new endpoints

main.py

@@ -20,7 +20,7 @@ from engine import FS
 
 app = FastAPI()
 data = pd.DataFrame()
-
+mlb = MultiLabelBinarizer()
 
 def inference(first: pandas.core.series.Series, second_id: str, df=None):
     if df is not None:
@@ -59,14 +59,41 @@ def process_dataframe(df, production):
 @app.on_event('startup')
 async def startup_event():
     global data
+    global mlb
     data = pd.read_csv('processed_data.csv', index_col='id', converters={'genres': pd.eval})
     all_genres = data.genres.explode().unique()
-    mlb = MultiLabelBinarizer()
     mlb.fit([all_genres])
     data['genres'] = data['genres'].apply(lambda x: mlb.transform([x])[0])
     data['emotions'] = data[['Happy', 'Angry', 'Surprise', 'Sad', 'Fear']].values.tolist()
 
 
+@app.get('/find/{title}')
+def titles(title: str):
+    response = {}
+    for index, row in data.iterrows():
+        if title.lower() in row['title'].lower():
+            response[index] = {'title': row['title'], 'year': row['release_year']}
+    return response
+
+
+@app.get('/details/{production_id}')
+def details(production_id: str):
+    try:
+        production = data.loc[production_id]
+    except:
+        return {'error': f'{production_id} is not a valid id'}
+    genres = production['genres']
+    genres = mlb.inverse_transform(genres.reshape(1, -1))[0]
+    return {
+        'title': production['title'],
+        'type': production['type'],
+        'description': production['description'],
+        'year': int(production['release_year']),
+        'runtime': int(production['runtime']),
+        'genres': genres,
+    }
+
+
 @app.get('/score/{first_id}/{second_id}')
 def rec_score(first_id: str, second_id: str):
     try: