Update app.py

app.py

@@ -1,77 +1,216 @@
-import torch
-from transformers import RagRetriever, RagTokenizer, RagSequenceForGeneration
 import gradio as gr
-from PyPDF2 import PdfReader
-import re
-
-# Laden des Tokenizers, des Retrievers und des Modells (auf CPU)
-model_name = "facebook/rag-token-nq"
-tokenizer = RagTokenizer.from_pretrained(model_name)
-retriever = RagRetriever.from_pretrained(model_name, index_name="exact")
-model = RagSequenceForGeneration.from_pretrained(model_name).to("cpu")  # Modell auf CPU laden
-
-# Funktion zum Extrahieren und Bereinigen von Text aus PDF
-def extract_text_from_pdf(pdf_path):
-    reader = PdfReader(pdf_path)
-    text = ""
-    for page in reader.pages:
-        page_text = page.extract_text()
-        if page_text:
-            text += page_text
-    return text
-
-def clean_text(text):
-    # Entfernen unnötiger Zeichen, Reduktion von Leerzeichen
-    text = re.sub(r'\s+', ' ', text)
-    text = re.sub(r'[^\w\s.,-]', '', text)
-    return text.strip()
-
-# Funktion zum Aufteilen langer Texte in Abschnitte
-def split_text_into_chunks(text, chunk_size=1000):
-    words = text.split()
-    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
-    return chunks
-
-# Hauptfunktion für die Fragebeantwortung mit RAG
-def chatbot_response(pdf_path, question):
-    try:
-        # PDF-Inhalt extrahieren und bereinigen
-        context = clean_text(extract_text_from_pdf(pdf_path))
-        if not context:
-            return "Das Dokument enthält keinen Text oder konnte nicht gelesen werden."
-
-        # Dokumenttext in Abschnitte aufteilen, um Speicher zu sparen
-        chunks = split_text_into_chunks(context)
-
-        # Antwortgenerierung mit minimalem Speicherverbrauch
-        answers = []
-        with torch.no_grad():  # Verhindert das Speichern von Gradienten (für CPU wichtig)
-            for chunk in chunks:
-                retriever.index = [chunk]
-                inputs = tokenizer(question, return_tensors="pt").to("cpu")  # Sicherstellen, dass Inputs auf CPU bleiben
-                generated_ids = model.generate(**inputs, max_length=150)  # Kürzere Antwortlänge
-                answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-                if answer:
-                    answers.append(answer)
-
-        final_answer = " / ".join(answers) if answers else "Keine spezifische Antwort gefunden."
-        return final_answer
-
-    except Exception as e:
-        return f"Es ist ein Fehler aufgetreten: {str(e)}"
-
-# Gradio-Interface erstellen
-pdf_input = gr.File(label="PDF-Datei hochladen", type="filepath")
-question_input = gr.Textbox(label="Frage eingeben", placeholder="Stelle eine Frage zu dem PDF-Dokument")
-response_output = gr.Textbox(label="Antwort", lines=4)
-
-interface = gr.Interface(
-    fn=chatbot_response,
-    inputs=[pdf_input, question_input],
-    outputs=response_output,
-    title="RAG PDF-Fragebeantwortung auf CPU",
-    description="Lade eine PDF-Datei hoch und stelle Fragen zu ihrem Inhalt. Das System verwendet Retrieval-Augmented Generation (RAG) auf CPU zur Beantwortung.",
-)
-
-# Interface für Hugging Face Spaces
-interface.launch(share=True)
+import os
+api_token = os.getenv("HF_TOKEN")
+
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+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"]  
+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())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 1024, 
+        chunk_overlap = 64 
+    )  
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings()
+    vectordb = FAISS.from_documents(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,
+        )
+    
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+
+    retriever=vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=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)
+    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)
+    return qa_chain, "QA chain initialized. Chatbot is ready!"
+
+
+def format_chat_history(message, chat_history):
+    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):
+    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
+
+
+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:
+        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>
+        """)
+        with gr.Row():
+            with gr.Column(scale = 86):
+                gr.Markdown("<b>Step 1 - Upload PDF documents 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")
+                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>")
+                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
+                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)
+                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", 
+
+            with gr.Column(scale = 200):
+                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                chatbot = gr.Chatbot(height=505)
+                with gr.Accordion("Relevent context from the source document", 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)
+                    with gr.Row():
+                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                        source2_page = gr.Number(label="Page", 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)
+                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)
+
+
+if __name__ == "__main__":
+    demo()