diff --git a/Web_app/pages/2_Movie_Recommendation.py b/Web_app/pages/2_Movie_Recommendation.py
index 38f52ccd..3aeddb5b 100644
--- a/Web_app/pages/2_Movie_Recommendation.py
+++ b/Web_app/pages/2_Movie_Recommendation.py
@@ -1,29 +1,27 @@
 import streamlit as st
 import pandas as pd
 import pickle
-from streamlit import session_state as session
 import requests
-import numpy as np
+import os
 
 def fetch_link(movie_name):
-    movie_name='-'.join(movie_name.split())
-    movie_link=f"https://www.justwatch.com/in/movie/{movie_name}"
+    movie_name = '-'.join(movie_name.split())
+    movie_link = f"https://www.justwatch.com/in/movie/{movie_name}"
     response = requests.get(movie_link)
     if response.status_code == 200:
         return movie_link
     else:
-        tv_link=f"https://www.justwatch.com/in/tv-show/{movie_name}"
+        tv_link = f"https://www.justwatch.com/in/tv-show/{movie_name}"
         response = requests.get(tv_link)
         if response.status_code == 200:
             return tv_link
     
-    return  f"https://en.wikipedia.org/wiki/{'_'.join(movie_name.split())}"
+    return f"https://en.wikipedia.org/wiki/{'_'.join(movie_name.split())}"
 
 def fetch_trailer(movie_name):
     return f"https://www.youtube.com/results?search_query={movie_name}+trailer"
 
-
-def recommend(movies,input_movie):
+def recommend(movies, input_movie):
     movie_index = movies[movies['title'] == input_movie].index[0]
     distances = similarity[movie_index]
     movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
@@ -36,49 +34,60 @@ def recommend(movies,input_movie):
         recommended_movies.append(movie_title)
         recommended_movies_link.append(fetch_link(movie_title))
         recommended_movies_trailer_link.append(fetch_trailer(movie_title))
-    return recommended_movies, recommended_movies_link,recommended_movies_trailer_link
+    return recommended_movies, recommended_movies_link, recommended_movies_trailer_link
+
+# Define paths to your files
+similarity_path = r'Web_app/pages/similarity.pkl'
+movies_csv_path = r'Web_app/movies.csv'
 
-similarity = pickle.load(open('similarity.pkl', 'rb'))
-movie_data=pd.read_csv("movies.csv") #you can either generate from scraper.py or can copy paste your own movies data
+# Check if the similarity file exists
+if os.path.exists(similarity_path):
+    with open(similarity_path, 'rb') as file:
+        similarity = pickle.load(file)
+else:
+    st.error(f"File not found: {similarity_path}")
+    similarity = None
 
+# Check if the movies CSV file exists
+if os.path.exists(movies_csv_path):
+    movie_data = pd.read_csv(movies_csv_path)
+else:
+    st.error(f"File not found: {movies_csv_path}")
+    movie_data = pd.DataFrame(columns=['title'])  # Empty DataFrame to avoid errors
 
 st.title("What to binge?")
 st.subheader('Movie recommendation :film_projector:', divider='rainbow')
 st.write("")
-recent_movie=st.selectbox("Which movie/tv show you just watched :sunglasses:!!!",movie_data['title']) # input movie name 
-recommend_movies={"Name":[],"Link":[]}
-result = pd.DataFrame.from_dict(recommend_movies)
-    
-if st.button('Recommend'):
-    names, links, trailers = recommend(movie_data,recent_movie)
-    
-    data_df = pd.DataFrame(
-    {
-        "Name": names,
-        "Link": links,
-        "Trailer": trailers
-    }
-    )
-
-    st.data_editor(
-        data_df,
-        column_config={
-            "Name": st.column_config.TextColumn(
-                "Top Recommended Movies",
-                help="The top recommended movies"
-            ),
-            "Link": st.column_config.LinkColumn(
-                "(watch/description) Links",
-                help="Links of recommended movies"
-            ),
-            "Trailer": st.column_config.LinkColumn(
-                "Trailer Links",
-                help="Youtuber trailer links of recommended movies"
-            ),
-        }
-    )
-
-
+recent_movie = st.selectbox("Which movie/tv show you just watched :sunglasses:!!!", movie_data['title'])
 
+if st.button('Recommend'):
+    if similarity is not None and not movie_data.empty:
+        names, links, trailers = recommend(movie_data, recent_movie)
+        
+        data_df = pd.DataFrame(
+            {
+                "Name": names,
+                "Link": links,
+                "Trailer": trailers
+            }
+        )
 
-    
\ No newline at end of file
+        st.data_editor(
+            data_df,
+            column_config={
+                "Name": st.column_config.TextColumn(
+                    "Top Recommended Movies",
+                    help="The top recommended movies"
+                ),
+                "Link": st.column_config.LinkColumn(
+                    "(watch/description) Links",
+                    help="Links of recommended movies"
+                ),
+                "Trailer": st.column_config.LinkColumn(
+                    "Trailer Links",
+                    help="YouTube trailer links of recommended movies"
+                ),
+            }
+        )
+    else:
+        st.error("Recommendation system cannot run because required files are missing.")
diff --git a/Web_app/pages/similarity.pkl b/Web_app/pages/similarity.pkl
new file mode 100644
index 00000000..2f4b4ebb
Binary files /dev/null and b/Web_app/pages/similarity.pkl differ