changed zip to csv and converted recommended_movies into array of objects

app.py

@@ -5,8 +5,8 @@ import ast
 
 
 from datasets import load_dataset, concatenate_datasets
-movies = load_dataset("jyshbgde/cinescopeDataset", data_files = "tmdb_5000_movies.zip")
-credits = load_dataset("jyshbgde/cinescopeDataset", data_files = "tmdb_5000_credits.zip")
+movies = load_dataset("jyshbgde/cinescopeDataset", data_files = "tmdb_5000_movies.csv")
+credits = load_dataset("jyshbgde/cinescopeDataset", data_files = "tmdb_5000_credits.csv")
 
 movies = movies['train']
 credits = credits['train']
@@ -94,7 +94,9 @@ similarity = cosine_similarity(vectors)
 def recommend(movie):
   movie = movie.lower()
   if movie not in new_movies_df['lower_title'].values:
-    return [["Check movie name again"]]
+    return [{
+        "error": "movie not found"
+    }]
   else:
     movie_index = new_movies_df[new_movies_df['lower_title'] == movie].index[0]
     distances = similarity[movie_index]
@@ -102,9 +104,18 @@ def recommend(movie):
     
     recommended_movies = []
     for i in movies_list:
+
+        obj = {
+            "_id":str(movies.iloc[i[0]]["movie_id"]),
+            "title":movies.iloc[i[0]]["title"],
+            "overview":movies.iloc[i[0]]["overview"],
+            "genres": movies.iloc[i[0]]["genres"],
+            "casts":movies.iloc[i[0]]["cast"],
+            "crew": movies.iloc[i[0]]["crew"],
+        }
+        recommended_movies.append(obj)
         
-        
-        recommended_movies.append([str(movies.iloc[i[0]]["movie_id"]) ,movies.iloc[i[0]]["title"], movies.iloc[i[0]]["overview"], movies.iloc[i[0]]["genres"], movies.iloc[i[0]]["cast"], movies.iloc[i[0]]["crew"] ])
+       
         
     return recommended_movies