Final

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chromadb/*.bin filter=lfs diff=lfs merge=lfs -text
+chromadb/*.sqlite3 filter=lfs diff=lfs merge=lfs -text
+chroma_db/**/* filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,239 @@
+import streamlit as st
+import chromadb
+from chromadb.utils import embedding_functions
+import groq
+from typing import Dict
+import os
+
+class CourseAdvisor:
+    def __init__(self, db_path: str = "./chroma_db"):
+        """Initialize the course advisor with existing ChromaDB database."""
+        # Initialize persistent client with path
+        self.chroma_client = chromadb.PersistentClient(path=db_path)
+        
+        # Initialize embedding function
+        self.embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(
+            model_name="jinaai/jina-embeddings-v2-base-en"
+        )
+        
+        # Get existing collection
+        self.collection = self.chroma_client.get_collection(
+            name="courses",
+            embedding_function=self.embedding_function
+        )
+        
+    def query_courses(self, query_text: str, chat_history: str, api_key: str, n_results: int = 3) -> Dict:
+        """Query the vector database and get course recommendations."""
+        # Initialize Groq client with provided API key
+        groq_client = groq.Groq(api_key=api_key)
+        
+        try:
+            # Get relevant documents from vector DB
+            results = self.collection.query(
+                query_texts=[query_text],
+                n_results=min(n_results, self.collection.count()),
+                include=['documents', 'metadatas']
+            )
+
+            # Prepare context from retrieved documents
+            docs_context = "\n\n".join(results['documents'][0])
+            
+        except Exception as e:
+            st.error(f"Error querying database: {str(e)}")
+            return {
+                'llm_response': "I encountered an error while searching the course database. Please try again.",
+                'retrieved_courses': []
+            }
+        
+        # Create prompt with chat history
+        prompt = f"""Previous conversation:
+{chat_history}
+
+Current user query: {query_text}
+
+Relevant course information:
+{docs_context}
+
+Please provide course recommendations based on the entire conversation context. Format your response as:
+1. Understanding of the user's needs (based on conversation history)
+2. Overall recommendation with reasoning
+3. Specific benefits of each recommended course
+4. Learning path suggestion (if applicable)
+5. Any prerequisites or important notes"""
+
+        try:
+            # Get response from Groq
+            completion = groq_client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "You are a helpful course advisor who provides detailed, relevant course recommendations based on the user's needs and conversation history. Keep responses clear and well-structured."},
+                    {"role": "user", "content": prompt}
+                ],
+                model="mixtral-8x7b-32768",
+                temperature=0.7,
+            )
+
+            return {
+                'llm_response': completion.choices[0].message.content,
+                'retrieved_courses': results['metadatas'][0]
+            }
+            
+        except Exception as e:
+            st.error(f"Error with Groq API: {str(e)}")
+            return {
+                'llm_response': "I encountered an error while generating recommendations. Please check your API key and try again.",
+                'retrieved_courses': []
+            }
+
+def initialize_session_state():
+    """Initialize session state variables."""
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+    if 'course_advisor' not in st.session_state:
+        st.session_state.course_advisor = CourseAdvisor()
+    if 'api_key' not in st.session_state:
+        st.session_state.api_key = ""
+
+def get_chat_history() -> str:
+    """Format chat history for LLM context."""
+    history = []
+    for message in st.session_state.messages[-5:]:  # Only use last 5 messages for context
+        role = message["role"]
+        content = message["content"]
+        history.append(f"{role}: {content}")
+    return "\n".join(history)
+
+def display_course_card(course: Dict):
+    """Display a single course recommendation in a card format."""
+    with st.container():
+        # Add a light background and padding
+        with st.container():
+            st.markdown("""
+                <style>
+                .course-card {
+                    background-color: #f8f9fa;
+                    padding: 1rem;
+                    border-radius: 0.5rem;
+                    margin-bottom: 1rem;
+                }
+                </style>
+            """, unsafe_allow_html=True)
+            
+            with st.container():
+                st.markdown('<div class="course-card">', unsafe_allow_html=True)
+                
+                # Course title
+                st.markdown(f"### {course['title']}")
+                
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    # Handle categories - convert to list if string
+                    categories = course.get('categories', 'N/A')
+                    if isinstance(categories, str):
+                        # Split by comma if it's a comma-separated string
+                        categories = [cat.strip() for cat in categories.split(',')]
+                    elif not isinstance(categories, list):
+                        categories = [str(categories)]
+                    
+                    # Display categories as bullet points if multiple
+                    if len(categories) > 1:
+                        st.markdown("**Categories:**")
+                        for category in categories:
+                            st.markdown(f"- {category}")
+                    else:
+                        st.markdown(f"**Category:** {categories[0]}")
+                    
+                    st.markdown(f"**Lessons:** {course.get('lessons', 'N/A')}")
+                
+                with col2:
+                    st.markdown(f"**Price:** {course.get('price', 'N/A')}")
+                    if 'url' in course:
+                        st.markdown(f"**[Visit Course]({course['url']})**")
+                
+                st.markdown('</div>', unsafe_allow_html=True)
+            
+        st.markdown("---")
+
+def main():
+    st.set_page_config(
+        page_title="Course Recommender",
+        page_icon="📚",
+        layout="wide"
+    )
+    
+    st.title("📚 AI Course Recommender")
+    
+    # Initialize session state
+    initialize_session_state()
+    
+    # Display collection info
+    collection = st.session_state.course_advisor.collection
+    st.sidebar.info(f"Connected to database with {collection.count()} courses")
+    
+    # Sidebar
+    with st.sidebar:
+        st.header("Settings")
+        
+        # API key input
+        api_key = st.text_input("Enter Groq API Key", 
+                               type="password",
+                               value=st.session_state.api_key)
+        if api_key != st.session_state.api_key:
+            st.session_state.api_key = api_key
+        
+        # Clear chat button
+        if st.button("Clear Chat History"):
+            st.session_state.messages = []
+    
+    # Main chat interface
+    st.header("Chat with AI Course Advisor")
+    
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    # Chat input
+    if prompt := st.chat_input("What would you like to learn?"):
+        # Check if API key is provided
+        if not st.session_state.api_key:
+            st.error("Please enter your Groq API key in the sidebar.")
+            return
+            
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        # Get AI response
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                # Get formatted chat history
+                chat_history = get_chat_history()
+                
+                # Query courses with chat history
+                response = st.session_state.course_advisor.query_courses(
+                    prompt,
+                    chat_history,
+                    st.session_state.api_key
+                )
+                
+                # Display AI recommendation
+                st.markdown(response['llm_response'])
+                
+                # Display course cards if any courses were retrieved
+                if response['retrieved_courses']:
+                    st.markdown("### 📋 Recommended Courses")
+                    for course in response['retrieved_courses']:
+                        display_course_card(course)
+        
+        # Add assistant response to chat history
+        st.session_state.messages.append({
+            "role": "assistant",
+            "content": response['llm_response'] + "\n\n" + "### Recommended Courses\n" +
+                      "\n".join([f"- {course['title']}" for course in response['retrieved_courses']])
+        })
+
+if __name__ == "__main__":
+    main()

chroma_db/bca992f1-533f-4712-9825-dffc5cb4917e/data_level0.bin

@@ -0,0 +1,3 @@
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chroma_db/bca992f1-533f-4712-9825-dffc5cb4917e/header.bin

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chroma_db/bca992f1-533f-4712-9825-dffc5cb4917e/length.bin

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chroma_db/chroma.sqlite3

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+size 2293760

requiremnts.txt

@@ -0,0 +1,3 @@
+streamlit
+chromadb
+groq