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import gradio as gr
from huggingface_hub import InferenceClient
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Funktion zum Laden und Indexieren eines Dokuments
def load_and_index_document(file_path: str):
loader = TextLoader(file_path)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_store = FAISS.from_documents(chunks, embeddings)
return vector_store
# Antwortfunktion für den RAG-Chatbot
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, file):
# Dateipfad des hochgeladenen Dokuments
file_path = file.name
# Dokument laden und indexieren
vector_store = load_and_index_document(file_path)
# Historie und Systemnachricht aufbereiten
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Abruf relevanter Abschnitte aus dem Dokument
docs = vector_store.similarity_search(message, k=3) # Abrufen von 3 relevanten Dokumentabschnitten
context = "\n".join([doc.page_content for doc in docs])
# Nachricht an das Modell
full_message = f"{context}\n\nUser: {message}\nAssistant:"
response = ""
try:
# Generierung der Antwort
for message in client.chat_completion(
[{"role": "system", "content": system_message}, {"role": "user", "content": full_message}],
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
except Exception as e:
yield f"An error occurred: {str(e)}"
# Gradio-UI erstellen
def create_gradio_ui():
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(value="You are a helpful assistant.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
gr.File(label="Upload Document") # Datei-Upload
],
live=True
)
return demo
if __name__ == "__main__":
ui = create_gradio_ui()
ui.launch()
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