Update app.py

app.py

@@ -1,64 +1,76 @@
 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
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 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
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+# 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])
 
-    messages.append({"role": "user", "content": message})
-
+    # 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)}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
-)
-
+# 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__":
-    demo.launch()
+    ui = create_gradio_ui()
+    ui.launch()