Delete app.py

app.py

@@ -1,76 +0,0 @@
-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()