Update app.py

app.py

@@ -2,6 +2,7 @@ import os, sys, json
 import gradio as gr
 import openai
 from openai import OpenAI
+import time
 
 from langchain.chains import LLMChain, RetrievalQA
 from langchain.chat_models import ChatOpenAI
@@ -78,6 +79,16 @@ MODEL_NAME  = "gpt-3.5-turbo-16k"
 #################################################
 #Funktionen zur Verarbeitung
 ################################################
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+
+def add_file(history, file):
+    history = history + [((file.name,), None)]
+    return history
+
+    
 #die Inhalte splitten, um in Vektordatenbank entsprechend zu laden als Splits
 def document_loading_splitting():
     global splittet
@@ -154,7 +165,7 @@ def rag_chain(llm, prompt, db):
 
 ###################################################
 #Funktion von Gradio aus, die den dort eingegebenen Prompt annimmt und weiterverarbeitet
-def invoke (prompt, chatbot, openai_api_key, rag_option, temperature=0.9, max_new_tokens=512, top_p=0.6, repetition_penalty=1.3,):
+def invoke (prompt, history, openai_api_key, rag_option, temperature=0.9, max_new_tokens=512, top_p=0.6, repetition_penalty=1.3,):
     global splittet
     
     if (openai_api_key == "" or openai_api_key == "sk-"):
@@ -187,6 +198,14 @@ def invoke (prompt, chatbot, openai_api_key, rag_option, temperature=0.9, max_ne
             result = llm_chain(llm, prompt)
     except Exception as e:
         raise gr.Error(e)
+
+    #Antwort als Stream ausgeben... und in History speichern
+    history[-1][1] = ""
+    for character in result:
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
+    
     return result
 
 ################################################
@@ -250,6 +269,52 @@ additional_inputs = [
     gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Strafe für wiederholte Tokens")
 ]
 
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        bubble_full_width=False,
+        avatar_images=(None, (os.path.join(os.path.dirname(__file__), "avatar.png"))),
+    )
+
+    with gr.Row():
+        txt = gr.Textbox(
+            scale=4,
+            show_label=False,
+            placeholder="Gibt Text ein und drücke Enter oder lade ein Bild hoch.",
+            container=False,
+        )
+        btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
+
+    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+        invoke, chatbot, chatbot, api_name="bot_response"
+    )
+    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
+        invoke, chatbot, chatbot
+    )
+
+demo.queue()
+demo.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+"""
+
 chatbot_stream = gr.Chatbot(avatar_images=(
     "https://drive.google.com/uc?id=18xKoNOHN15H_qmGhK__VKnGjKjirrquW",
     "https://drive.google.com/uc?id=1tfELAQW_VbPCy6QTRbexRlwAEYo8rSSv"
@@ -277,4 +342,5 @@ with gr.Blocks() as demo:
            gr.Radio(["Off", "Chroma"], label="Retrieval Augmented Generation", value = "Off")
            gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1)
         
-demo.queue( max_size=100).launch(debug=True)
+demo.queue( max_size=100).launch(debug=True)
+"""