added database and script

database.py

@@ -0,0 +1,69 @@
+import requests
+import pandas as pd
+import json
+
+# Load CSV
+try:
+    df = pd.read_csv('/Users/moizpro/Desktop/MoviesRecommender/MovieRecommender/imdb_movie_codes.csv')
+    print("df loaded..")
+except FileNotFoundError:
+    print("Error: CSV file not found at path")
+    exit()
+
+# Define column names for the DataFrame
+column_names = [
+    "id", "Title", "Year", "Rated", "Runtime", "Genre1", "Genre2", "Genre3",
+    "Director", "Writer", "Plot", "Awards", "IMDb", "Rotten Tomatoes",
+    "Metascore", "IMDb_votes", "Type", "BoxOffice"
+]
+database = pd.DataFrame(columns=column_names)
+
+# Define function to fetch movie details and save in DataFrame
+def get_movie(movie_id):
+    api_key = "f2443b04"
+    url = f"http://www.omdbapi.com/?apikey={api_key}&i={movie_id}&plot=full"
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            # Check for valid response
+            if data.get("Response") == "True":
+                # Extract genres
+                genres = (data.get("Genre") or "").split(", ")
+                genre1, genre2, genre3 = genres[:3] if len(genres) >= 3 else genres + [None] * (3 - len(genres)
+)
+                # Append the movie data to the DataFrame
+                database.loc[len(database)] = [
+                    movie_id,
+                    data.get("Title"),
+                    data.get("Year"),
+                    data.get("Rated"),
+                    data.get("Runtime"),
+                    genre1, genre2, genre3,
+                    data.get("Director"),
+                    data.get("Writer"),
+                    data.get("Plot"),
+                    data.get("Awards"),
+                    data.get("imdbRating"),
+                    data.get("Ratings")[1]["Value"] if len(data.get("Ratings", [])) > 1 else None,
+                    data.get("Metascore"),
+                    data.get("imdbVotes"),
+                    data.get("Type"),
+                    data.get("BoxOffice"),
+                ]
+            else:
+                print(f"Error in API response for movie_id {movie_id}: {data.get('Error')}")
+        else:
+            print(f"HTTP error {response.status_code} for movie_id {movie_id}")
+    except Exception as e:
+        print(f"Exception occurred for movie_id {movie_id}: {e}")
+
+# Loop through movie IDs in the CSV and fetch details
+for i in range(len(df)):
+    movie_id = df.iloc[i, 0]
+    get_movie(movie_id)
+
+# Save the DataFrame to a CSV file
+output_file = '/Users/moizpro/Desktop/MoviesRecommender/MovieRecommender'
+database.to_csv(output_file, index=False)
+print(f"Movie database saved to {output_file}")