Create app.py

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+import difflib
+
+# Assuming you have 'lpi_df' and 'similarity' defined before this point
+
+st.title('Course Recommendation App')
+
+user_input = st.text_input('Enter What You Want to Learn : ')
+
+if user_input:
+    list_of_all_titles = lpi_df['Module'].tolist()
+    find_close_match = difflib.get_close_matches(user_input, list_of_all_titles)
+
+    if find_close_match:
+        close_match = find_close_match[0]
+        index_of_the_course = lpi_df[lpi_df.Module == close_match].index.values[0]
+        similarity_score = list(enumerate(similarity[index_of_the_course]))
+        sorted_similar_course = sorted(similarity_score, key=lambda x: x[1], reverse=True)
+
+        st.write('Courses suggested for you :')
+
+        i = 1
+        for course in sorted_similar_course:
+            index = course[0]
+            title_from_index = lpi_df[lpi_df.index == index]['Module'].values[0]
+            if i < 30:
+                st.write(f"{i}. {title_from_index}")
+                i += 1
+
+        if i == 1:
+            st.write('No close matches found.')
+    else:
+        st.write('No close matches found.')
+