From b34f303b023688a77a3df4ce725546829e400a62 Mon Sep 17 00:00:00 2001
From: Krystian Wasilewski <krywas@st.amu.edu.pl>
Date: Fri, 27 Jan 2023 18:43:12 +0100
Subject: [PATCH] api adjusted for changing params

---
 main.py | 53 ++++++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 46 insertions(+), 7 deletions(-)

diff --git a/main.py b/main.py
index 2a6a07e..14c8044 100644
--- a/main.py
+++ b/main.py
@@ -22,13 +22,25 @@ app = FastAPI()
 data = pd.DataFrame()
 mlb = MultiLabelBinarizer()
 
-def inference(first: pandas.core.series.Series, second_id: str, df=None):
+
+def inference(first: pandas.core.series.Series,
+              second_id: str,
+              release_year_param='similar',
+              runtime_param='similar',
+              seasons_param='similar',
+              genres_param='same',
+              emotions_param='same',
+              df=None):
     if df is not None:
         second = df.loc[second_id]
     else:
         second = data.loc[second_id]
 
-    FS = fuzzy_system(release_year_param='similar', runtime_param='similar', seasons_param='similar', genres_param='same', emotions_param='same')
+    FS = fuzzy_system(release_year_param=release_year_param,
+                      runtime_param=runtime_param,
+                      seasons_param=seasons_param,
+                      genres_param=genres_param,
+                      emotions_param=emotions_param)
 
     year_diff = int(first['release_year'] - second['release_year'])
     FS.set_variable('RELEASE_YEAR', year_diff)
@@ -51,10 +63,24 @@ def inference(first: pandas.core.series.Series, second_id: str, df=None):
     return second_id, FS.inference(['RECOMMENDATION'])['RECOMMENDATION']
 
 
-def process_dataframe(df, production):
+def process_dataframe(df,
+                      production,
+                      release_year_param,
+                      runtime_param,
+                      seasons_param,
+                      genres_param,
+                      emotions_param
+                      ):
     scores = []
     for index, row in df.iterrows():
-        scores.append(inference(production, str(index), df))
+        scores.append(inference(production,
+                                str(index),
+                                release_year_param,
+                                runtime_param,
+                                seasons_param,
+                                genres_param,
+                                emotions_param,
+                                df))
     return scores
 
 
@@ -111,7 +137,13 @@ def rec_score(first_id: str, second_id: str):
 
 
 @app.get('/recs/{production_id}')
-async def recs(production_id: str, count: int | None = 5):
+async def recs(production_id: str,
+               release_year_param: str | None = 'similar',
+               runtime_param: str | None = 'similar',
+               seasons_param: str | None = 'similar',
+               genres_param: str | None = 'same',
+               emotions_param: str | None = 'same',
+               count: int | None = 5):
     try:
         first = data.loc[production_id]
     except KeyError:
@@ -122,14 +154,21 @@ async def recs(production_id: str, count: int | None = 5):
     cpus = multiprocessing.cpu_count()
     df_list = np.array_split(data, cpus)
     pool = Pool(cpus)
-    results = [pool.apply_async(process_dataframe, [df, first]) for df in df_list]
+    results = [pool.apply_async(process_dataframe,
+                                [df,
+                                 first,
+                                 release_year_param,
+                                 runtime_param,
+                                 seasons_param,
+                                 genres_param,
+                                 emotions_param]) for df in df_list]
 
     for r in results:
         r.wait()
     for r in results:
         scores += r.get()
     print(f'time elapsed = {time.time() - time_start}')
-    scores = [idx[0] for idx in sorted(scores, key=lambda x: x[1], reverse=True)[:count+1]]
+    scores = [idx[0] for idx in sorted(scores, key=lambda x: x[1], reverse=True)[:count + 1]]
     scores.remove(production_id)
     return {
         'id': scores