Upload 5 files

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from programmeRecommendation import recommendProgrammes
+import uvicorn
+
+app = FastAPI()
+origins = ["*"]
+
+#handle CORS issue
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]  
+)
+
+@app.post("/cmsai/recommendmovies")
+def recommendMovies(scheduleDate : str, genre: list[str], referenceProgrammeTitle : str): 
+    moviesInRecommendationList = recommendProgrammes(scheduleDate, genre, referenceProgrammeTitle)
+    return moviesInRecommendationList    
+
+if __name__ == '__main__':
+    recommendMovies('2024-12-01', ["Drama","Fantasy"], 'Halloween')
+    #uvicorn.run(app= app)

movielist_images.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:089bcdb5b5601e51a8826d04d1b96e9d8c17bbf8f9aa7c61ed8d5a13d4185ec6
+size 2285443

programmeRecommendation.py

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+import pandas as pd
+import pickle 
+from pathlib import Path
+import joblib
+import os
+import requests
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+os.environ['CALENDARIFIC_API_KEY'] = os.getenv("CALENDARIFIC_API_KEY")
+
+currentPath = Path(__file__).resolve().parent
+modelfilePath = currentPath / "similarity.pkl"
+moviesfilePath = currentPath / "movielist_images.pkl"
+
+with open(modelfilePath, 'rb') as file:
+    model = pickle.load(file)
+
+with open(moviesfilePath,'rb') as file:
+    movieslist = joblib.load(file)
+
+def recommendMoviesByTitle(programmeTitle):
+  recommended_movies = []
+  if programmeTitle in movieslist['programmetitle'].values:
+    movie_index = movieslist[movieslist['programmetitle'] == programmeTitle].index[0]
+    sim_score = model[movie_index]
+    movie_list = sorted(list(enumerate(sim_score)), reverse = True, key = lambda x: x[1])[1:11]
+    for i in movie_list:      
+      recommended_movies.append({'programmetitle':movieslist.iloc[i[0]].programmetitle, 'imageurl': movieslist.iloc[i[0]].imagepath})
+  else:
+    return("No movies to recommend by keyword...")
+  return recommended_movies
+
+COUNTRY_CODE = "US"
+def get_holidaysByScheduleDate(scheduleDate):    
+    key = os.environ["CALENDARIFIC_API_KEY"]   
+    current_date = pd.to_datetime(scheduleDate,errors="coerce")  
+    year = current_date.year
+    month = current_date.month
+    holidaylist = []
+
+    endpoint_url = f"https://calendarific.com/api/v2/holidays?api_key={key}&country={COUNTRY_CODE}&year={year}&month={month}"
+    response = requests.get(endpoint_url)
+    if response.status_code == 200:
+        holidays = response.json()['response']['holidays']
+        holidaylist = list(map(lambda x: x['description'],holidays))
+        
+        return list(set(holidaylist))
+    else:        
+        return []
+
+def recommendMoviesByKeywordsAndGenres(keywordToSearchBy, genres, referenceProgrammeTitle):
+    recommended_movies = []
+    filtered_movies = []
+    # Convert the list of genres to lowercase for consistent comparison
+    genres = [genre.lower().strip() for genre in genres]
+
+    if keywordToSearchBy:
+    # Step 1: Filter the DataFrame to find movies that match the given keyword
+        filtered_movies = movieslist[movieslist['Name'].str.contains(keywordToSearchBy, case=False)]
+
+    if  len(filtered_movies) == 0:
+        return recommendMoviesByTitle(referenceProgrammeTitle)        
+
+    # Step 2: Further filter the movies based on the list of genres
+    filtered_movies = filtered_movies[filtered_movies['genredescription'].apply(
+            lambda genre_list: all(genre.lower() in [g.lower().strip() for g in genre_list.split(',')] for genre in genres))]
+
+    if filtered_movies.empty:
+        return("No movies found with the given keyword and genres.")
+
+    movies = filtered_movies.sample(len(filtered_movies))
+    
+    recommended_movies.append({'programmetitle':movies.iloc[0]['programmetitle'],'imageurl': movies.iloc[0]['imagepath']})
+
+    movie_index = movies.index[0]    
+    sim_score = model[movie_index]
+
+    movie_list = sorted(list(enumerate(sim_score)), reverse=True, key=lambda x: x[1])[1:10]
+
+    for i in movie_list:
+        recommended_movies.append({'programmetitle':movieslist.iloc[i[0]].programmetitle,'imageurl': movieslist.iloc[i[0]].imagepath}) 
+    return recommended_movies
+
+
+def recommendProgrammes(scheduleDate : str, genres: list, referenceProgrammeTitle : str):
+    holidaylist = get_holidaysByScheduleDate(scheduleDate)
+    eventName = ""
+    if any("Christmas" in sentence for sentence in holidaylist):
+       eventName = "Christmas"
+    elif any("New year" in sentence for sentence in holidaylist):
+       eventName = "New year"
+
+    return recommendMoviesByKeywordsAndGenres(eventName,genres=genres, referenceProgrammeTitle = referenceProgrammeTitle)
+       
+
+

requirements.txt

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+fastapi
+uvicorn
+pandas 
+joblib
+python-dotenv
+requests

similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e79d90530995aca5f5770d12487c64dd33d4857930eb35d9864c99935a4a2d04
+size 595953451