Update app.py

app.py

@@ -1,64 +1,75 @@
 import gradio as gr
 import os
-api_token = os.getenv("HF_TOKEN")
-
-
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFLoader
+from bs4 import BeautifulSoup
+import requests
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
 from langchain.chains import ConversationalRetrievalChain
-from langchain_community.embeddings import HuggingFaceEmbeddings 
-from langchain_community.llms import HuggingFacePipeline
-from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
 from langchain_community.llms import HuggingFaceEndpoint
-import torch
 
-list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
+# Initialize environment
+api_token =os.getenv("HF_TOKEN")
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Load and split PDF document
-def load_doc(list_file_path):
-    # Processing for one document only
-    # loader = PyPDFLoader(file_path)
-    # pages = loader.load()
-    loaders = [PyPDFLoader(x) for x in list_file_path]
-    pages = []
-    for loader in loaders:
-        pages.extend(loader.load())
+def scrape_website(url):
+    """Scrape text content from a website"""
+    try:
+        response = requests.get(url)
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        for script in soup(["script", "style"]):
+            script.decompose()
+            
+        text = soup.get_text()
+        lines = (line.strip() for line in text.splitlines())
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        text = ' '.join(chunk for chunk in chunks if chunk)
+        
+        return text
+    except Exception as e:
+        return f"Error scraping website: {str(e)}"
+
+def process_text(text):
+    """Split text into chunks"""
     text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = 1024, 
-        chunk_overlap = 64 
-    )  
-    doc_splits = text_splitter.split_documents(pages)
-    return doc_splits
+        chunk_size=1024,
+        chunk_overlap=64
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
 
-# Create vector database
 def create_db(splits):
+    """Create vector database"""
     embeddings = HuggingFaceEmbeddings()
-    vectordb = FAISS.from_documents(splits, embeddings)
+    vectordb = FAISS.from_documents([{"page_content": text, "metadata": {}} for text in splits], embeddings)
     return vectordb
 
-
-# Initialize langchain LLM chain
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            huggingfacehub_api_token = api_token,
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
-    else:
-        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token = api_token,
-            repo_id=llm_model, 
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
+def initialize_database(url, progress=gr.Progress()):
+    """Initialize database from URL"""
+    # Scrape website content
+    text_content = scrape_website(url)
+    if text_content.startswith("Error"):
+        return None, text_content
+    
+    # Create text chunks
+    doc_splits = process_text(text_content)
+    
+    # Create vector database
+    vector_db = create_db(doc_splits)
+    return vector_db, "Database created successfully!"
+
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    """Initialize LLM chain"""
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+    )
     
     memory = ConversationBufferMemory(
         memory_key="chat_history",
@@ -66,151 +77,128 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         return_messages=True
     )
 
-    retriever=vector_db.as_retriever()
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
-        retriever=retriever,
-        chain_type="stuff", 
+        retriever=vector_db.as_retriever(),
+        chain_type="stuff",
         memory=memory,
         return_source_documents=True,
         verbose=False,
     )
     return qa_chain
 
-# Initialize database
-def initialize_database(list_file_obj, progress=gr.Progress()):
-    # Create a list of documents (when valid)
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Load document and create splits
-    doc_splits = load_doc(list_file_path)
-    # Create or load vector database
-    vector_db = create_db(doc_splits)
-    return vector_db, "Database created!"
-
-# Initialize LLM
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
+    """Initialize LLM with selected options"""
     llm_name = list_llm[llm_option]
-    print("llm_name: ",llm_name)
-    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
+    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
     return qa_chain, "QA chain initialized. Chatbot is ready!"
 
-
 def format_chat_history(message, chat_history):
+    """Format chat history for the model"""
     formatted_chat_history = []
     for user_message, bot_message in chat_history:
         formatted_chat_history.append(f"User: {user_message}")
         formatted_chat_history.append(f"Assistant: {bot_message}")
     return formatted_chat_history
-    
 
 def conversation(qa_chain, message, history):
+    """Handle conversation with the model"""
     formatted_chat_history = format_chat_history(message, history)
-    # Generate response using QA chain
     response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
+    
     response_answer = response["answer"]
     if response_answer.find("Helpful Answer:") != -1:
         response_answer = response_answer.split("Helpful Answer:")[-1]
-    response_sources = response["source_documents"]
-    response_source1 = response_sources[0].page_content.strip()
-    response_source2 = response_sources[1].page_content.strip()
-    response_source3 = response_sources[2].page_content.strip()
-    # Langchain sources are zero-based
-    response_source1_page = response_sources[0].metadata["page"] + 1
-    response_source2_page = response_sources[1].metadata["page"] + 1
-    response_source3_page = response_sources[2].metadata["page"] + 1
-    # Append user message and response to chat history
-    new_history = history + [(message, response_answer)]
-    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
     
-
-def upload_file(file_obj):
-    list_file_path = []
-    for idx, file in enumerate(file_obj):
-        file_path = file_obj.name
-        list_file_path.append(file_path)
-    return list_file_path
-
+    response_sources = response["source_documents"][:3]
+    sources = []
+    for i in range(3):
+        if i < len(response_sources):
+            sources.append((response_sources[i].page_content.strip(), 1))
+        else:
+            sources.append(("", 1))
+    
+    new_history = history + [(message, response_answer)]
+    return (qa_chain, gr.update(value=""), new_history, 
+            sources[0][0], sources[0][1],
+            sources[1][0], sources[1][1],
+            sources[2][0], sources[2][1])
 
 def demo():
-    # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo:
-    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
-        gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
-        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
-        <b>Please do not upload confidential documents.</b>
-        """)
+        
+        gr.HTML("<center><h1>RAG Website Chatbot</h1></center>")
+        gr.Markdown("""<b>Query any website content!</b> This AI agent performs retrieval augmented generation (RAG) on website content.""")
+        
         with gr.Row():
-            with gr.Column(scale = 86):
-                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
+            with gr.Column(scale=86):
+                gr.Markdown("<b>Step 1 - Enter Website URL and Initialize RAG pipeline</b>")
                 with gr.Row():
-                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                    url_input = gr.Textbox(label="Website URL", placeholder="Enter website URL here...")
                 with gr.Row():
                     db_btn = gr.Button("Create vector database")
                 with gr.Row():
-                        db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
-                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
+                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
+                
+                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
                 with gr.Row():
-                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
+                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
                 with gr.Row():
                     with gr.Accordion("LLM input parameters", open=False):
-                        with gr.Row():
-                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
-                        with gr.Row():
-                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
-                        with gr.Row():
-                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
+                        slider_temperature = gr.Slider(0.01, 1.0, 0.5, step=0.1, label="Temperature")
+                        slider_maxtokens = gr.Slider(128, 9192, 4096, step=128, label="Max New Tokens")
+                        slider_topk = gr.Slider(1, 10, 3, step=1, label="top-k")
                 with gr.Row():
                     qachain_btn = gr.Button("Initialize Question Answering Chatbot")
                 with gr.Row():
-                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
+                    llm_progress = gr.Textbox(value="Not initialized", show_label=False)
 
-            with gr.Column(scale = 200):
-                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+            with gr.Column(scale=200):
+                gr.Markdown("<b>Step 2 - Chat about the Website Content</b>")
                 chatbot = gr.Chatbot(height=505)
-                with gr.Accordion("Relevent context from the source document", open=False):
+                with gr.Accordion("Relevant context from the source", open=False):
                     with gr.Row():
                         doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                        source1_page = gr.Number(label="Page", scale=1)
+                        source1_page = gr.Number(label="Section", scale=1)
                     with gr.Row():
                         doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                        source2_page = gr.Number(label="Page", scale=1)
+                        source2_page = gr.Number(label="Section", scale=1)
                     with gr.Row():
                         doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                        source3_page = gr.Number(label="Page", scale=1)
+                        source3_page = gr.Number(label="Section", scale=1)
                 with gr.Row():
                     msg = gr.Textbox(placeholder="Ask a question", container=True)
                 with gr.Row():
                     submit_btn = gr.Button("Submit")
                     clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
-            
-        # Preprocessing events
-        db_btn.click(initialize_database, \
-            inputs=[document], \
-            outputs=[vector_db, db_progress])
-        qachain_btn.click(initialize_LLM, \
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
 
-        # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        submit_btn.click(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-    demo.queue().launch(debug=True)
+        # Event handlers
+        db_btn.click(initialize_database,
+                    inputs=[url_input],
+                    outputs=[vector_db, db_progress])
+        
+        qachain_btn.click(initialize_LLM,
+                         inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
+                         outputs=[qa_chain, llm_progress])
+        
+        msg.submit(conversation,
+                  inputs=[qa_chain, msg, chatbot],
+                  outputs=[qa_chain, msg, chatbot, doc_source1, source1_page,
+                          doc_source2, source2_page, doc_source3, source3_page])
+        
+        submit_btn.click(conversation,
+                        inputs=[qa_chain, msg, chatbot],
+                        outputs=[qa_chain, msg, chatbot, doc_source1, source1_page,
+                                doc_source2, source2_page, doc_source3, source3_page])
+        
+        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
+                       inputs=None,
+                       outputs=[chatbot, doc_source1, source1_page,
+                               doc_source2, source2_page, doc_source3, source3_page])
 
+    demo.queue().launch(debug=True)
 
 if __name__ == "__main__":
-    demo()
+    demo()