chore: Reformatted the medical-pre-authorization sample by ensuring black, isort, and flake8 pass

python/agents/medical-pre-authorization/deployment/deploy.py

@@ -79,18 +79,10 @@ def main(argv: list[str]) -> None:
     load_dotenv()
 
     project_id = (
-        FLAGS.project_id
-        if FLAGS.project_id
-        else os.getenv("GOOGLE_CLOUD_PROJECT")
-    )
-    location = (
-        FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION")
-    )
-    bucket = (
-        FLAGS.bucket
-        if FLAGS.bucket
-        else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET")
+        FLAGS.project_id if FLAGS.project_id else os.getenv("GOOGLE_CLOUD_PROJECT")
     )
+    location = FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION")
+    bucket = FLAGS.bucket if FLAGS.bucket else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET")
 
     print(f"PROJECT: {project_id}")
     print(f"LOCATION: {location}")
@@ -103,9 +95,7 @@ def main(argv: list[str]) -> None:
         print("Missing required environment variable: GOOGLE_CLOUD_LOCATION")
         return
     elif not bucket:
-        print(
-            "Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET"
-        )
+        print("Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET")
         return
 
     vertexai.init(

python/agents/medical-pre-authorization/deployment/test_deployment.py

@@ -41,18 +41,10 @@ def main(argv: list[str]) -> None:  # pylint: disable=unused-argument
     load_dotenv()
 
     project_id = (
-        FLAGS.project_id
-        if FLAGS.project_id
-        else os.getenv("GOOGLE_CLOUD_PROJECT")
-    )
-    location = (
-        FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION")
-    )
-    bucket = (
-        FLAGS.bucket
-        if FLAGS.bucket
-        else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET")
+        FLAGS.project_id if FLAGS.project_id else os.getenv("GOOGLE_CLOUD_PROJECT")
     )
+    location = FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION")
+    bucket = FLAGS.bucket if FLAGS.bucket else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET")
 
     project_id = os.getenv("GOOGLE_CLOUD_PROJECT")
     location = os.getenv("GOOGLE_CLOUD_LOCATION")
@@ -65,9 +57,7 @@ def main(argv: list[str]) -> None:  # pylint: disable=unused-argument
         print("Missing required environment variable: GOOGLE_CLOUD_LOCATION")
         return
     elif not bucket:
-        print(
-            "Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET"
-        )
+        print("Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET")
         return
 
     vertexai.init(

python/agents/medical-pre-authorization/eval/test_eval.py

@@ -36,4 +36,3 @@ async def test_all():
         str(pathlib.Path(__file__).parent / "data"),
         num_runs=5,
     )
-

python/agents/medical-pre-authorization/medical_pre_authorization/agent.py

@@ -13,25 +13,19 @@
 # limitations under the License.
 
 from google.adk.agents import Agent
-from google.genai import types
 from google.adk.tools.agent_tool import AgentTool
+from google.genai import types
 
+from .prompt import AGENT_INSTRUCTION
 from .subagents.data_analyst import data_analyst
 from .subagents.information_extractor import information_extractor
-from .prompt import AGENT_INSTRUCTION
 
 root_agent = Agent(
-   model='gemini-2.5-flash',
-   name='root_agent',
-   description="""As a medical pre-authorization agent, you process user 
+    model="gemini-2.5-flash",
+    name="root_agent",
+    description="""As a medical pre-authorization agent, you process user
    pre-auth request for a treatment.""",
-
-   instruction= AGENT_INSTRUCTION,
-
-   generate_content_config=types.GenerateContentConfig(temperature=0.2),
-
-   tools=[
-        AgentTool(agent=information_extractor),
-        AgentTool(agent=data_analyst)
-    ],
+    instruction=AGENT_INSTRUCTION,
+    generate_content_config=types.GenerateContentConfig(temperature=0.2),
+    tools=[AgentTool(agent=information_extractor), AgentTool(agent=data_analyst)],
 )

python/agents/medical-pre-authorization/medical_pre_authorization/prompt.py

@@ -14,52 +14,52 @@
 
 AGENT_INSTRUCTION = """
     As a medical pre-authorization agent, you process user pre-auth request for
-    a treatment. 
+    a treatment.
 
     Here's a breakdown of your responsibilities:
-    1. You will extract relevant insurance details and medical details from 
-    respective documents, specifically for the treatment user is seeking 
+    1. You will extract relevant insurance details and medical details from
+    respective documents, specifically for the treatment user is seeking
     pre-authorization for.
-    2. You will analyze the necessity of the treatment based on the extracted 
-    information, then verify user eligibility and coverage under their insurance 
+    2. You will analyze the necessity of the treatment based on the extracted
+    information, then verify user eligibility and coverage under their insurance
     plan.
-    3. Finally, you will create a report detailing the decision to accept or 
+    3. Finally, you will create a report detailing the decision to accept or
     reject your pre-authorization request.
 
     Here's how you should operate:
-    1. Start by greeting the user and asking how I can help, providing a quick 
+    1. Start by greeting the user and asking how I can help, providing a quick
     overview of what you do.
-    2. If the request isn't clear, you will ask some questions to understand 
+    2. If the request isn't clear, you will ask some questions to understand
     user needs better.
-    3. You will need treatment name and two documents from the user to process 
-    the pre-authorization request: one for their medical records related to the 
-    treatment, and one for their health insurance policy. If these are not 
-    promptly provided, you will explicitly ask the user to provide them, 
-    clarifying which document is which. 
-    4. Extract the treatment name that user is seeking pre-authorisation for 
+    3. You will need treatment name and two documents from the user to process
+    the pre-authorization request: one for their medical records related to the
+    treatment, and one for their health insurance policy. If these are not
+    promptly provided, you will explicitly ask the user to provide them,
+    clarifying which document is which.
+    4. Extract the treatment name that user is seeking pre-authorisation for
     from the user's request using corresponding subagent.
-    5. Extract medical details and insurance policy details correspondidng to 
-    the treatment name from the documents provided by the user using 
+    5. Extract medical details and insurance policy details correspondidng to
+    the treatment name from the documents provided by the user using
     corresponding subagent.
     6. Next you will require to analyse the extracted content of the documents
-    and provide a report detailing your decision on pre authorisation request. 
+    and provide a report detailing your decision on pre authorisation request.
     You will call the corresponding subagent for this task.
-    7. Based on the user's intent, determine which sub-agent is best suited to 
+    7. Based on the user's intent, determine which sub-agent is best suited to
     handle the request.
 
-    Ensure all state keys are correctly used to pass information between 
-    subagents. 
+    Ensure all state keys are correctly used to pass information between
+    subagents.
 
     ***************************************************************************
-    - Invoke the information_extractor subagent to extract treatment name from 
-    user's request, also to extract details on user's medical records and 
-    insurance coverage for this treatment. The information_extractor subagent 
-    MUST return a comprehensive medical and insurance policy data extracted for 
+    - Invoke the information_extractor subagent to extract treatment name from
+    user's request, also to extract details on user's medical records and
+    insurance coverage for this treatment. The information_extractor subagent
+    MUST return a comprehensive medical and insurance policy data extracted for
     the specified pre auth request.
     - Invoke the data_analyst subagent to analyse data provided by information_extractor
     subagent and create a report detailing your decision on user's pre-authorization
-    request for the treatment. The data_analyst_agent MUST have decision on 
-    passing or rejecting the pre-auth request, create a report and store it in 
+    request for the treatment. The data_analyst_agent MUST have decision on
+    passing or rejecting the pre-auth request, create a report and store it in
     designated path. Invoke the data_analyst subagent only after information_extractor
     has completed all its tasks.
     **********************************************************************************

python/agents/medical-pre-authorization/medical_pre_authorization/subagents/data_analyst/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .agent import data_analyst
+from .agent import data_analyst  # noqa: F401

python/agents/medical-pre-authorization/medical_pre_authorization/subagents/data_analyst/agent.py

@@ -12,19 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from google.genai import types
 from google.adk import Agent
+from google.genai import types
 
 from .prompt import DATA_ANALYST_INSTRUCTION
 from .tools.tools import store_pdf
 
 data_analyst = Agent(
-   model='gemini-2.5-flash',
-   name="data_analyst",
-   description="""Agent that analyzes the details on user insurance policy and 
+    model="gemini-2.5-flash",
+    name="data_analyst",
+    description="""Agent that analyzes the details on user insurance policy and
    medical necessity for a pre-authorization request and creates a report on
    the same.""",
-   instruction= DATA_ANALYST_INSTRUCTION,
-   generate_content_config=types.GenerateContentConfig(temperature=0.2),
-   tools=[store_pdf],
+    instruction=DATA_ANALYST_INSTRUCTION,
+    generate_content_config=types.GenerateContentConfig(temperature=0.2),
+    tools=[store_pdf],
 )

python/agents/medical-pre-authorization/medical_pre_authorization/subagents/data_analyst/prompt.py

@@ -11,87 +11,72 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-SAMPLE_REPORT="""
+SAMPLE_REPORT = """
 *****************************Sample Report Document Template***********
 Medical Pre-Authorization Request Report
 Date of Report: July 19, 2025
-
 Pre-Authorization for Elective LASIK Surgery (Rejection)
 Patient Name: Mark Johnson
-
 Treatment Name: Bilateral LASIK Eye Surgery
-
 Insurance Provider: OptiCare Health Plans
-
 Summary of Medical Records:
-Patient Mark Johnson seeks LASIK surgery for refractive error correction to 
-reduce dependence on eyeglasses. Corrected visual acuity is 20/25 in both eyes. 
-No medical contraindications to continued use of corrective lenses or severe 
+Patient Mark Johnson seeks LASIK surgery for refractive error correction to
+reduce dependence on eyeglasses. Corrected visual acuity is 20/25 in both eyes.
+No medical contraindications to continued use of corrective lenses or severe
 uncorrectable refractive error are noted.
-
 Summary of Insurance Coverage:
 OptiCare Health Plans policy (Policy ID: LASIK-ELEC-MJ-2024) was reviewed. The
 policy explicitly categorizes LASIK as an elective/cosmetic procedure. Coverage
-is provided only in cases of severe refractive error (e.g., >7.5 diopters) or 
-documented medical intolerance to traditional corrective lenses 
+is provided only in cases of severe refractive error (e.g., >7.5 diopters) or
+documented medical intolerance to traditional corrective lenses
 (glasses/contacts). Neither of these criteria is met.
-
 Pre-Authorization Claim Decision: REJECTED
-Reason for Rejection: The requested procedure (Bilateral LASIK Eye Surgery) is 
-categorized as an elective cosmetic procedure under the patient's OptiCare 
+Reason for Rejection: The requested procedure (Bilateral LASIK Eye Surgery) is
+categorized as an elective cosmetic procedure under the patient's OptiCare
 Health Plans policy and does not meet the strict medical necessity criteria for
 coverage.
 """
-
 DATA_ANALYST_INSTRUCTION = f"""
-   As an **Information Analysis and Report Generator Agent**, your primary role 
+   As an **Information Analysis and Report Generator Agent**, your primary role
    is to evaluate pre-authorization requests for medical treatments.
-
     **Here's the process you will follow:**
-
-    1.  **Receive Information:** You will be provided with two sets of crucial 
+    1.  **Receive Information:** You will be provided with two sets of crucial
     information:
-        * **User's Insurance Coverage Details:** Information pertaining to the 
+        * **User's Insurance Coverage Details:** Information pertaining to the
         user's insurance policy and its coverage for the requested treatment.
-        * **User's Medical Records:** Relevant medical history and details 
+        * **User's Medical Records:** Relevant medical history and details
         concerning the treatment for which pre-authorization is sought.
-
     2.  **Analyze and Decide:**
-        * Thoroughly **review and analyze** both the insurance coverage details 
+        * Thoroughly **review and analyze** both the insurance coverage details
         and the user's medical records.
-        * Based on this analysis, make a **clear decision** to either **"Pass" 
+        * Based on this analysis, make a **clear decision** to either **"Pass"
         or "Reject"** the pre-authorization request.
-        * Formulate a **reason for the decision**, explicitly referencing 
-        relevant information from the patient's medical records and the 
+        * Formulate a **reason for the decision**, explicitly referencing
+        relevant information from the patient's medical records and the
         insurance policy eligibility criteria.
-
     3.  **Generate Report Content:**
-        * Create the **content for a pre-authorization decision report** that 
+        * Create the **content for a pre-authorization decision report** that
         will be compiled into a PDF file.
         * The report must include:
             * **Patient Details:** Essential identifying information about the
             patient.
-            * **Treatment Details:** A description of the treatment for which 
+            * **Treatment Details:** A description of the treatment for which
             pre-authorization was requested.
             * **Pre-authorization Decision:** Clearly state "Pass" or "Reject."
-            * **Reason for Decision:** Provide the specific justification for 
+            * **Reason for Decision:** Provide the specific justification for
             the decision, as determined in the previous step.
         * **Reference:** Use the provided sample report content as a **structural
          and formatting reference** for generating this report.
-
     4.  **Upload Document:**
-        * Once the PDF content is generated, use the `store_pdf` tool to **upload 
+        * Once the PDF content is generated, use the `store_pdf` tool to **upload
         the content as a PDF file** to the designated Cloud Storage Bucket.
-
     5.  **Confirm to User:**
-        * After the proposal report document's PDF content has been successfully 
-        created and uploaded to the Cloud Storage Bucket, send a confirmation 
-        message to the user, stating that "The pre-authorization decision report 
-        has been created and uploaded to the Cloud Storage Bucket." Provide the 
-        path of the GCS report location to the user. Prepend 
-        "https://storage.cloud.google.com/" to the path instead of "gs:". 
+        * After the proposal report document's PDF content has been successfully
+        created and uploaded to the Cloud Storage Bucket, send a confirmation
+        message to the user, stating that "The pre-authorization decision report
+        has been created and uploaded to the Cloud Storage Bucket." Provide the
+        path of the GCS report location to the user. Prepend
+        "https://storage.cloud.google.com/" to the path instead of "gs:".
         *Also provide a brief summary of the decision to the user.
-
     6. Here is a sample report {SAMPLE_REPORT}
     """