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app.py

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+import os
+import re
+import logging
+import requests
+import numpy as np
+import faiss
+from bs4 import BeautifulSoup
+from sentence_transformers import SentenceTransformer
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS as LangchainFAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms import Together
+from langchain.chains import RetrievalQA
+import gradio as gr
+
+# Set Together.ai API key
+os.environ["TOGETHER_API_KEY"] = os.getenv("TOGETHER_API_KEY", "a36246d65d8290f43667350b364c5b6bb8562eb50a4b947eec5bd7e79f2dffc6")
+
+# Logging setup
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Step 1: Load and chunk webpage
+def fetch_webpage_text(url):
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.text, "html.parser")
+        content_div = soup.find("div", {"id": "mw-content-text"}) or soup.body
+        return content_div.get_text(separator="\n", strip=True)
+    except Exception as e:
+        logger.error(f"Error fetching content from {url}: {e}")
+        return ""
+
+def clean_text(text):
+    text = re.sub(r'\[\s*\d+\s*\]', '', text)
+    text = re.sub(r'\[\s*[a-zA-Z]+\s*\]', '', text)
+    text = re.sub(r'\n{2,}', '\n', text)
+    text = re.sub(r'[ \t]+', ' ', text)
+    return text.strip()
+
+def chunk_text(text, chunk_size=500, overlap=50):
+    cleaned = clean_text(text)
+    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
+    return splitter.split_text(cleaned)
+
+def load_and_chunk_webpage(url):
+    text = fetch_webpage_text(url)
+    return chunk_text(text)
+
+# Step 2: Embed chunks using SentenceTransformer
+def embed_chunks(chunks):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = model.encode(chunks, normalize_embeddings=True)
+    return embeddings, model
+
+# Step 3: Build FAISS index using LangChain wrapper
+def build_retriever(chunks):
+    embedding_func = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = LangchainFAISS.from_texts(chunks, embedding_func)
+    return db.as_retriever(search_type="similarity", search_kwargs={"k": 3}), db
+
+# Step 4: Initialize LLM and RAG Chain
+def initialize_llm():
+    return Together(
+        model="meta-llama/Llama-3-8b-chat-hf",
+        temperature=0.7,
+        max_tokens=512
+    )
+
+# Initialize all components
+wiki_url = "https://en.wikipedia.org/wiki/LLaMA"
+chunks = load_and_chunk_webpage(wiki_url)
+embeddings, embed_model = embed_chunks(chunks)
+retriever, db = build_retriever(chunks)
+llm = initialize_llm()
+
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    retriever=retriever,
+    chain_type="stuff"
+)
+
+# Chat logic
+def chat_with_bot(query):
+    if not query.strip():
+        return "❗ Please enter a question."
+    return qa_chain.run(query)
+
+# Summary logic
+def summarize_content():
+    sample_text = " ".join(chunks[:20])
+    prompt = f"Summarize this text in 5 bullet points:\n\n{sample_text[:3000]}"
+    summary = llm.invoke(prompt)
+    return summary.content if hasattr(summary, "content") else summary
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Chat with LLaMA Webpage Content")
+
+    with gr.Row():
+        chatbot = gr.Chatbot(label="Chat History")
+
+    with gr.Row():
+        question = gr.Textbox(label="Ask your question about LLaMA", placeholder="e.g., Who developed LLaMA?")
+        ask_btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear Chat")
+
+    summary_output = gr.Textbox(label="📋 Summary of the Webpage", lines=8)
+    summarize_btn = gr.Button("Summarize Content")
+
+    def user_chat_handler(q, history):
+        response = chat_with_bot(q)
+        history.append((q, response))
+        return history, ""
+
+    ask_btn.click(fn=user_chat_handler, inputs=[question, chatbot], outputs=[chatbot, question])
+    clear_btn.click(lambda: [], None, chatbot)
+    summarize_btn.click(fn=summarize_content, inputs=[], outputs=summary_output)
+
+demo.launch()

requirements.txt

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+gradio
+beautifulsoup4
+requests
+langchain
+langchain-community
+huggingface-hub
+sentence-transformers
+faiss-cpu
+together