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Smart_Search_LLM

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metechmohit commited on Oct 8, 2024

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06fbf75

1 Parent(s): cd89909

Embeddings & VDB

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Files changed (3) hide show

app.py +92 -70
requirements.txt +3 -3
scraped.csv +68 -0

app.py CHANGED Viewed

@@ -1,30 +1,33 @@
-import re
-import requests
-from bs4 import BeautifulSoup
 import pandas as pd
 import streamlit as st
-from groq import Groq
 import os
-from dotenv import load_dotenv
-# Step 1: Scrape the free courses from Analytics Vidhya
-url = "https://courses.analyticsvidhya.com/pages/all-free-courses"
-response = requests.get(url)
-soup = BeautifulSoup(response.content, 'html.parser')
-courses = []
-# Extracting course title, image, and course link
-for course_card in soup.find_all('header', class_='course-card__img-container'):
-    img_tag = course_card.find('img', class_='course-card__img')
-    if img_tag:
-        title = img_tag.get('alt')
-        image_url = img_tag.get('src')
-        link_tag = course_card.find_previous('a')
-        if link_tag:
             course_link = link_tag.get('href')
             if not course_link.startswith('http'):
                 course_link = 'https://courses.analyticsvidhya.com' + course_link
@@ -33,79 +36,98 @@ for course_card in soup.find_all('header', class_='course-card__img-container'):
                 'image_url': image_url,
                 'course_link': course_link
             })
-# Step 2: Create DataFrame
-df = pd.DataFrame(courses)
-load_dotenv()
-client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 def search_courses(query):
     try:
-        # Prepare the prompt for Groq
-        prompt = f"""Given the following query: "{query}"
-        Please analyze the query and rank the following courses based on their relevance to the query.
-        Prioritize courses from Analytics Vidhya. Provide a relevance score from 0 to 1 for each course.
-        Only return courses with a relevance score of 0.5 or higher.
-        Return the results in the following format:
-        Title: [Course Title]
-        Relevance: [Score]
-        Courses:
-        {df['title'].to_string(index=False)}
-        """
-        # Get response from Groq
-        response = client.chat.completions.create(
-            model="llama-3.2-1b-preview",
-            messages=[
-                {"role": "system", "content": "You are an AI assistant specialized in course recommendations."},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0.2,
-            max_tokens=1000
-        )
-        # Parse Groq's response
         results = []
-        matches = re.findall(r'\*\*(.+?)\*\*\s*\(Relevance Score: (0\.\d+)\)', response.choices[0].message.content)
-        for title, score in matches:
-            title = title.strip()
-            score = float(score)
-            if score >= 0.5:
-                matching_courses = df[df['title'].str.contains(title[:30], case=False, na=False)]
-                if not matching_courses.empty:
-                    course = matching_courses.iloc[0]
-                    results.append({
-                        'title': course['title'],  # Use the full title from the database
-                        'image_url': course['image_url'],
-                        'course_link': course['course_link'],
-                        'score': score
-                    })
-        return sorted(results, key=lambda x: x['score'], reverse=True)[:10]  # Return top 10 results
     except Exception as e:
         st.error(f"An error occurred in search_courses: {str(e)}")
         return []
 def display_search_results(result_list):
     if result_list:
         for item in result_list:
             course_title = item['title']
             course_image = item['image_url']
             course_link = item['course_link']
-            relevance_score = round(item['score'] * 100, 2)
             st.image(course_image, use_column_width=True)
             st.write(f"### {course_title}")
-            st.write(f"Relevance: {relevance_score}%")
-            st.markdown(f"[View Course]({course_link})", unsafe_allow_html=True)
     else:
         st.write("No results found. Please try a different query.")
-#Streamlit UI
 st.title("Analytics Vidhya Free Courses🔍")
 st.image("original.png")
 st.markdown("#### 🔍🌐 Get the most appropriate course as per your learning requirement.")

 import pandas as pd
+import requests
 import streamlit as st
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
 import os
+import requests
+from bs4 import BeautifulSoup
+# Function to scrape courses from a single page using BeautifulSoup
+def scrape_courses_from_page(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+    courses = []
+    # Extract course title, image, and course link
+    course_cards = soup.find_all('header', class_='course-card__img-container')
+    for course_card in course_cards:
+        img_tag = course_card.find('img', class_='course-card__img')
+        if img_tag:
+            title = img_tag.get('alt')
+            image_url = img_tag.get('src')
+            # Find the course link using the previous 'a' tag
+            link_tag = course_card.find_previous('a')
             course_link = link_tag.get('href')
             if not course_link.startswith('http'):
                 course_link = 'https://courses.analyticsvidhya.com' + course_link
                 'image_url': image_url,
                 'course_link': course_link
             })
+    return courses
+# Function to scrape across multiple pages using BeautifulSoup
+def scrape_courses_from_all_pages(base_url, total_pages):
+    all_courses = []
+    for page_num in range(1, total_pages + 1):
+        url = f"{base_url}?page={page_num}"
+        courses_on_page = scrape_courses_from_page(url)
+        all_courses.extend(courses_on_page)
+    return pd.DataFrame(all_courses)
+# Define base URL and total pages
+base_url = "https://courses.analyticsvidhya.com/collections/courses"
+total_pages = 8  # Assuming there are 8 pages of courses
+# Check if the CSV file already exists
+if not os.path.exists("scraped.csv"):
+    courses_df = scrape_courses_from_all_pages(base_url, total_pages)
+    courses_df.to_csv("scraped.csv", index=False)
+else:
+    pass
+# Load Scraped courses data
+df = pd.read_csv("scraped.csv")
+# Load the Hugging Face embedding model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+# Generate embeddings for course titles
+course_embeddings = model.encode(df['title'].tolist())
+# Create FAISS index for similarity search
+dimension = course_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)  # Using L2 distance
+index.add(np.array(course_embeddings))
+# Function to search courses using embeddings
 def search_courses(query):
     try:
+        # Generate the embedding for the query
+        query_embedding = model.encode([query])
+        # Search for the closest courses using FAISS
+        top_k = 6  # Retrieve the top 6 closest matches
+        distances, indices = index.search(np.array(query_embedding), top_k)
+        # Collect the results based on the indices returned by FAISS
         results = []
+        for idx, distance in zip(indices[0], distances[0]):
+            course = df.iloc[idx]
+            results.append({
+                'title': course['title'],
+                'image_url': course['image_url'],
+                'course_link': course['course_link'],
+                'score': 1 - distance  # Convert distance to similarity score
+            })
+        return sorted(results, key=lambda x: x['score'], reverse=True)
     except Exception as e:
         st.error(f"An error occurred in search_courses: {str(e)}")
         return []
+# Function to display search results in Streamlit
 def display_search_results(result_list):
     if result_list:
         for item in result_list:
             course_title = item['title']
             course_image = item['image_url']
             course_link = item['course_link']
             st.image(course_image, use_column_width=True)
             st.write(f"### {course_title}")
+            button_html = f"""
+            <a href="{course_link}" target="_blank">
+                <button style="background-color:#4CAF50; border:none; color:white; padding:10px 20px; text-align:center; text-decoration:none; display:inline-block; font-size:16px; margin:4px 2px; cursor:pointer; border-radius:5px;">
+                    View Course
+                </button>
+            </a>
+            """
+            st.markdown(button_html, unsafe_allow_html=True)
     else:
         st.write("No results found. Please try a different query.")
+# Streamlit UI
 st.title("Analytics Vidhya Free Courses🔍")
 st.image("original.png")
 st.markdown("#### 🔍🌐 Get the most appropriate course as per your learning requirement.")

requirements.txt CHANGED Viewed

@@ -2,6 +2,6 @@ streamlit==1.39.0
 requests==2.32.3
 pandas==2.2.3
 beautifulsoup4==4.12.3
-groq==0.11.0
-python-dotenv
-#gradio==4.44.1

 requests==2.32.3
 pandas==2.2.3
 beautifulsoup4==4.12.3
+sentence-transformers==3.1.1
+faiss-cpu==1.9.0
+numpy==1.26.4

scraped.csv ADDED Viewed

	@@ -0,0 +1,68 @@

+title,image_url,course_link
+Agentic AI Pioneer Program - Launching Soon!,https://import.cdn.thinkific.com/118220/oJYYBFgQWamsbuYK9dTN_card%20hero%20image%20(1).jpg,https://courses.analyticsvidhya.com/courses/agentic-ai-pioneer-program-lauching_soon
+Creating Problem-Solving Agents using GenAI for Action Composition,https://import.cdn.thinkific.com/118220/ih6JsBMgSCWbl9ofgpYg_Thumbnail%20.png,https://courses.analyticsvidhya.com/courses/creating-problem-solving-agents-using-genai-for-action-composition
+Improving Real World RAG Systems: Key Challenges & Practical Solutions,https://import.cdn.thinkific.com/118220/70WfAJhFRsyXXdLQcWIl_281762.jpg,https://courses.analyticsvidhya.com/courses/improving-real-world-rag-systems-key-challenges
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