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app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+df = pd.read_csv("movies.csv")
+features = ["keywords", "cast", "genres", "director"]
+
+for feature in features:
+    df[feature] = df[feature].fillna('')
+
+def combined_features(row):
+    return row['keywords']+" "+row['cast']+" "+row['genres']+" "+row['director']
+
+df["combined_features"] = df.apply(combined_features, axis=1)
+
+Tfidf_vect = TfidfVectorizer()
+vector_matrix = Tfidf_vect.fit_transform(df["combined_features"])
+vector_matrix.toarray()
+
+cosine_sim = cosine_similarity(vector_matrix)
+
+def get_index_from_title(title):
+    return df[df.title == title]["index"].values[0]
+
+def get_title_from_index(index):
+    return df[df.index == index]["title"].values[0]
+
+def check_movie(m_name):
+    movie_index = get_index_from_title(m_name)
+    similar_movies= list(enumerate(cosine_sim[movie_index]))
+    sorted_similar_movies = sorted(similar_movies, key=lambda x:x[1], reverse=True)
+    mv = get_suggestions(sorted_similar_movies)
+    return mv
+
+def get_suggestions(sorted_similar_movies):
+    i=0
+    movies = ""
+    for movie in sorted_similar_movies:
+        t = get_title_from_index(movie[0])
+        movies = movies + t +"\n"
+        
+        i=i+1
+        if i>10:
+            print(movies)
+            return movies
+
+def check(enter_movie_name):
+    mvs = check_movie(enter_movie_name)
+    return mvs
+
+
+movie = gr.Interface(fn=check, inputs="text", outputs="text")
+movie.launch(share=True)