Create utils.py

utils.py

@@ -0,0 +1,32 @@
+import json
+from transformers import pipeline
+
+# Load local course data
+def load_courses():
+    with open("data/courses.json") as f:
+        return json.load(f)
+
+# Use LLM to recommend best course
+def recommend_course(user_input, courses):
+    relevant = [c for c in courses if user_input.lower() in c["category"].lower() or user_input.lower() in c["title"].lower()]
+    
+    if not relevant:
+        return "No relevant courses found."
+
+    sorted_courses = sorted(relevant, key=lambda x: x["rating"], reverse=True)
+
+    # Optional: Use LLM to summarize reviews
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+    summaries = []
+    for course in sorted_courses[:3]:
+        review_text = " ".join(course["reviews"])
+        summary = summarizer(review_text, max_length=60, min_length=10, do_sample=False)[0]["summary_text"]
+        summaries.append((course, summary))
+
+    result = ""
+    for course, summary in summaries:
+        result += f"**{course['title']}** ({course['platform']})\n"
+        result += f"⭐ Rating: {course['rating']}\n"
+        result += f"📝 Summary of reviews: {summary}\n\n"
+    
+    return result