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README.md

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-# bofire-candidates-api
-A FastAPI based application that can be used to generate candidates via https using BoFire.
+# BoFire Candidates API
+
+[![Test](https://github.com/experimental-design/bofire-candidates-api/workflows/Tests/badge.svg)](https://github.com/experimental-design/bofire-candidates-api/actions?query=workflow%3ATests)
+[![Lint](https://github.com/experimental-design/bofire-candidates-api/workflows/Lint/badge.svg)](https://github.com/experimental-design/bofire-candidates-api/actions?query=workflow%3ALint)
+
+
+An **experimental** FastAPI based application that can be used to generate candidates via https using BoFire. It makes use of the pydantic `data_models` in BoFire, which allows for an easy fastAPI integration include full Swagger documentation which can be found when visiting `/docs` of the running web application.
+
+Currently candidates are generated directly at request which can lead to http timeouts and other problems. Future versions should include an asynchronous worker based scenario for generating candidates.
+
+## Usage
+
+The following snippet shows how to use the candidates API via Pythons `request` module.
+
+```python
+import json
+from typing import Optional
+
+import requests
+from bofire.benchmarks.api import Himmelblau
+from bofire.data_models.dataframes.api import Candidates, Experiments
+from bofire.data_models.strategies.api import (
+    AlwaysTrueCondition,
+    AnyStrategy,
+    NumberOfExperimentsCondition,
+    RandomStrategy,
+    SoboStrategy,
+    Step,
+    StepwiseStrategy,
+)
+from pydantic import BaseModel
+
+
+class CandidateRequest(BaseModel):
+    strategy_data: AnyStrategy
+    n_candidates: int
+    experiments: Optional[Experiments]
+    pendings: Optional[Candidates]
+
+# generate experimental data from the Himmelblau benchmark
+bench = Himmelblau()
+experiments = bench.f(bench.domain.inputs.sample(10), return_complete=True)
+
+# setup the strategy for which candidates should be generated
+strategy_data = StepwiseStrategy(
+    domain=bench.domain,
+    steps=[
+        Step(
+            condition=NumberOfExperimentsCondition(
+                n_experiments=10
+            ),
+            strategy_data=RandomStrategy(domain=bench.domain)
+        ),
+        Step(
+            condition=AlwaysTrueCondition(),
+            strategy_data=SoboStrategy(domain=bench.domain)
+        )
+    ]
+)
+
+# create the payload
+payload = CandidateRequest(
+    strategy_data=strategy_data,
+    n_candidates=1,
+    experiments=Experiments.from_pandas(experiments, bench.domain),
+    pendings=None
+)
+
+
+URL = "http://127.0.0.1:8000/candidates"
+HEADERS = {'accept': 'application/json', 'Content-Type': 'application/json'}
+
+# request candidates
+response = requests.post(url=f"{URL}/generate", data=payload.model_dump_json(), headers=HEADERS)
+
+# convert response to a pandas dataframe
+df_candidates =Candidates(**json.loads(response.content)).to_pandas()
+
+```
+
+
+## Installation
+
+Use the following command to set and run the API locally as well as run the unit tests.
+
+### Setup
+
+```bash
+pip install -r requirements.txt
+```
+
+
+### Run
+```bash
+uvicorn --app-dir app:app --reload
+```
+
+### Run unit tests
+
+```bash
+pytest
+```