Demo

src/bdc/steps/google_places.py

@@ -228,7 +228,7 @@ def get_first_place_candidate(self, query, input_type) -> (dict, int):
             return None, 0
 
         if not response["status"] == HTTPStatus.OK.name:
-            log.warning(
+            log.debug(
                 f"Failed to fetch data. Status code: {response['status']}",
             )
             return None, 0

src/bdc/steps/helpers/generate_hash_leads.py

@@ -7,7 +7,6 @@
 
 import pandas as pd
 
-from bdc.steps.step import Step
 from database import get_database
 from logger import get_logger
 
@@ -56,12 +55,12 @@ def hash_check(
 
         if lead_hash in lookup_table:
             # If the hash exists in the lookup table, return the corresponding data
-            log.info(f"Hash {lead_hash} already exists in the lookup table.")
+            log.debug(f"Hash {lead_hash} already exists in the lookup table.")
             try:
                 previous_data = lead_data[fields_tofill]
                 return previous_data
             except KeyError as e:
-                log.info(
+                log.debug(
                     f"Hash is present but data fields {fields_tofill} were not found."
                 )
                 lookup_table[lead_hash] = lookup_table[lead_hash][:-1] + [

src/database/leads/local_repository.py

@@ -24,9 +24,15 @@ class LocalRepository(Repository):
     DF_OUTPUT = os.path.abspath(
         os.path.join(BASE_PATH, "../../data/leads_enriched.csv")
     )
+    DF_HISTORICAL_OUTPUT = os.path.abspath(
+        os.path.join(BASE_PATH, "../../data/100k_historic_enriched.csv")
+    )
     DF_PREPROCESSED_INPUT = os.path.abspath(
         os.path.join(BASE_PATH, "../../data/preprocessed_data_files/")
     )
+    DF_PREDICTION_OUTPUT = os.path.abspath(
+        os.path.join(BASE_PATH, "../../data/leads_predicted_size.csv")
+    )
     REVIEWS = os.path.abspath(os.path.join(BASE_PATH, "../../data/reviews/"))
     SNAPSHOTS = os.path.abspath(os.path.join(BASE_PATH, "../../data/snapshots/"))
     GPT_RESULTS = os.path.abspath(os.path.join(BASE_PATH, "../../data/gpt-results/"))
@@ -51,6 +57,13 @@ def save_dataframe(self):
         self.df.to_csv(self.DF_OUTPUT, index=False)
         log.info(f"Saved enriched data locally to {self.DF_OUTPUT}")
 
+    def save_prediction(self, df):
+        """
+        Save dataframe in df parameter in chosen output location
+        """
+        df.to_csv(self.DF_PREDICTION_OUTPUT, index=False)
+        log.info(f"Saved prediction result locally to {self.DF_PREDICTION_OUTPUT}")
+
     def insert_data(self, data):
         """
         TODO: Insert new data into specified dataframe
@@ -68,7 +81,7 @@ def save_review(self, review, place_id, force_refresh=False):
         json_file_path = os.path.join(self.REVIEWS, file_name)
 
         if os.path.exists(json_file_path):
-            log.info(f"Reviews for {place_id} already exist")
+            log.debug(f"Reviews for {place_id} already exist")
             return
 
         with open(json_file_path, "w", encoding="utf-8") as json_file:
@@ -253,10 +266,17 @@ def save_classification_report(self, report, model_name: str):
         except Exception as e:
             log.error(f"Could not save report at {report_file_path}! Error: {str(e)}")
 
-    def load_preprocessed_data(
-        self, file_name: str = "historical_preprocessed_data.csv"
-    ):
+    def get_preprocessed_data_path(self, historical: bool = True):
+        file_name = (
+            "historical_preprocessed_data.csv"
+            if historical
+            else "preprocessed_data.csv"
+        )
+        file_path = os.path.join(self.DF_PREPROCESSED_INPUT, file_name)
+        return file_path
+
+    def load_preprocessed_data(self, historical: bool = True):
         try:
-            return pd.read_csv(os.path.join(self.DF_PREPROCESSED_INPUT, file_name))
+            return pd.read_csv(self.get_preprocessed_data_path(historical))
         except FileNotFoundError:
             log.error("Error: Could not find input file for preprocessed data.")

src/database/leads/repository.py

@@ -18,12 +18,21 @@ def DF_INPUT(self):
         pass
 
     @property
+    @abstractmethod
     def DF_OUTPUT(self):
         """
         Define database path to store dataframe
         """
         pass
 
+    @property
+    @abstractmethod
+    def DF_HISTORICAL_OUTPUT(self):
+        """
+        Define database path to store historical enriched dataframe (used for preprocessing input)
+        """
+        pass
+
     @property
     @abstractmethod
     def REVIEWS(self):
@@ -65,7 +74,9 @@ def set_dataframe(self, df):
     def get_input_path(self):
         return self.DF_INPUT
 
-    def get_output_path(self):
+    def get_enriched_data_path(self, historical=False):
+        if historical:
+            return self.DF_HISTORICAL_OUTPUT
         return self.DF_OUTPUT
 
     @abstractmethod
@@ -82,6 +93,13 @@ def save_dataframe(self):
         """
         pass
 
+    @abstractmethod
+    def save_prediction(self, df):
+        """
+        Save dataframe in df parameter in chosen output location
+        """
+        pass
+
     @abstractmethod
     def insert_data(self, data):
         """
@@ -221,7 +239,14 @@ def save_classification_report(self, report, model_name: str):
         pass
 
     @abstractmethod
-    def load_preprocessed_data(self, file_name: str):
+    def get_preprocessed_data_path(self, historical: bool = True):
+        """
+        Returns the path for a preprocessed data file (either historical or current)
+        """
+        pass
+
+    @abstractmethod
+    def load_preprocessed_data(self, historical: bool = True):
         """
         Load the preprocessed data from the given file
         """

src/database/leads/s3_repository.py

@@ -43,6 +43,10 @@ class S3Repository(Repository):
     MODELS_BUCKET = "amos--models"
     DF_INPUT = f"s3://{EVENTS_BUCKET}/leads/enriched.csv"
     DF_OUTPUT = f"s3://{EVENTS_BUCKET}/leads/enriched.csv"
+    DF_HISTORICAL_OUTPUT = (
+        f"s3://{EVENTS_BUCKET}/historical_data/100k_historic_enriched.csv"
+    )
+    DF_PREDICTION_OUTPUT = f"s3://{EVENTS_BUCKET}/leads/leads_predicted_size.csv"
     DF_PREPROCESSED_INPUT = f"s3://{FEATURES_BUCKET}/preprocessed_data_files/"
     REVIEWS = f"s3://{EVENTS_BUCKET}/reviews/"
     SNAPSHOTS = f"s3://{EVENTS_BUCKET}/snapshots/"
@@ -131,6 +135,16 @@ def save_dataframe(self):
         self._save_to_s3(csv_buffer.getvalue(), bucket, obj_key)
         log.info(f"Successfully saved enriched leads to s3://{bucket}/{obj_key}")
 
+    def save_prediction(self, df):
+        """
+        Save dataframe in df parameter in chosen output location
+        """
+        bucket, obj_key = decode_s3_url(self.DF_PREDICTION_OUTPUT)
+        csv_buffer = StringIO()
+        df.to_csv(csv_buffer, index=False)
+        self._save_to_s3(csv_buffer.getvalue(), bucket, obj_key)
+        log.info(f"Successfully saved prediction result to s3://{bucket}/{obj_key}")
+
     def _save_to_s3(self, data, bucket, key):
         s3.put_object(
             Bucket=bucket,
@@ -374,15 +388,17 @@ def save_classification_report(self, report, model_name: str):
         except Exception as e:
             log.error(f"Could not save report for '{model_name}' to S3: {str(e)}")
 
-    def load_preprocessed_data(
-        self, file_name: str = "historical_preprocessed_data.csv"
-    ):
+    def get_preprocessed_data_path(self, historical: bool = True):
+        file_name = (
+            "historical_preprocessed_data.csv"
+            if historical
+            else "preprocessed_data.csv"
+        )
         file_path = self.DF_PREPROCESSED_INPUT + file_name
-        if not file_path.startswith("s3://"):
-            log.error(
-                "S3 location has to be defined like this: s3://<BUCKET>/<OBJECT_KEY>"
-            )
-            return
+        return file_path
+
+    def load_preprocessed_data(self, historical: bool = True):
+        file_path = self.get_preprocessed_data_path(historical)
 
         source = None
         remote_dataset = None