diff --git "a/Logger.txt" "b/Logger.txt" --- "a/Logger.txt" +++ "b/Logger.txt" @@ -17762,3 +17762,2068 @@ Logger Data: This is Insight's lab log data. 2025-02-04 19:49:31 INFO Table details fetched successfully. 2025-02-04 19:50:37 INFO Database names fetched successfully. 2025-02-04 19:50:37 INFO Metadata fetched for table: Patient +2025-02-17 11:37:49 INFO Date: 2025-02-17 +======================================== +Time: 11:37:49 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-17 11:37:53 INFO not logined +2025-02-17 11:37:53 INFO Rendering unauthenticated menu. +2025-02-17 11:38:27 INFO Login button clicked. +2025-02-17 11:38:30 INFO Login successful for user: maheshsr +2025-02-17 11:38:55 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-17 12:45:28 INFO Date: 2025-02-17 +======================================== +Time: 12:45:28 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-17 12:45:28 INFO not logined +2025-02-17 12:45:28 INFO not logined +2025-02-17 12:45:28 INFO Rendering unauthenticated menu. +2025-02-17 12:45:28 INFO Rendering unauthenticated menu. +2025-02-17 12:45:49 INFO Login button clicked. +2025-02-17 12:45:49 INFO Login button clicked. +2025-02-17 12:45:53 INFO Login successful for user: maheshsr +2025-02-17 12:45:53 INFO Login successful for user: maheshsr +2025-02-17 12:46:09 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-17 12:46:09 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-17 13:07:25 INFO Date: 2025-02-17 +======================================== +Time: 13:07:25 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-17 13:07:30 INFO not logined +2025-02-17 13:07:30 INFO Rendering unauthenticated menu. +2025-02-17 13:07:48 INFO Login button clicked. +2025-02-17 13:07:52 INFO Login successful for user: maheshsr +2025-02-17 13:08:11 ERROR Error fetching database names: Not an executable object: "\n SELECT name \n FROM sys.databases\n WHERE name NOT IN ('master', 'tempdb', 'model', 'msdb');\n " +2025-02-18 20:32:22 INFO Date: 2025-02-18 +======================================== +Time: 20:32:22 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-18 20:32:27 INFO not logined +2025-02-18 20:32:27 INFO Rendering unauthenticated menu. +2025-02-18 21:39:35 INFO Date: 2025-02-18 +======================================== +Time: 21:39:35 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-18 21:39:35 INFO Date: 2025-02-18 +======================================== +Time: 21:39:35 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-18 21:39:35 INFO Login button clicked. +2025-02-18 21:39:35 INFO Login button clicked. +2025-02-18 21:39:43 INFO Login successful for user: maheshsr +2025-02-18 21:40:41 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-18 21:40:41 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-19 15:18:10 INFO Date: 2025-02-19 +======================================== +Time: 15:18:10 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-19 15:18:15 INFO not logined +2025-02-19 15:18:15 INFO Rendering unauthenticated menu. +2025-02-19 15:18:38 INFO Login button clicked. +2025-02-19 15:18:41 INFO Login successful for user: abhishek +2025-02-19 15:19:32 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2025-02-19 15:23:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json +2025-02-19 15:23:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json +2025-02-19 15:23:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json +2025-02-19 15:23:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json +2025-02-19 15:23:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-19 15:23:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-19 15:23:57 ERROR Error fetching database names: Not an executable object: "\n SELECT name \n FROM sys.databases\n WHERE name NOT IN ('master', 'tempdb', 'model', 'msdb');\n " +2025-02-20 14:04:55 INFO Date: 2025-02-20 +======================================== +Time: 14:04:55 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-20 14:04:55 INFO not logined +2025-02-20 14:04:55 INFO not logined +2025-02-20 14:04:55 INFO Rendering unauthenticated menu. +2025-02-20 14:04:55 INFO Rendering unauthenticated menu. +2025-02-21 13:24:11 INFO Date: 2025-02-21 +======================================== +Time: 13:24:11 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 13:24:16 INFO not logined +2025-02-21 13:24:16 INFO Rendering unauthenticated menu. +2025-02-21 13:24:36 INFO Login button clicked. +2025-02-21 13:24:41 INFO Login successful for user: abhishek +2025-02-21 13:39:00 INFO Date: 2025-02-21 +======================================== +Time: 13:39:00 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 13:39:05 INFO not logined +2025-02-21 13:39:05 INFO Rendering unauthenticated menu. +2025-02-21 13:39:23 INFO Login button clicked. +2025-02-21 13:39:27 INFO Login successful for user: abhishek +2025-02-21 13:41:22 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' +2025-02-21 13:42:02 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' +2025-02-21 13:51:34 INFO Date: 2025-02-21 +======================================== +Time: 13:51:34 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 13:51:40 INFO not logined +2025-02-21 13:51:40 INFO Rendering unauthenticated menu. +2025-02-21 13:52:08 INFO Login button clicked. +2025-02-21 13:52:12 INFO Login successful for user: abhishek +2025-02-21 13:54:09 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' +2025-02-21 14:04:19 INFO Date: 2025-02-21 +======================================== +Time: 14:04:19 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 14:04:24 INFO not logined +2025-02-21 14:04:24 INFO Rendering unauthenticated menu. +2025-02-21 14:06:02 INFO Login button clicked. +2025-02-21 14:06:06 INFO Login successful for user: abhishek +2025-02-21 14:07:11 INFO Generating SQL query with prompt: + You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. + + I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. + Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` + + I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary + are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries + where each key is the column name and each value is the datatype. There may be multiple table structures described here. + The table structure is enclosed in triple backticks. + Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` + + Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + + This is the English language query that needs to be converted into an SQL Query within four backticks. + English language query: ````get all the patients```` + + Your task is to generate an SQL query that can be executed on a SQLite database. + Only produce the SQL query as a string. + Do NOT produce any backticks before or after. + Do NOT produce any JSON tags. + Do NOT produce any additional text that is not part of the query itself. + +2025-02-21 14:07:17 INFO Tokens consumed: 1486 +2025-02-21 14:07:20 INFO No existing token_consumed found for month: 2025-02 +2025-02-21 14:07:21 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True +2025-02-21 14:07:22 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 14:07:24 INFO New token created: token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/2025-02.json +2025-02-21 14:07:24 INFO Query executed successfully. +2025-02-21 14:07:26 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 61 +2025-02-21 14:07:28 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 14:07:30 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/62.json +2025-02-21 14:15:32 INFO Generating SQL query with prompt: + You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. + + I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. + Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` + + I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary + are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries + where each key is the column name and each value is the datatype. There may be multiple table structures described here. + The table structure is enclosed in triple backticks. + Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` + + Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + + This is the English language query that needs to be converted into an SQL Query within four backticks. + English language query: ````get all the risk score```` + + Your task is to generate an SQL query that can be executed on a SQLite database. + Only produce the SQL query as a string. + Do NOT produce any backticks before or after. + Do NOT produce any JSON tags. + Do NOT produce any additional text that is not part of the query itself. + +2025-02-21 14:15:39 INFO Tokens consumed: 1489 +2025-02-21 14:15:42 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 14:15:43 INFO token updated successfully: 2025-02 +2025-02-21 14:15:43 INFO token updated successfully. +2025-02-21 14:15:43 INFO Query executed successfully. +2025-02-21 14:15:45 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 62 +2025-02-21 14:15:48 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 14:15:50 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/63.json +2025-02-21 14:16:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json +2025-02-21 14:16:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json +2025-02-21 14:16:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json +2025-02-21 14:16:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json +2025-02-21 14:16:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 14:16:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 14:16:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json +2025-02-21 14:16:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json +2025-02-21 14:16:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json +2025-02-21 14:16:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json +2025-02-21 14:16:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json +2025-02-21 14:16:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 14:16:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json +2025-02-21 14:16:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 14:16:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json +2025-02-21 14:16:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json +2025-02-21 14:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json +2025-02-21 14:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json +2025-02-21 14:16:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 14:16:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json +2025-02-21 14:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 14:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json +2025-02-21 14:16:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 14:16:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 14:16:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 14:16:57 ERROR Error while querying the DB : no such table: Patient +2025-02-21 14:16:57 ERROR Error loading dataset: 'NoneType' object has no attribute 'columns' +2025-02-21 14:55:16 INFO Date: 2025-02-21 +======================================== +Time: 14:55:16 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 14:55:21 INFO not logined +2025-02-21 14:55:21 INFO Rendering unauthenticated menu. +2025-02-21 14:56:54 INFO Login button clicked. +2025-02-21 14:56:59 INFO Login successful for user: abhishek +2025-02-21 14:58:25 INFO Generating SQL query with prompt: + You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. + + I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. + Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` + + I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary + are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries + where each key is the column name and each value is the datatype. There may be multiple table structures described here. + The table structure is enclosed in triple backticks. + Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` + + Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + + This is the English language query that needs to be converted into an SQL Query within four backticks. + English language query: ````create a data set of all the patients```` + + Your task is to generate an SQL query that can be executed on a SQLite database. + Only produce the SQL query as a string. + Do NOT produce any backticks before or after. + Do NOT produce any JSON tags. + Do NOT produce any additional text that is not part of the query itself. + +2025-02-21 14:58:31 INFO Tokens consumed: 1490 +2025-02-21 14:58:33 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 14:58:35 INFO token updated successfully: 2025-02 +2025-02-21 14:58:35 INFO token updated successfully. +2025-02-21 14:58:35 INFO Query executed successfully. +2025-02-21 14:58:36 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 +2025-02-21 14:58:38 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: False +2025-02-21 14:58:40 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:00:32 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: False +2025-02-21 15:00:33 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:00:33 INFO Query saved in the library with id 1. +2025-02-21 15:00:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:01:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:01:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:01:41 INFO Query executed successfully. +2025-02-21 15:01:41 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:02:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:02:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:02:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:02:39 INFO Query executed successfully. +2025-02-21 15:02:39 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:02:39 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 60`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:02:42 INFO Tokens consumed: 937 +2025-02-21 15:02:48 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:02:49 INFO token updated successfully: 2025-02 +2025-02-21 15:02:49 INFO token updated successfully. +2025-02-21 15:02:51 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 +2025-02-21 15:02:52 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: False +2025-02-21 15:02:54 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/1.py +2025-02-21 15:02:54 INFO Code generated and written in generated_method//0.py +2025-02-21 15:02:54 INFO Insight generated and displayed using AG Grid. +2025-02-21 15:03:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:03:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:03:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:03:24 INFO Query executed successfully. +2025-02-21 15:03:24 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:03:24 INFO Generating graph with prompt: + You are an expert in understanding English language instructions to generate a graph based on a given dataframe. + + I am providing you the dataframe structure as a dictionary in double backticks. + Dataframe structure: `` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string`` + + I am also providing you a summary of the dataframe as a dictionary in double backticks. + Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` + + I have provided the dataframe structure and its summary. I can't provide the entire dataframe. + + I am also giving you the intent instruction in triple backticks. + Instruction for generating the graph: ```generate a bar graph of patient based on age group``` + + Your task is to write the code that will generate a Plotly chart. + You should be able to derive the chart type from the instruction. + Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. + + You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. + + Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. + Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. + Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` + + If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. + Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` + + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. + Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. + Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( + x='year:O', + y='sum(yield):Q', + column='site:N' + )```` + + A grouped bar chart will be explicitly asked for in the instructions. + + Only produce the Python code. + Do NOT produce any backticks or double quotes or single quotes before or after the code. + Do generate the Plotly import statement as part of the code. + Do NOT justify your code. + Do not generate any narrative or comments in the code. + Do NOT produce any JSON tags. + Do not print or return the chart object at the end. + Do NOT produce any additional text that is not part of the query itself. + Always name the final Plotly chart object as 'chart'. + Go back and check if the generated code can be used in the `st.plotly_chart()` method. + +2025-02-21 15:03:29 INFO Tokens consumed: 1694 +2025-02-21 15:03:31 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:03:32 INFO token updated successfully: 2025-02 +2025-02-21 15:03:32 INFO token updated successfully. +2025-02-21 15:03:36 INFO Plotly chart object created successfully. +2025-02-21 15:03:37 INFO Graph generated and displayed using Plotly. +2025-02-21 15:04:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:04:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:04:52 INFO Query executed successfully. +2025-02-21 15:04:52 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:05:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:05:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:05:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:05:14 INFO Query executed successfully. +2025-02-21 15:05:14 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:05:15 INFO No existing insight found for base code: SELECT * FROM Patient +2025-02-21 15:05:17 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False +2025-02-21 15:05:17 INFO Creating a new folder in the blob storage: +2025-02-21 15:05:20 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 +2025-02-21 15:05:21 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:05:23 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:06:24 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:06:24 INFO Insight list generated successfully. +2025-02-21 15:06:31 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:06:31 INFO Insight list generated successfully. +2025-02-21 15:06:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:06:33 INFO Query executed successfully. +2025-02-21 15:07:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:21 INFO Query executed successfully. +2025-02-21 15:07:21 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:07:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:07:38 INFO Query executed successfully. +2025-02-21 15:07:38 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:07:38 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:07:42 INFO Tokens consumed: 937 +2025-02-21 15:07:44 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:07:46 INFO token updated successfully: 2025-02 +2025-02-21 15:07:46 INFO token updated successfully. +2025-02-21 15:07:47 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 +2025-02-21 15:07:49 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:07:50 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/2.py +2025-02-21 15:07:50 INFO Code generated and written in generated_method//1.py +2025-02-21 15:07:50 INFO Insight generated and displayed using AG Grid. +2025-02-21 15:08:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:08:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:08:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:08:38 INFO Query executed successfully. +2025-02-21 15:08:38 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:08:38 INFO Generating graph with prompt: + You are an expert in understanding English language instructions to generate a graph based on a given dataframe. + + I am providing you the dataframe structure as a dictionary in double backticks. + Dataframe structure: `` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string`` + + I am also providing you a summary of the dataframe as a dictionary in double backticks. + Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` + + I have provided the dataframe structure and its summary. I can't provide the entire dataframe. + + I am also giving you the intent instruction in triple backticks. + Instruction for generating the graph: ```generate a scattered graph of no. of patient on age group``` + + Your task is to write the code that will generate a Plotly chart. + You should be able to derive the chart type from the instruction. + Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. + + You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. + + Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. + Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. + Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` + + If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. + Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` + + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. + Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. + Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( + x='year:O', + y='sum(yield):Q', + column='site:N' + )```` + + A grouped bar chart will be explicitly asked for in the instructions. + + Only produce the Python code. + Do NOT produce any backticks or double quotes or single quotes before or after the code. + Do generate the Plotly import statement as part of the code. + Do NOT justify your code. + Do not generate any narrative or comments in the code. + Do NOT produce any JSON tags. + Do not print or return the chart object at the end. + Do NOT produce any additional text that is not part of the query itself. + Always name the final Plotly chart object as 'chart'. + Go back and check if the generated code can be used in the `st.plotly_chart()` method. + +2025-02-21 15:08:52 INFO Tokens consumed: 2077 +2025-02-21 15:08:55 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:08:57 INFO token updated successfully: 2025-02 +2025-02-21 15:08:57 INFO token updated successfully. +2025-02-21 15:08:57 INFO Plotly chart object created successfully. +2025-02-21 15:08:57 ERROR Error in generating graph: %s +2025-02-21 15:09:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:09:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:09:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:09:33 INFO Query executed successfully. +2025-02-21 15:09:33 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:24:22 INFO Date: 2025-02-21 +======================================== +Time: 15:24:22 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 15:24:27 INFO not logined +2025-02-21 15:24:27 INFO Rendering unauthenticated menu. +2025-02-21 15:25:45 INFO Login button clicked. +2025-02-21 15:25:49 INFO Login successful for user: abhishek +2025-02-21 15:26:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:27:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:27:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:27:01 INFO Query executed successfully. +2025-02-21 15:27:01 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:28:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:28:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:28:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:28:15 INFO Query executed successfully. +2025-02-21 15:28:15 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:28:15 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:28:19 INFO Tokens consumed: 936 +2025-02-21 15:28:22 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:28:24 INFO token updated successfully: 2025-02 +2025-02-21 15:28:24 INFO token updated successfully. +2025-02-21 15:28:25 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 2 +2025-02-21 15:28:27 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:28:28 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/3.py +2025-02-21 15:28:28 INFO Code generated and written in generated_method//2.py +2025-02-21 15:28:29 ERROR Error executing generated code 2 for generate an insight of patient whose age is above 50: invalid syntax (, line 1) +2025-02-21 15:28:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:28:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:28:56 INFO Query executed successfully. +2025-02-21 15:28:56 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:28:56 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:28:59 INFO Tokens consumed: 939 +2025-02-21 15:29:01 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:29:03 INFO token updated successfully: 2025-02 +2025-02-21 15:29:03 INFO token updated successfully. +2025-02-21 15:29:05 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 3 +2025-02-21 15:29:06 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:29:08 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/4.py +2025-02-21 15:29:08 INFO Code generated and written in generated_method//3.py +2025-02-21 15:29:08 INFO Insight generated and displayed using AG Grid. +2025-02-21 15:29:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:29:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:29:44 INFO Query executed successfully. +2025-02-21 15:29:44 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:30:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:30:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:30:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:30:14 INFO Query executed successfully. +2025-02-21 15:30:14 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:30:14 INFO Generating graph with prompt: + You are an expert in understanding English language instructions to generate a graph based on a given dataframe. + + I am providing you the dataframe structure as a dictionary in double backticks. + Dataframe structure: `` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string`` + + I am also providing you a summary of the dataframe as a dictionary in double backticks. + Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` + + I have provided the dataframe structure and its summary. I can't provide the entire dataframe. + + I am also giving you the intent instruction in triple backticks. + Instruction for generating the graph: ```generate a scattered graph number of patient v/s age group``` + + Your task is to write the code that will generate a Plotly chart. + You should be able to derive the chart type from the instruction. + Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. + + You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. + + Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. + Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. + Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` + + If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. + Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` + + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. + Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. + Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( + x='year:O', + y='sum(yield):Q', + column='site:N' + )```` + + A grouped bar chart will be explicitly asked for in the instructions. + + Only produce the Python code. + Do NOT produce any backticks or double quotes or single quotes before or after the code. + Do generate the Plotly import statement as part of the code. + Do NOT justify your code. + Do not generate any narrative or comments in the code. + Do NOT produce any JSON tags. + Do not print or return the chart object at the end. + Do NOT produce any additional text that is not part of the query itself. + Always name the final Plotly chart object as 'chart'. + Go back and check if the generated code can be used in the `st.plotly_chart()` method. + +2025-02-21 15:30:26 INFO Tokens consumed: 2136 +2025-02-21 15:30:28 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:30:30 INFO token updated successfully: 2025-02 +2025-02-21 15:30:30 INFO token updated successfully. +2025-02-21 15:30:33 INFO Plotly chart object created successfully. +2025-02-21 15:31:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:31:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:31:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:31:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:31:33 INFO Query executed successfully. +2025-02-21 15:31:33 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:31:35 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) +2025-02-21 15:31:37 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True +2025-02-21 15:31:38 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 +2025-02-21 15:31:39 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:31:41 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 15:37:23 INFO Date: 2025-02-21 +======================================== +Time: 15:37:23 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 15:37:27 INFO not logined +2025-02-21 15:37:27 INFO Rendering unauthenticated menu. +2025-02-21 15:37:46 INFO Login button clicked. +2025-02-21 15:37:50 INFO Login successful for user: abhishek +2025-02-21 15:38:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:39:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:39:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:39:36 INFO Query executed successfully. +2025-02-21 15:39:36 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:40:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:40:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:40:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:40:22 INFO Query executed successfully. +2025-02-21 15:40:22 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:40:22 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:40:25 INFO Tokens consumed: 939 +2025-02-21 15:40:28 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:40:29 INFO token updated successfully: 2025-02 +2025-02-21 15:40:29 INFO token updated successfully. +2025-02-21 15:40:31 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 4 +2025-02-21 15:40:32 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:40:34 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/5.py +2025-02-21 15:40:34 INFO Code generated and written in generated_method//4.py +2025-02-21 15:40:34 INFO Insight generated and displayed using AG Grid. +2025-02-21 15:41:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:41:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:41:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:41:44 INFO Query executed successfully. +2025-02-21 15:41:44 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:41:46 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) +2025-02-21 15:41:47 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True +2025-02-21 15:41:49 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 +2025-02-21 15:41:50 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:41:51 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 15:45:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:45:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:45:13 INFO Query executed successfully. +2025-02-21 15:45:13 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:45:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:45:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:45:40 INFO Query executed successfully. +2025-02-21 15:45:40 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:45:42 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) +2025-02-21 15:45:44 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True +2025-02-21 15:45:45 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 +2025-02-21 15:45:46 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:45:48 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 15:47:34 INFO Date: 2025-02-21 +======================================== +Time: 15:47:34 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 15:47:39 INFO not logined +2025-02-21 15:47:39 INFO Rendering unauthenticated menu. +2025-02-21 15:49:17 INFO Login button clicked. +2025-02-21 15:49:21 INFO Login successful for user: abhishek +2025-02-21 15:52:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:57:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:57:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:57:16 INFO Query executed successfully. +2025-02-21 15:57:16 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:58:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:58:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:58:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:58:52 INFO Query executed successfully. +2025-02-21 15:58:52 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:58:52 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 15:58:55 INFO Tokens consumed: 939 +2025-02-21 15:58:58 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 15:58:59 INFO token updated successfully: 2025-02 +2025-02-21 15:58:59 INFO token updated successfully. +2025-02-21 15:59:01 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 5 +2025-02-21 15:59:03 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:59:04 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/6.py +2025-02-21 15:59:04 INFO Code generated and written in generated_method//5.py +2025-02-21 15:59:04 INFO Insight generated and displayed using AG Grid. +2025-02-21 15:59:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:59:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:59:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 15:59:39 INFO Query executed successfully. +2025-02-21 15:59:39 INFO Dataset columns displayed using AG Grid. +2025-02-21 15:59:41 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) +2025-02-21 15:59:42 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True +2025-02-21 15:59:44 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 +2025-02-21 15:59:45 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 15:59:47 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 16:04:16 INFO Date: 2025-02-21 +======================================== +Time: 16:04:16 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 16:04:21 INFO not logined +2025-02-21 16:04:21 INFO Rendering unauthenticated menu. +2025-02-21 16:04:45 INFO Login button clicked. +2025-02-21 16:04:49 INFO Login successful for user: abhishek +2025-02-21 16:05:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:08:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:08:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:08:18 INFO Query executed successfully. +2025-02-21 16:08:18 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:09:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:02 INFO Query executed successfully. +2025-02-21 16:09:02 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:09:02 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 16:09:10 INFO Tokens consumed: 937 +2025-02-21 16:09:12 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:09:14 INFO token updated successfully: 2025-02 +2025-02-21 16:09:14 INFO token updated successfully. +2025-02-21 16:09:15 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 6 +2025-02-21 16:09:17 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:09:18 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/7.py +2025-02-21 16:09:18 INFO Code generated and written in generated_method//6.py +2025-02-21 16:09:18 INFO Insight generated and displayed using AG Grid. +2025-02-21 16:09:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:09:46 INFO Query executed successfully. +2025-02-21 16:09:46 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:09:48 WARNING Skipping empty file: %s +2025-02-21 16:09:49 INFO Existing insight found for base code: %s +2025-02-21 16:09:51 INFO Insight updated successfully: %s +2025-02-21 16:09:51 INFO Insight updated successfully. +2025-02-21 16:11:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json +2025-02-21 16:11:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json +2025-02-21 16:11:34 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:11:34 INFO Insight list generated successfully. +2025-02-21 16:12:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:12:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:12:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:12:49 INFO Query executed successfully. +2025-02-21 16:12:49 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:13:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:02 INFO Query executed successfully. +2025-02-21 16:13:02 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:13:03 INFO No existing insight found for base code: %s +2025-02-21 16:13:04 INFO Blob exists check for insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253: False +2025-02-21 16:13:04 INFO Creating a new folder in the blob storage: +2025-02-21 16:13:07 INFO Latest file number in insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 +2025-02-21 16:13:08 INFO Blob exists check for insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:13:09 INFO New insight created: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:13:24 INFO Query executed successfully. +2025-02-21 16:13:24 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:13:26 WARNING Skipping empty file: %s +2025-02-21 16:13:27 INFO Existing insight found for base code: %s +2025-02-21 16:13:28 INFO Insight updated successfully: %s +2025-02-21 16:13:28 INFO Insight updated successfully. +2025-02-21 16:15:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:15:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:15:04 INFO Query executed successfully. +2025-02-21 16:15:04 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:15:07 WARNING Skipping empty file: %s +2025-02-21 16:15:08 INFO Existing insight found for base code: %s +2025-02-21 16:15:09 INFO Insight updated successfully: %s +2025-02-21 16:15:09 INFO Insight updated successfully. +2025-02-21 16:17:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:18:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:18:00 INFO Query executed successfully. +2025-02-21 16:18:00 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:18:02 WARNING Skipping empty file: %s +2025-02-21 16:18:04 INFO Existing insight found for base code: %s +2025-02-21 16:18:05 INFO Insight updated successfully: %s +2025-02-21 16:18:05 INFO Insight updated successfully. +2025-02-21 16:24:12 INFO Date: 2025-02-21 +======================================== +Time: 16:24:12 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 16:24:16 INFO not logined +2025-02-21 16:24:16 INFO Rendering unauthenticated menu. +2025-02-21 16:24:46 INFO Login button clicked. +2025-02-21 16:24:50 INFO Login successful for user: abhishek +2025-02-21 16:25:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:25:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:25:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:25:52 INFO Query executed successfully. +2025-02-21 16:25:52 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:27:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:27:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:27:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:27:06 INFO Query executed successfully. +2025-02-21 16:27:06 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:27:06 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 16:27:10 INFO Tokens consumed: 939 +2025-02-21 16:27:12 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:27:14 INFO token updated successfully: 2025-02 +2025-02-21 16:27:14 INFO token updated successfully. +2025-02-21 16:27:15 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 7 +2025-02-21 16:27:17 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:27:18 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/8.py +2025-02-21 16:27:18 INFO Code generated and written in generated_method//7.py +2025-02-21 16:27:19 INFO Insight generated and displayed using AG Grid. +2025-02-21 16:28:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:28:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:28:37 INFO Query executed successfully. +2025-02-21 16:28:37 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:28:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:28:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:28:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:28:54 INFO Query executed successfully. +2025-02-21 16:28:54 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:28:55 INFO No existing insight found for base code: %s +2025-02-21 16:28:57 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False +2025-02-21 16:28:57 INFO Creating a new folder in the blob storage: +2025-02-21 16:29:00 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 +2025-02-21 16:29:01 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:29:02 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:29:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:29:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:29:09 INFO Query executed successfully. +2025-02-21 16:29:09 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:29:10 INFO Existing insight found for base code: %s +2025-02-21 16:29:12 INFO Insight updated successfully: %s +2025-02-21 16:29:12 INFO Insight updated successfully. +2025-02-21 16:35:50 INFO Date: 2025-02-21 +======================================== +Time: 16:35:50 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 16:35:55 INFO not logined +2025-02-21 16:35:55 INFO Rendering unauthenticated menu. +2025-02-21 16:36:26 INFO Login button clicked. +2025-02-21 16:36:30 INFO Login successful for user: abhishek +2025-02-21 16:37:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:37:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:37:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:37:57 INFO Query executed successfully. +2025-02-21 16:37:57 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:38:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:38:36 INFO Query executed successfully. +2025-02-21 16:38:36 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:38:36 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 16:38:40 INFO Tokens consumed: 939 +2025-02-21 16:38:42 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:38:44 INFO token updated successfully: 2025-02 +2025-02-21 16:38:44 INFO token updated successfully. +2025-02-21 16:38:46 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 8 +2025-02-21 16:38:48 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:38:49 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/9.py +2025-02-21 16:38:49 INFO Code generated and written in generated_method//8.py +2025-02-21 16:38:49 INFO Insight generated and displayed using AG Grid. +2025-02-21 16:39:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:39:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:39:14 INFO Query executed successfully. +2025-02-21 16:39:14 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:39:16 INFO Existing insight found for base code: %s +2025-02-21 16:39:17 INFO Insight updated successfully: %s +2025-02-21 16:39:17 INFO Insight updated successfully. +2025-02-21 16:40:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:29 INFO Query executed successfully. +2025-02-21 16:40:29 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:40:31 INFO Existing insight found for base code: %s +2025-02-21 16:40:32 INFO Insight updated successfully: %s +2025-02-21 16:40:32 INFO Insight updated successfully. +2025-02-21 16:40:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:44 INFO Insight list generated successfully. +2025-02-21 16:40:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:50 INFO Insight list generated successfully. +2025-02-21 16:40:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:40:51 INFO Query executed successfully. +2025-02-21 16:41:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:21 INFO Query executed successfully. +2025-02-21 16:41:21 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:41:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:41:57 INFO Query executed successfully. +2025-02-21 16:41:57 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:41:57 INFO Generating graph with prompt: + You are an expert in understanding English language instructions to generate a graph based on a given dataframe. + + I am providing you the dataframe structure as a dictionary in double backticks. + Dataframe structure: `` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string`` + + I am also providing you a summary of the dataframe as a dictionary in double backticks. + Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` + + I have provided the dataframe structure and its summary. I can't provide the entire dataframe. + + I am also giving you the intent instruction in triple backticks. + Instruction for generating the graph: ```generate scattered graph patient based on age group``` + + Your task is to write the code that will generate a Plotly chart. + You should be able to derive the chart type from the instruction. + Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. + + You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. + + Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. + Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. + Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` + + If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. + Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` + + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. + Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. + Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( + x='year:O', + y='sum(yield):Q', + column='site:N' + )```` + + A grouped bar chart will be explicitly asked for in the instructions. + + Only produce the Python code. + Do NOT produce any backticks or double quotes or single quotes before or after the code. + Do generate the Plotly import statement as part of the code. + Do NOT justify your code. + Do not generate any narrative or comments in the code. + Do NOT produce any JSON tags. + Do not print or return the chart object at the end. + Do NOT produce any additional text that is not part of the query itself. + Always name the final Plotly chart object as 'chart'. + Go back and check if the generated code can be used in the `st.plotly_chart()` method. + +2025-02-21 16:42:00 INFO Tokens consumed: 1509 +2025-02-21 16:42:03 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:42:04 INFO token updated successfully: 2025-02 +2025-02-21 16:42:04 INFO token updated successfully. +2025-02-21 16:42:05 ERROR Error creating plotly chart object: [Errno 2] No such file or directory: 'data.csv' +2025-02-21 16:42:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:42:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:42:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:42:40 INFO Query executed successfully. +2025-02-21 16:42:40 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:43:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:43:09 INFO Query executed successfully. +2025-02-21 16:43:09 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:43:09 INFO Generating graph with prompt: + You are an expert in understanding English language instructions to generate a graph based on a given dataframe. + + I am providing you the dataframe structure as a dictionary in double backticks. + Dataframe structure: `` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string`` + + I am also providing you a summary of the dataframe as a dictionary in double backticks. + Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` + + I have provided the dataframe structure and its summary. I can't provide the entire dataframe. + + I am also giving you the intent instruction in triple backticks. + Instruction for generating the graph: ```generate scattered graph of patient based on age group``` + + Your task is to write the code that will generate a Plotly chart. + You should be able to derive the chart type from the instruction. + Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. + + You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. + + Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. + Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. + Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` + + If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. + Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` + + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. + Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. + Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( + x='year:O', + y='sum(yield):Q', + column='site:N' + )```` + + A grouped bar chart will be explicitly asked for in the instructions. + + Only produce the Python code. + Do NOT produce any backticks or double quotes or single quotes before or after the code. + Do generate the Plotly import statement as part of the code. + Do NOT justify your code. + Do not generate any narrative or comments in the code. + Do NOT produce any JSON tags. + Do not print or return the chart object at the end. + Do NOT produce any additional text that is not part of the query itself. + Always name the final Plotly chart object as 'chart'. + Go back and check if the generated code can be used in the `st.plotly_chart()` method. + +2025-02-21 16:43:18 INFO Tokens consumed: 2013 +2025-02-21 16:43:21 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:43:22 INFO token updated successfully: 2025-02 +2025-02-21 16:43:22 INFO token updated successfully. +2025-02-21 16:43:25 INFO Plotly chart object created successfully. +2025-02-21 16:44:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:37 INFO Query executed successfully. +2025-02-21 16:44:37 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:44:39 INFO Existing insight found for base code: %s +2025-02-21 16:44:41 INFO Insight updated successfully: %s +2025-02-21 16:44:41 INFO Insight updated successfully. +2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:44:56 INFO Query executed successfully. +2025-02-21 16:44:56 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:44:59 INFO Existing insight found for base code: %s +2025-02-21 16:45:01 INFO Insight updated successfully: %s +2025-02-21 16:45:01 INFO Insight updated successfully. +2025-02-21 16:45:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:45:16 INFO Insight list generated successfully. +2025-02-21 16:45:22 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:45:22 INFO Insight list generated successfully. +2025-02-21 16:45:23 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:45:23 INFO Query executed successfully. +2025-02-21 16:55:32 INFO Date: 2025-02-21 +======================================== +Time: 16:55:32 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 16:55:36 INFO not logined +2025-02-21 16:55:36 INFO Rendering unauthenticated menu. +2025-02-21 16:55:56 INFO Login button clicked. +2025-02-21 16:56:00 INFO Login successful for user: abhishek +2025-02-21 16:56:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:57:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:57:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:57:22 INFO Query executed successfully. +2025-02-21 16:57:22 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:58:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:58:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:58:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:58:36 INFO Query executed successfully. +2025-02-21 16:58:36 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:58:36 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 50`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 16:58:40 INFO Tokens consumed: 939 +2025-02-21 16:58:42 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 16:58:44 INFO token updated successfully: 2025-02 +2025-02-21 16:58:44 INFO token updated successfully. +2025-02-21 16:58:45 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 9 +2025-02-21 16:58:47 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 16:58:48 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/10.py +2025-02-21 16:58:48 INFO Code generated and written in generated_method//9.py +2025-02-21 16:58:48 INFO Insight generated and displayed using AG Grid. +2025-02-21 16:59:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:59:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:59:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:59:44 INFO Query executed successfully. +2025-02-21 16:59:44 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:59:46 INFO Existing insight found for base code: %s +2025-02-21 16:59:46 ERROR 1 +2025-02-21 16:59:47 INFO Insight updated successfully: %s +2025-02-21 16:59:47 INFO Insight updated successfully. +2025-02-21 16:59:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:59:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 16:59:54 INFO Query executed successfully. +2025-02-21 16:59:54 INFO Dataset columns displayed using AG Grid. +2025-02-21 16:59:56 INFO Existing insight found for base code: %s +2025-02-21 16:59:56 ERROR 1 +2025-02-21 16:59:57 INFO Insight updated successfully: %s +2025-02-21 16:59:57 INFO Insight updated successfully. +2025-02-21 17:00:05 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:00:06 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json +2025-02-21 17:00:06 INFO Insight list generated successfully. +2025-02-21 17:00:15 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:00:16 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json +2025-02-21 17:00:16 INFO Insight list generated successfully. +2025-02-21 17:00:17 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:00:17 INFO Query executed successfully. +2025-02-21 17:00:21 ERROR Error generating chart: StreamlitDuplicateElementId() +2025-02-21 17:00:58 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:01:00 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json +2025-02-21 17:01:00 INFO Insight list generated successfully. +2025-02-21 17:01:01 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json +2025-02-21 17:01:01 INFO Query executed successfully. +2025-02-21 17:01:10 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:01:10 INFO Insight list generated successfully. +2025-02-21 17:01:15 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:01:15 INFO Insight list generated successfully. +2025-02-21 17:01:17 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:01:17 INFO Query executed successfully. +2025-02-21 17:08:38 INFO Date: 2025-02-21 +======================================== +Time: 17:08:38 +Logger Data: This is Insight's lab log data. +---------------------------------------- + +2025-02-21 17:08:42 INFO not logined +2025-02-21 17:08:42 INFO Rendering unauthenticated menu. +2025-02-21 17:13:33 INFO Login button clicked. +2025-02-21 17:13:37 INFO Login successful for user: abhishek +2025-02-21 17:14:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:15:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:15:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:15:41 INFO Query executed successfully. +2025-02-21 17:15:41 INFO Dataset columns displayed using AG Grid. +2025-02-21 17:16:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:16:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:16:17 INFO Query executed successfully. +2025-02-21 17:16:17 INFO Dataset columns displayed using AG Grid. +2025-02-21 17:16:17 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 60`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 17:16:22 INFO Tokens consumed: 977 +2025-02-21 17:16:25 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 17:16:26 INFO token updated successfully: 2025-02 +2025-02-21 17:16:26 INFO token updated successfully. +2025-02-21 17:16:28 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 10 +2025-02-21 17:16:29 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 17:16:31 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/11.py +2025-02-21 17:16:31 INFO Code generated and written in generated_method//10.py +2025-02-21 17:16:31 ERROR Error executing generated code 10 for generate an insight of patient whose age is above 60: invalid syntax (, line 1) +2025-02-21 17:17:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:17:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:17:43 INFO Query executed successfully. +2025-02-21 17:17:43 INFO Dataset columns displayed using AG Grid. +2025-02-21 17:17:43 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. + + I am providing the english language task in double backticks + Task: ``generate an insight of patient whose age is above 60`` + + I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe structure is enclosed in triple backticks. + Dataframe Structures: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. + The dataframe is enclosed in triple backticks. + Dataframe: ``` Column Dtype +0 id string +1 identifier_value string +2 identifier_use string +3 identifier_type string +4 identifier_start_date string +5 identifier_assigner string +6 active int64 +7 official_name_family string +8 official_name_given string +9 usual_name_given string +10 gender string +11 birth_date string +12 Age int64 +13 home_address_line string +14 home_address_city string +15 home_address_district string +16 home_address_state string +17 home_address_postalCode int64 +18 home_address_period_start string``` + + You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. + Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. + Do not define an empty output dictionary as it will be already defined outside the generated code. + Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. + Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. + If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. + Put the given task as a comment line in the first line of the code generated. + Do not generate a method, but generate only script. + + Your task is to generate python code that can be executed. + Do NOT produce any backticks before or after. + Do NOT produce any narrative or justification before or after the code + Do NOT produce any additional text that is not part of the python code of the method itself. + You must give a new line character before every actual line of code. + The script you produced must be able to run on a Python runtime. + Go back and check if the generated code can be run within a python runtime. + Go back and check to make sure you have not produced any narrative or justification before or after the code. + Go back and check to make sure you have not enclosed the code in triple backticks. + +2025-02-21 17:17:46 INFO Tokens consumed: 937 +2025-02-21 17:17:48 INFO Existing token_consumed found for month: 2025-02 +2025-02-21 17:17:50 INFO token updated successfully: 2025-02 +2025-02-21 17:17:50 INFO token updated successfully. +2025-02-21 17:17:52 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 11 +2025-02-21 17:17:53 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True +2025-02-21 17:17:55 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/12.py +2025-02-21 17:17:55 INFO Code generated and written in generated_method//11.py +2025-02-21 17:17:55 INFO Insight generated and displayed using AG Grid. +2025-02-21 17:18:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:18:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:19:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:19:00 INFO Query executed successfully. +2025-02-21 17:19:00 INFO Dataset columns displayed using AG Grid. +2025-02-21 17:19:02 INFO Existing insight found for base code: %s +2025-02-21 17:19:04 INFO Insight updated successfully: %s +2025-02-21 17:19:04 INFO Insight updated successfully. +2025-02-21 17:19:24 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:19:24 INFO Insight list generated successfully. +2025-02-21 17:20:02 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:20:02 INFO Insight list generated successfully. +2025-02-21 17:20:03 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json +2025-02-21 17:20:03 INFO Query executed successfully.