The xFusionCorp Industries ML team uses uv and lockfiles to keep Python dependencies reproducible across machines. A teammate has left behind a requirements.in specification that does not match the team's standard. Correct it and compile it into a pinned lockfile.
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A high-level dependency specification exists at
/root/code/fraud-detection/requirements.in.uvis already installed. -
The corrected specification must meet the following requirements:
- it lists exactly these four top-level packages:
scikit-learn,mlflow,pandas, andnumpy; - every package carries a version constraint that uv can actually satisfy against PyPI.
- it lists exactly these four top-level packages:
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Review the existing
requirements.in, and correct everything that does not match the requirements above. -
From the project directory, compile the corrected specification into a pinned lockfile:
uv pip compile requirements.in -o `requirements.txt` -
The resulting
requirements.txtmust pin each of the four top-level packages to an exact version using==, and must also include the transitive dependencies thatuvresolved.
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Update the
requirements.infile like this:# Fraud detection project dependencies scikit-learn mlflow==3.12.0 numpy pandasWhat we did here? We have replaced
sklearnwithscikit-learn, setmlflowrequired version 3.12.0 and addedpandaslibrary. -
Run the uv pip command to create
requirements.txtfromrequirements.in:uv pip compile fraud-detection/requirements.in -o fraud-detection/requirements.txt
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It will create the
requirements.txtlike below:# This file was autogenerated by uv via the following command: # uv pip compile fraud-detection/requirements.in -o requirements.txt aiohappyeyeballs==2.6.1 # via aiohttp aiohttp==3.13.5 # via mlflow aiosignal==1.4.0 # via aiohttp alembic==1.18.4 # via mlflow annotated-doc==0.0.4 # via fastapi annotated-types==0.7.0 # via pydantic anyio==4.13.0 # via starlette attrs==26.1.0 # via aiohttp blinker==1.9.0 # via flask cachetools==7.1.1 # via # mlflow-skinny # mlflow-tracing certifi==2026.4.22 # via requests cffi==2.0.0 # via cryptography charset-normalizer==3.4.7 # via requests click==8.3.3 # via # flask # mlflow-skinny # uvicorn cloudpickle==3.1.2 # via mlflow-skinny contourpy==1.3.3 # via matplotlib cryptography==46.0.7 # via # google-auth # mlflow cycler==0.12.1 # via matplotlib databricks-sdk==0.108.0 # via # mlflow-skinny # mlflow-tracing docker==7.1.0 # via mlflow fastapi==0.136.1 # via mlflow-skinny flask==3.1.3 # via # flask-cors # mlflow flask-cors==6.0.2 # via mlflow fonttools==4.63.0 # via matplotlib frozenlist==1.8.0 # via # aiohttp # aiosignal gitdb==4.0.12 # via gitpython gitpython==3.1.50 # via mlflow-skinny google-auth==2.52.0 # via databricks-sdk graphene==3.4.3 # via mlflow graphql-core==3.2.8 # via # graphene # graphql-relay graphql-relay==3.2.0 # via graphene greenlet==3.5.0 # via sqlalchemy gunicorn==25.3.0 # via mlflow h11==0.16.0 # via uvicorn huey==2.6.0 # via mlflow idna==3.15 # via # anyio # requests # yarl importlib-metadata==8.7.1 # via # mlflow-skinny # opentelemetry-api itsdangerous==2.2.0 # via flask jinja2==3.1.6 # via flask joblib==1.5.3 # via scikit-learn kiwisolver==1.5.0 # via matplotlib mako==1.3.12 # via alembic markupsafe==3.0.3 # via # flask # jinja2 # mako # werkzeug matplotlib==3.10.9 # via mlflow mlflow==3.12.0 # via -r fraud-detection/requirements.in mlflow-skinny==3.12.0 # via mlflow mlflow-tracing==3.12.0 # via mlflow multidict==6.7.1 # via # aiohttp # yarl numpy==2.4.4 # via # -r fraud-detection/requirements.in # contourpy # matplotlib # mlflow # pandas # scikit-learn # scipy # skops opentelemetry-api==1.41.1 # via # mlflow-skinny # mlflow-tracing # opentelemetry-sdk # opentelemetry-semantic-conventions opentelemetry-proto==1.41.1 # via # mlflow-skinny # mlflow-tracing opentelemetry-sdk==1.41.1 # via # mlflow-skinny # mlflow-tracing opentelemetry-semantic-conventions==0.62b1 # via opentelemetry-sdk packaging==26.2 # via # gunicorn # matplotlib # mlflow-skinny # mlflow-tracing # skops pandas==2.3.3 # via # -r fraud-detection/requirements.in # mlflow pillow==12.2.0 # via matplotlib prettytable==3.17.0 # via skops propcache==0.5.2 # via # aiohttp # yarl protobuf==6.33.6 # via # databricks-sdk # mlflow-skinny # mlflow-tracing # opentelemetry-proto pyarrow==23.0.1 # via mlflow pyasn1==0.6.3 # via pyasn1-modules pyasn1-modules==0.4.2 # via google-auth pycparser==3.0 # via cffi pydantic==2.13.4 # via # fastapi # mlflow-skinny # mlflow-tracing pydantic-core==2.46.4 # via pydantic pyparsing==3.3.2 # via matplotlib python-dateutil==2.9.0.post0 # via # graphene # matplotlib # pandas python-dotenv==1.2.2 # via mlflow-skinny pytz==2026.2 # via pandas pyyaml==6.0.3 # via mlflow-skinny requests==2.34.2 # via # databricks-sdk # docker # mlflow-skinny scikit-learn==1.8.0 # via # -r fraud-detection/requirements.in # mlflow # skops scipy==1.17.1 # via # mlflow # scikit-learn # skops six==1.17.0 # via python-dateutil skops==0.14.0 # via mlflow smmap==5.0.3 # via gitdb sqlalchemy==2.0.49 # via # alembic # mlflow sqlparse==0.5.5 # via mlflow-skinny starlette==0.52.1 # via # fastapi # mlflow-skinny threadpoolctl==3.6.0 # via scikit-learn typing-extensions==4.15.0 # via # aiosignal # alembic # anyio # fastapi # graphene # mlflow-skinny # opentelemetry-api # opentelemetry-sdk # opentelemetry-semantic-conventions # pydantic # pydantic-core # sqlalchemy # starlette # typing-inspection typing-inspection==0.4.2 # via # fastapi # pydantic tzdata==2026.2 # via pandas urllib3==2.7.0 # via # docker # requests uvicorn==0.47.0 # via mlflow-skinny wcwidth==0.7.0 # via prettytable werkzeug==3.1.8 # via # flask # flask-cors yarl==1.23.0 # via aiohttp zipp==3.23.1 # via importlib-metadata