Update app.py

app.py

@@ -1,49 +1,66 @@
-import pandas as pd
-import numpy as np
-import joblib
-import streamlit as st
-
-
-cosine_sim=joblib.load('model_movie_recomadation_sigmoid.joblib')
-
-indices_df=pd.read_csv('indices.csv')
-
-
-html_temp = """
-    <div style="background-color:tomato;padding:10px">
-    <h2 style="color:white;text-align:center;">Movie Recomandation APP</h2>
-    </div>
-    """
-st.markdown(html_temp, unsafe_allow_html=True)
-
-
-def get_recommendations(title, cosine_sim=cosine_sim):
-    # Get the index of the movie that matches the title
-    
-    filtered_df = indices_df[indices_df.apply(lambda row: row.astype(str).str.contains(movie, case=False).any(), axis=1)].index
-    
-    sim_scores = list(enumerate(cosine_sim[filtered_df]))
-
-    index, values = sim_scores[0]
-    result = list(zip(range(len(values)), values))
-    # Sort the movies based on the similarity scores
-    result_sorted = sorted(result, key=lambda x: x[1], reverse=True)
-
-    # Get the scores of the 10 most similar movies
-    sim_scores = result_sorted[1:11]
-
-    # Get the movie indices
-    ##global movie_indices
-    movie_indices = [i[0] for i in sim_scores]
-
-    #Return the top 10 most similar movies
-    return movie_indices
-
-
-movie=st.text_input("Enter the Movie Tittle")
-
-if st.button("Search"):
-    
-    val=get_recommendations(movie)
-
+import pandas as pd
+import numpy as np
+import joblib
+import streamlit as st
+
+
+cosine_sim=joblib.load('model_movie_recomadation_sigmoid.joblib')
+
+indices_df=pd.read_csv('indices.csv')
+
+
+html_temp = """
+    <div style="background-color:tomato;padding:10px">
+    <h2 style="color:white;text-align:center;">Movie Recomandation APP</h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+image_url="https://tse1.mm.bing.net/th?id=OIP.T1nYWZh17oT5wuISslNzdwHaEK&pid=Api&P=0&h=180"
+
+st.image(image_url, use_container_width=True)
+st.markdown(f"""
+    <style>
+        /* Set the background image for the entire app */
+        .stApp {{
+            background-color:#6793AC;
+            background-size: 100px;
+            background-repeat:no;
+            background-attachment: auto;
+            background-position:full;
+        }}
+    </style>
+    """, unsafe_allow_html=True)
+
+
+
+
+def get_recommendations(title, cosine_sim=cosine_sim):
+    # Get the index of the movie that matches the title
+    
+    filtered_df = indices_df[indices_df.apply(lambda row: row.astype(str).str.contains(movie, case=False).any(), axis=1)].index
+    
+    sim_scores = list(enumerate(cosine_sim[filtered_df]))
+
+    index, values = sim_scores[0]
+    result = list(zip(range(len(values)), values))
+    # Sort the movies based on the similarity scores
+    result_sorted = sorted(result, key=lambda x: x[1], reverse=True)
+
+    # Get the scores of the 10 most similar movies
+    sim_scores = result_sorted[1:11]
+
+    # Get the movie indices
+    ##global movie_indices
+    movie_indices = [i[0] for i in sim_scores]
+
+    #Return the top 10 most similar movies
+    return movie_indices
+
+
+movie=st.text_input("Enter the Movie Tittle")
+
+if st.button("Search"):
+    
+    val=get_recommendations(movie)
+
     st.dataframe(indices_df.iloc[val])