Update courses.py

courses.py

@@ -1,4 +1,3 @@
-import gradio as gr
 from sentence_transformers import SentenceTransformer
 import numpy as np
 
@@ -21,15 +20,21 @@ courses = [
 ]
 
 course_texts = [course["title"] + " " + course["description"] + " " + course["curriculum"] for course in courses]
-model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
+# Load the model with a fallback option
+try:
+    model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+except:
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Encode the course texts
 course_embeddings = model.encode(course_texts)
 
 def search_courses(query):
     query_embedding = model.encode([query])[0]
     similarities = np.dot(course_embeddings, query_embedding)
     sorted_course_indices = np.argsort(similarities)[::-1]
-    
+
     results = []
     for idx in sorted_course_indices[:3]:  # Top 3 results
         results.append({
@@ -37,18 +42,6 @@ def search_courses(query):
             "description": courses[idx]["description"],
             "curriculum": courses[idx]["curriculum"]
         })
-    
-    return results
-
-def gradio_search(query):
-    results = search_courses(query)
-    output = ""
-    for result in results:
-        output += f"**Title**: {result['title']}\n"
-        output += f"**Description**: {result['description']}\n"
-        output += f"**Curriculum**: {result['curriculum']}\n\n"
-    return output
-interface = gr.Interface(fn=gradio_search, inputs="text", outputs="markdown",   title="Smart Course Search", description="Find the most relevant free courses from Analytics Vidhya.")
 
+    return results
 
-interface.launch()