Update app.py

app.py

@@ -1,76 +1,87 @@
 import gradio as gr
 import os
-from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFaceEndpoint
+from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
+from langchain_community.vectorstores import FAISS
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 
-# Der Token wird sicher aus den Space Secrets abgerufen
-api_token = os.getenv("HF_TOKEN")  # Kein direkter API-Token im Code sichtbar
+# API-Token
+api_token = os.getenv("HF_TOKEN")
 
-# Kostenlose LLM-Optionen (Free-Version)
+# LLM-Optionen
 list_llm = ["google/flan-t5-small", "google/flan-t5-base"]
 
-# **Dokument laden und aufteilen**
+# Dokumente laden und aufteilen
 def load_doc(list_file_path):
+    if not list_file_path:
+        return [], "Fehler: Keine Dokumente gefunden!"
     loaders = [PyPDFLoader(x) for x in list_file_path]
-    pages = []
+    documents = []
     for loader in loaders:
-        pages.extend(loader.load())
+        documents.extend(loader.load())
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
-    return text_splitter.split_documents(pages)
+    return text_splitter.split_documents(documents)
 
-# **Vektor-Datenbank erstellen**
+# Vektor-Datenbank erstellen
 def create_db(splits):
-    embeddings = HuggingFaceEmbeddings()
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     return FAISS.from_documents(splits, embeddings)
 
-# **LLM-Kette initialisieren**
+# Datenbank initialisieren
+def initialize_database(list_file_obj):
+    if not list_file_obj:
+        return None, "Fehler: Keine Dateien hochgeladen!"
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    doc_splits = load_doc(list_file_path)
+    vector_db = create_db(doc_splits)
+    return vector_db, "Datenbank erfolgreich erstellt!"
+
+# LLM-Kette initialisieren
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    if vector_db is None:
+        return None, "Fehler: Keine Vektordatenbank verfügbar."
     llm = HuggingFaceEndpoint(
         repo_id=llm_model,
-        huggingfacehub_api_token=api_token,  # Holt den API-Token aus den Space Secrets
+        huggingfacehub_api_token=api_token,
         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(
+    return ConversationalRetrievalChain.from_llm(
         llm, retriever=retriever, chain_type="stuff", memory=memory, return_source_documents=True
     )
-    return qa_chain
 
-# **Datenbank initialisieren**
-def initialize_database(list_file_obj):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    doc_splits = load_doc(list_file_path)
-    vector_db = create_db(doc_splits)
-    return vector_db, "Datenbank erfolgreich erstellt!"
-
-# **LLM initialisieren**
+# LLM initialisieren
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
+    if vector_db is None:
+        return None, "Datenbank wurde nicht erstellt!"
     llm_name = list_llm[llm_option]
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
     return qa_chain, "QA-Kette initialisiert. Chatbot ist bereit!"
 
-# **Konversation**
+# Konversation
 def conversation(qa_chain, message, history):
+    if qa_chain is None:
+        return None, [{"role": "system", "content": "Die QA-Kette wurde nicht initialisiert."}], history
+    if not message.strip():
+        return qa_chain, [{"role": "system", "content": "Bitte eine Frage eingeben!"}], history
     response = qa_chain.invoke({"question": message, "chat_history": history})
-    response_answer = response["answer"]
-    return qa_chain, response_answer, history + [(message, response_answer)]
+    response_text = response.get("answer", "Keine Antwort verfügbar.")
+    sources = [doc.metadata["source"] for doc in response.get("source_documents", [])]
+    formatted_response = history + [{"role": "assistant", "content": response_text}]
+    return qa_chain, formatted_response, formatted_response
 
-# **Demo erstellen**
+# Demo erstellen
 def demo():
     with gr.Blocks() as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
-
-        gr.Markdown("<center><h1>PDF-Chatbot mit kostenfreien Hugging Face-Modellen</h1></center>")
-        document = gr.Files(label="Lade PDF-Dokumente hoch", file_types=[".pdf"])
+        gr.Markdown("<center><h1>PDF-Chatbot mit kostenlosen Modellen</h1></center>")
+        document = gr.Files(label="PDF-Dokument hochladen")
         db_btn = gr.Button("Erstelle Vektordatenbank")
         llm_btn = gr.Radio(["Flan-T5 Small", "Flan-T5 Base"], label="Verfügbare LLMs", value="Flan-T5 Small", type="index")
         slider_temperature = gr.Slider(0.01, 1.0, 0.5, label="Temperature")
@@ -85,7 +96,7 @@ def demo():
         qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain])
         submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, chatbot, chatbot])
 
-    demo.launch()
+    demo.launch(debug=True)
 
 if __name__ == "__main__":
     demo()