Update app.py

app.py

@@ -1,100 +1,74 @@
 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 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.chains import ConversationalRetrievalChain
 from langchain_community.llms import HuggingFaceEndpoint
-import torch
+from langchain.memory import ConversationBufferMemory
 
-list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
+# Liste der Modelle
+list_llm = ["google/flan-t5-small", "distilbert-base-uncased"]  # Leichtere, CPU-freundliche Modelle
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Load and split PDF document
+# PDF-Dokument laden und aufteilen
 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 
-    )  
+        chunk_size=512,  # Kleinere Chunks für schnellere Verarbeitung
+        chunk_overlap=32
+    )
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-# Create vector database
+# Erstellen der Vektordatenbank
 def create_db(splits):
     embeddings = HuggingFaceEmbeddings()
     vectordb = FAISS.from_documents(splits, embeddings)
     return vectordb
 
+# Initialisierung des LLM Chains
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k
+    )
 
-# 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()
+    retriever = vector_db.as_retriever()
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff", 
+        chain_type="stuff",
         memory=memory,
         return_source_documents=True,
-        verbose=False,
+        verbose=False
     )
     return qa_chain
 
-# Initialize database
-def initialize_database(list_file_obj, progress=gr.Progress()):
-    # Create a list of documents (when valid)
+# Initialisierung der Datenbank
+def initialize_database(list_file_obj):
     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!"
+    return vector_db, "Datenbank erfolgreich erstellt!"
 
-# Initialize LLM
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
+# Initialisierung des LLMs
+def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
     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!"
-
+    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
+    return qa_chain, "LLM erfolgreich initialisiert! Chatbot ist bereit."
 
 def format_chat_history(message, chat_history):
     formatted_chat_history = []
@@ -102,115 +76,48 @@ def format_chat_history(message, chat_history):
         formatted_chat_history.append(f"User: {user_message}")
         formatted_chat_history.append(f"Assistant: {bot_message}")
     return formatted_chat_history
-    
 
+# Chat-Funktion
 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:
+    if "Helpful Answer:" in response_answer:
         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
-
+    return qa_chain, gr.update(value=""), new_history
 
+# Gradio App erstellen
 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() 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 PDF Chatbot</h1></center>")
         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)
-
+            with gr.Column():
+                gr.Markdown("### Schritt 1: Lade PDF-Dokument hoch")
+                document = gr.Files(height=300, file_count="multiple", file_types=[".pdf"], interactive=True)
+                db_btn = gr.Button("Erstelle Vektordatenbank")
+                db_progress = gr.Textbox(value="Nicht initialisiert", show_label=False)
+                gr.Markdown("### Schritt 2: Wähle LLM und Einstellungen")
+                llm_btn = gr.Radio(list_llm_simple, label="Verfügbare Modelle", value=list_llm_simple[0], type="index")
+                slider_temperature = gr.Slider(0.01, 1.0, value=0.5, step=0.1, label="Temperature")
+                slider_maxtokens = gr.Slider(64, 512, value=256, step=64, label="Max Tokens")
+                slider_topk = gr.Slider(1, 10, value=3, step=1, label="Top-k")
+                qachain_btn = gr.Button("Initialisiere QA-Chatbot")
+                llm_progress = gr.Textbox(value="Nicht initialisiert", show_label=False)
+
+            with gr.Column():
+                gr.Markdown("### Schritt 3: Stelle Fragen an dein Dokument")
+                chatbot = gr.Chatbot(height=400, type="messages")
+                msg = gr.Textbox(placeholder="Frage stellen...")
+                submit_btn = gr.Button("Absenden")
+
+        db_btn.click(initialize_database, [document], [vector_db, db_progress])
+        qachain_btn.click(initialize_LLM, [llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], [qa_chain, llm_progress])
+        msg.submit(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot])
+        submit_btn.click(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot])
+    demo.launch(debug=True)
 
 if __name__ == "__main__":
-    demo()
+    demo()