Create app.py

app.py

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+import os
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer, util
+import streamlit as st
+from preprocess import preprocess
+
+# File paths
+csv_file_path = 'courses_with_embeddings.csv'
+embeddings_file_path = 'course_embeddings.npy'
+
+# Function to check if data files exist and contain data
+def check_files_exist():
+    if os.path.exists(csv_file_path) and os.path.exists(embeddings_file_path):
+        # Check if CSV and Numpy files have data
+        if os.path.getsize(csv_file_path) > 0 and os.path.getsize(embeddings_file_path) > 0:
+            return True
+    return False
+
+# Run preprocess if files don't exist or are empty
+if not check_files_exist():
+    preprocess()
+
+# Load data and embeddings
+df = pd.read_csv(csv_file_path)
+embeddings = np.load(embeddings_file_path)
+
+# Load the pre-trained model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Search function
+def search_courses(query, top_k=5):
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(query_embedding, embeddings)[0]
+    top_results = similarities.topk(k=top_k)
+    results = [df.iloc[idx.item()] for idx in top_results.indices]
+    return results
+
+def main():
+    # Streamlit interface
+    st.title("Smart Course Search")
+    st.markdown(
+        """
+        ### Find the Most Relevant Free Courses on Analytics Vidhya
+        Welcome to **Smart Course Search**! Simply type in your area of interest, and we'll show you the best courses available.
+        """
+    )
+
+    # User input for the search query
+    query = st.text_input("Enter your search query:")
+
+    if query:
+        st.markdown(f"### Showing results for: *'{query}'*")
+        results = search_courses(query)
+        for result in results:
+            st.markdown("---")
+            st.markdown(f"## {result['title']}")
+            st.markdown(f"**Description:**")
+            st.markdown(result['description'])
+            
+            # Course Curriculum Section
+            if 'Course curriculum' in result:
+                st.markdown("### Course Curriculum:")
+                st.markdown(result['Course curriculum'])
+            
+            # About the Instructor Section
+            if 'About the Instructor' in result:
+                st.markdown("### About the Instructor:")
+                st.write(result['About the Instructor'])
+
+            # Adding a button to enroll or learn more about the course
+            if 'url' in result:
+                st.markdown("**[Learn More and Enroll Here](%s)**" % result['url'])
+
+if __name__ == "__main__":
+    main()