Upload 3 files

app.py

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+# app.py
+
+import streamlit as st
+from rag_engine import (
+    load_user_document,
+    build_retriever_from_docs,
+    answer_question
+)
+import os
+
+# Set environment variables
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+os.environ["USER_AGENT"] = "PythonicAI/1.0 (+adinafraz01@gmail.com)"
+
+st.set_page_config(
+    page_title="AI Documentation Assistant",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# -------------------- SESSION STATE --------------------
+if "retriever" not in st.session_state:
+    st.session_state.retriever = None
+
+if "docs_loaded" not in st.session_state:
+    st.session_state.docs_loaded = False
+
+if "current_url" not in st.session_state:
+    st.session_state.current_url = None
+
+if "processing" not in st.session_state:
+    st.session_state.processing = False
+
+# -------------------- CUSTOM CSS --------------------
+st.markdown("""
+<style>
+body {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+    min-height: 100vh;
+}
+
+.main-container {
+    max-width: 1000px;
+    margin: 0 auto;
+}
+
+.header-card {
+    background: rgba(255,255,255,0.95);
+    border-radius: 20px;
+    padding: 30px;
+    margin-bottom: 30px;
+    box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+    backdrop-filter: blur(10px);
+    border: 1px solid rgba(255,255,255,0.2);
+}
+
+.input-card {
+    background: rgba(255,255,255,0.95);
+    border-radius: 15px;
+    padding: 25px;
+    margin-bottom: 25px;
+    box-shadow: 0 8px 32px rgba(0,0,0,0.1);
+    border: 1px solid rgba(255,255,255,0.2);
+}
+
+.answer-card {
+    background: white;
+    border-radius: 15px;
+    padding: 30px;
+    margin-top: 25px;
+    box-shadow: 0 10px 40px rgba(0,0,0,0.08);
+    animation: fadeIn 0.5s ease;
+    border-left: 5px solid #667eea;
+}
+
+.url-display {
+    background: #f0f4ff;
+    border-radius: 10px;
+    padding: 15px;
+    margin: 15px 0;
+    border-left: 4px solid #4f46e5;
+    word-break: break-all;
+}
+
+.stButton button {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    font-weight: 600;
+    border: none;
+    border-radius: 12px;
+    padding: 14px 28px;
+    width: 100%;
+    transition: all 0.3s;
+    font-size: 16px;
+}
+
+.stButton button:hover {
+    transform: translateY(-3px);
+    box-shadow: 0 7px 20px rgba(0,0,0,0.2);
+}
+
+.primary-btn {
+    background: linear-gradient(135deg, #4f46e5 0%, #7c3aed 100%) !important;
+}
+
+.secondary-btn {
+    background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important;
+}
+
+.question-box {
+    background: #f8fafc;
+    border-radius: 12px;
+    padding: 20px;
+    border: 2px solid #e2e8f0;
+    font-size: 16px;
+    min-height: 120px;
+}
+
+.info-box {
+    background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
+    color: white;
+    padding: 20px;
+    border-radius: 15px;
+    margin: 15px 0;
+}
+
+.example-chip {
+    display: inline-block;
+    background: #e0e7ff;
+    color: #4f46e5;
+    padding: 8px 16px;
+    margin: 5px;
+    border-radius: 20px;
+    cursor: pointer;
+    font-size: 14px;
+    transition: all 0.2s;
+    border: 1px solid #c7d2fe;
+}
+
+.example-chip:hover {
+    background: #c7d2fe;
+    transform: translateY(-2px);
+}
+
+@keyframes fadeIn {
+    from { opacity:0; transform:translateY(10px); }
+    to { opacity:1; transform:translateY(0); }
+}
+
+/* Scrollbar styling */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: #f1f1f1;
+    border-radius: 10px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    border-radius: 10px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: linear-gradient(135deg, #5a67d8 0%, #6d28d9 100%);
+}
+</style>
+""", unsafe_allow_html=True)
+
+# -------------------- SIDEBAR --------------------
+with st.sidebar:
+    st.markdown("""
+    <div style="text-align:center; padding:18px 0;">
+        <h2 style="color:black; margin-bottom:2px; font-size:20px;">📚 AI Doc Assistant</h2>
+        <p style="color:black; font-size:20px;">Python Documentation Q&A</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("---")
+    
+    if st.session_state.docs_loaded:
+        st.markdown("### ✅ Document Status")
+        st.success("Document Loaded Successfully")
+        
+        if st.session_state.current_url:
+            st.markdown("**Current URL:**")
+            st.code(st.session_state.current_url[:80] + "..." if len(st.session_state.current_url) > 80 else st.session_state.current_url, language="text")
+        
+        if st.button("🗑️ Clear Document", use_container_width=True):
+            st.session_state.docs_loaded = False
+            st.session_state.retriever = None
+            st.session_state.current_url = None
+            st.rerun()
+    else:
+        st.markdown("### ⚠️ Status")
+        st.info("No document loaded")
+    
+    st.markdown("---")
+    st.markdown("### 📖 How to Use")
+    st.markdown("""
+    1. **Enter a URL** pointing to Python documentation
+    2. Click **Load Document** to process
+    3. Ask **Python-related questions** about the content
+    4. Get instant, context-aware answers
+    """)
+    
+    st.markdown("---")
+    st.markdown("### 💡 Example URLs")
+    urls = [
+        "https://numpy.org/doc/2.3/user/absolute_beginners.html",
+        "https://pandas.pydata.org/docs/",
+        "https://matplotlib.org/stable/users/index.html",
+        "https://scikit-learn.org/stable/user_guide.html"
+    ]
+    
+    for url in urls:
+        if st.button(f"📄 {url.split('/')[2]}", key=f"url_{url}"):
+            st.session_state.url_input = url
+            st.rerun()
+    
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align:center; color:black; font-size:12px; padding:10px;">
+        Powered by OpenAI • LangChain • FAISS
+    </div>
+    """, unsafe_allow_html=True)
+
+# -------------------- MAIN CONTENT --------------------
+st.markdown('<div class="main-container">', unsafe_allow_html=True)
+
+# Header
+st.markdown("""
+<div class="header-card">
+    <h1 style="text-align:center; font-size:42px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin-bottom:10px;">
+        🤖 AI Documentation Assistant
+    </h1>
+    <p style="text-align:center; font-size:18px; color:#4b5563; margin-bottom:20px;">
+        Ask questions about any Python documentation from a URL
+    </p>
+</div>
+""", unsafe_allow_html=True)
+
+# URL Input Section
+st.markdown('<div class="input-card">', unsafe_allow_html=True)
+st.markdown("### 🌐 Enter Documentation URL")
+
+# Initialize session state for URL if not exists
+if "url_input" not in st.session_state:
+    st.session_state.url_input = "https://numpy.org/doc/2.3/user/absolute_beginners.html"
+
+url = st.text_input(
+    "Enter the URL of Python documentation:",
+    value=st.session_state.url_input,
+    placeholder="https://example.com/python-docs",
+    label_visibility="collapsed",
+    key="url_input_field"
+)
+
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    load_clicked = st.button(
+        "📥 Load Document",
+        use_container_width=True,
+        type="primary",
+        disabled=not url.strip()
+    )
+
+with col2:
+    if st.session_state.docs_loaded:
+        if st.button("🔄 Reload", use_container_width=True):
+            load_clicked = True
+
+st.markdown('</div>', unsafe_allow_html=True)
+
+# Handle document loading
+if load_clicked:
+    if not url.strip():
+        st.error("❌ Please enter a valid URL")
+    else:
+        try:
+            with st.spinner("🔍 Loading and processing documentation..."):
+                # Store current URL
+                st.session_state.current_url = url
+                
+                # Show progress
+                progress_text = st.empty()
+                progress_bar = st.progress(0)
+                
+                progress_text.text("Downloading document...")
+                docs = load_user_document(url)
+                progress_bar.progress(30)
+                
+                progress_text.text("Processing content...")
+                st.session_state.retriever = build_retriever_from_docs(docs)
+                progress_bar.progress(70)
+                
+                progress_text.text("Finalizing...")
+                st.session_state.docs_loaded = True
+                progress_bar.progress(100)
+                
+                progress_text.text("✅ Ready!")
+            
+            st.success(f"✅ Successfully loaded documentation from: {url}")
+           
+            
+            # Show document info
+            st.markdown(f"""
+            <div class="url-display">
+                <strong>📄 Loaded Document:</strong> {url}<br>
+                <strong>📊 Pages Processed:</strong> {len(docs)}<br>
+                <strong>✅ Status:</strong> Ready for questions
+            </div>
+            """, unsafe_allow_html=True)
+            
+        except Exception as e:
+            st.error(f"❌ Error loading document: {str(e)}")
+
+# Question Input Section (only show if document is loaded)
+if st.session_state.docs_loaded:
+    st.markdown('<div class="input-card">', unsafe_allow_html=True)
+    
+    st.markdown("### 💭 Ask a Question")
+    
+    # Example questions
+    st.markdown("**💡 Try asking:**")
+    col1, col2, col3, col4 = st.columns(4)
+    
+    example_questions = [
+        "What is this library about?",
+        "How to get started?",
+        "What are the main functions?",
+        "Give me a code example"
+    ]
+    
+    for i, question in enumerate(example_questions):
+        col = [col1, col2, col3, col4][i]
+        with col:
+            if st.button(question, use_container_width=True):
+                st.session_state.question_input = question
+                st.rerun()
+    
+    # Question input
+    question = st.text_area(
+        "Enter your Python-related question:",
+        value=st.session_state.get("question_input", ""),
+        placeholder="Example: What is NumPy? How to create arrays? What are the main functions?",
+        height=120,
+        key="question_input_area"
+    )
+    
+    # Answer button
+    col1, col2, col3 = st.columns([2, 1, 1])
+    
+    with col2:
+        if st.button("🚀 Get Answer", type="primary", use_container_width=True):
+            if not question.strip():
+                st.warning("Please enter a question")
+            else:
+                st.session_state.processing = True
+                st.session_state.current_question = question
+    
+    with col3:
+        if st.button("🗑️ Clear", use_container_width=True):
+            st.session_state.question_input = ""
+            st.rerun()
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Process and display answer
+    if st.session_state.get("processing") and st.session_state.get("current_question"):
+        with st.spinner("🤔 Analyzing document and generating answer..."):
+            try:
+                answer = answer_question(
+                    st.session_state.current_question, 
+                    st.session_state.retriever
+                )
+                
+                st.markdown("### 📝 Answer")
+                st.markdown(f'<div class="answer-card">{answer}</div>', unsafe_allow_html=True)
+                
+                # Reset processing state
+                st.session_state.processing = False
+                
+            except Exception as e:
+                st.error(f"❌ Error generating answer: {str(e)}")
+                st.session_state.processing = False
+    
+    # Quick actions
+    st.markdown("""
+    <div style="text-align:center; margin-top:30px;">
+        <hr style="border:none; height:1px; background:linear-gradient(90deg, transparent, #667eea, transparent); margin:20px 0;">
+        <p style="color:#6b7280; font-size:14px;">
+            💡 Tip: Ask specific questions for better answers. Try asking about functions, usage examples, or concepts.
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
+
+else:
+    # Welcome message when no document is loaded
+    st.markdown("""
+    <div class="info-box">
+        <h3>👋 Welcome to AI Documentation Assistant!</h3>
+        <p>Get started by entering a URL to Python documentation above.</p>
+        <p><strong>Try the default URL</strong> or enter your own documentation link.</p>
+    </div>
+    
+    <div style="background:white; border-radius:15px; padding:30px; margin-top:20px;">
+        <h3 style="color:#4f46e5;">✨ Features:</h3>
+        <ul style="font-size:16px; line-height:1.8;">
+            <li><strong>📚 URL-based</strong> - Just paste a documentation URL</li>
+            <li><strong>🤖 AI-powered</strong> - Get intelligent answers from docs</li>
+            <li><strong>⚡ Fast processing</strong> - Quick document analysis</li>
+            <li><strong>💬 Interactive</strong> - Ask follow-up questions</li>
+            <li><strong>🎯 Python-focused</strong> - Optimized for Python documentation</li>
+        </ul>
+        
+        <h3 style="color:#4f46e5; margin-top:30px;">🚀 Quick Start:</h3>
+        <ol style="font-size:16px; line-height:1.8;">
+            <li>Enter a Python documentation URL (or use the default)</li>
+            <li>Click <strong>"Load Document"</strong></li>
+            <li>Wait a few seconds for processing</li>
+            <li>Ask any Python-related question about the documentation</li>
+        </ol>
+    </div>
+    """, unsafe_allow_html=True)
+
+st.markdown('</div>', unsafe_allow_html=True)

rag_engine.py

@@ -0,0 +1,180 @@
+# rag_engine.py
+
+import os
+import warnings
+from typing import List
+from dotenv import load_dotenv
+
+# Suppress warnings
+warnings.filterwarnings('ignore')
+
+# Set environment variables before imports
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+os.environ["USER_AGENT"] = "PythonicAI/1.0 (+adinafraz01@gmail.com)"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Load env variables
+load_dotenv()
+openai_key = os.getenv("OPENAI_API_KEY")
+
+if not openai_key:
+    raise ValueError("OPENAI_API_KEY not found in environment variables. Please create a .env file with your API key.")
+
+os.environ["OPENAI_API_KEY"] = openai_key
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_core.prompts import PromptTemplate
+from langchain.schema import Document
+
+# ------------------------------------------
+# DOCUMENT LOADING (URL ONLY)
+# ------------------------------------------
+
+def load_user_document(url: str) -> List[Document]:
+    """Load URL into LangChain documents."""
+    try:
+        if not url:
+            raise ValueError("No URL provided")
+        
+        # Clean URL
+        if not url.startswith(('http://', 'https://')):
+            url = f'https://{url}'
+            
+        # Use WebBaseLoader with custom headers
+        headers = {
+            'User-Agent': os.environ.get("USER_AGENT", "PythonicAI/1.0")
+        }
+        
+        loader = WebBaseLoader(
+            web_paths=[url],
+            requests_per_second=1,
+            requests_kwargs={'headers': headers}
+        )
+        docs = loader.load()
+        
+        if not docs:
+            raise ValueError("No content found in the URL")
+            
+        return docs
+        
+    except Exception as e:
+        raise Exception(f"Error loading URL: {str(e)}")
+
+# ------------------------------------------
+# BUILD RETRIEVER FROM DOCUMENTS
+# ------------------------------------------
+
+def build_retriever_from_docs(docs: List[Document]):
+    """Build FAISS vector store from documents."""
+    if not docs:
+        raise ValueError("No documents provided")
+    
+    # Use the same splitter settings as your example
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200
+    )
+    
+    chunks = splitter.split_documents(docs)
+    
+    # Use the same embedding model as your example
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    
+    # Build vector store
+    vector_store = FAISS.from_documents(chunks, embeddings)
+    
+    return vector_store.as_retriever(
+        search_type="similarity",
+        search_kwargs={"k": 6}
+    )
+
+# ------------------------------------------
+# PYTHON DOMAIN CHECK
+# ------------------------------------------
+
+_llm = None
+
+def get_llm():
+    """Singleton pattern for LLM to avoid re-initialization."""
+    global _llm
+    if _llm is None:
+        _llm = ChatOpenAI(
+            model="gpt-4o-mini",
+            temperature=0.2,
+            max_tokens=1000,
+            request_timeout=60
+        )
+    return _llm
+
+def is_python_related(question: str) -> bool:
+    """Check if question is Python-related."""
+    python_keywords = [
+        'python', 'numpy', 'pandas', 'matplotlib', 'seaborn',
+        'machine learning', 'deep learning', 'tensorflow', 'pytorch',
+        'scikit-learn', 'sklearn', 'dataframe', 'array', 'plot',
+        'import', 'def ', 'class ', 'function', 'method', 'library',
+        'package', 'module', 'pip install', 'conda install', 'jupyter',
+        'data', 'science', 'analysis', 'visualization', 'programming',
+        'code', 'script', 'algorithm', 'model', 'training', 'prediction'
+    ]
+    
+    # Quick keyword check first
+    question_lower = question.lower()
+    for keyword in python_keywords:
+        if keyword in question_lower:
+            return True
+    
+    return False
+
+# ------------------------------------------
+# ANSWER QUESTION
+# ------------------------------------------
+
+# Define the prompt template (same as your example)
+prompt = PromptTemplate(
+    template="""
+    You are a helpful python assistant. 
+    Answer ONLY from the provided text context.
+    Display answer in a beautiful and user understandable format with proper formatting.
+    Use bullet points, numbered lists, or code blocks when appropriate.
+    If the context is insufficient, just say you don't know.
+
+    Context: {context}
+    Question: {question}
+    """,
+    input_variables=['context', 'question']
+)
+
+def answer_question(question: str, retriever):
+    """Answer question using RAG pipeline."""
+    # Validate topic
+    if not is_python_related(question):
+        return "I can only answer Python and data science related questions based on the provided documentation."
+    
+    try:
+        # Retrieve relevant documents
+        docs = retriever.invoke(question)
+        
+        if not docs:
+            return "I couldn't find relevant information in the documentation to answer this question."
+        
+        # Combine context from retrieved documents
+        context = "\n\n".join(doc.page_content for doc in docs)
+        
+        # Create final prompt
+        final_prompt = prompt.invoke({
+            "context": context,
+            "question": question
+        })
+        
+        # Get answer from LLM
+        llm = get_llm()
+        answer = llm.invoke(final_prompt)
+        
+        return answer.content
+        
+    except Exception as e:
+        return f"Error generating answer: {str(e)}"

requirements.txt

@@ -1,3 +1,10 @@
-altair
-pandas
-streamlit
+streamlit
+langchain
+langchain-community
+langchain-openai
+openai
+python-dotenv
+faiss-cpu
+beautifulsoup4
+requests
+pypdf