-4o", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "When was the first XBOX released?"}]}}
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{"custom_id": "task-2", "method": "POST", "url": "/chat/completions", "body": {"model": "gpt-4o", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "What is Altair Basic?"}]}}

‎docs/images/8_streamlit_azure_openai_batch.main.png‎

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‎pyproject.toml‎

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[tool.ruff]
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line-length = 120
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target-version = "py310"
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exclude = [
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".jsonl"
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]
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[tool.ruff.lint]
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select = ["E", "F", "I", "UP"]

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