Update app.py

app.py

@@ -20,6 +20,8 @@ from langchain.prompts import PromptTemplate
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from chromadb.errors import InvalidDimensionException
+from utils import *
+from beschreibungen import *
 
 #from langchain.vectorstores import MongoDBAtlasVectorSearch
 #from pymongo import MongoClient
@@ -117,15 +119,28 @@ os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
 #################################################
 #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
 
+##############################################
+#History - die Frage oder das File eintragen...
+def add_text(history, prompt):
+    history = history + [(prompt, None)]
+    return history, prompt, "" #gr.Textbox(value="", interactive=False)
+    
+def add_file(history, file, prompt):
+    if (prompt == ""):
+        history = history + [((file.name,), None)]
+    else:
+        history = history + [((file.name,), None), (prompt, None)]
+    return history, prompt, ""
+
+def transfer_input(inputs):
+    textbox = reset_textbox()
+    return (
+        inputs,
+        gr.update(value=""),
+        gr.Button.update(visible=True),
+    )
+##################################################
 # Funktion, um für einen best. File-typ ein directory-loader zu definieren
 def create_directory_loader(file_type, directory_path):
     #verscheidene Dokument loaders:
@@ -300,8 +315,8 @@ def chatbot_response(messages):
             print("Bild.............................")
     return responses
 
-    
-def invoke (prompt, history, rag_option, model_option, openai_api_key,  temperature=0.5, max_new_tokens=4048, top_p=0.6, repetition_penalty=1.3,):
+  
+def invoke (prompt, history, rag_option, model_option, openai_api_key, k=3, top_p=0.6, temperature=0.5, max_new_tokens=4048, max_context_length_tokens=2048, repetition_penalty=1.3,):
     global splittet
     print(splittet)
     #Prompt an history anhängen und einen Text daraus machen
@@ -361,10 +376,26 @@ def invoke (prompt, history, rag_option, model_option, openai_api_key,  temperat
     except Exception as e:
         raise gr.Error(e)
 
+    """
     #Antwort als Stream ausgeben... 
     for i in range(len(result)):
         time.sleep(0.05)
         yield  result[: i+1]
+    """
+
+    #Antwort als Stream ausgeben... 
+    history[-1][1] = ""
+    for character in result:
+        history[-1][1] += character
+        time.sleep(0.03)
+        yield history, "Generating"
+        if shared_state.interrupted:
+            shared_state.recover()
+            try:
+                yield history, "Stop: Success"
+                return
+            except:
+                pass
     
 ################################################
 #GUI
@@ -375,21 +406,137 @@ def invoke (prompt, history, rag_option, model_option, openai_api_key,  temperat
 description = """<strong>Information:</strong> Hier wird ein <strong>Large Language Model (LLM)</strong> mit 
                  <strong>Retrieval Augmented Generation (RAG)</strong> auf <strong>externen Daten</strong> verwendet.\n\n
                  """
-css = """.toast-wrap { display: none !important } """
-examples=[['Was ist ChtGPT-4?'],['schreibe ein Python Programm, dass die GPT-4 API aufruft.']]
+#css = """.toast-wrap { display: none !important } """
+#examples=[['Was ist ChtGPT-4?'],['schreibe ein Python Programm, dass die GPT-4 API aufruft.']]
 
 def vote(data: gr.LikeData):
     if data.liked: print("You upvoted this response: " + data.value)
     else: print("You downvoted this response: " + data.value)
 
-def read_image(image, size=512):
-        return np.array(Image.fromarray(image).resize((size, size)))
 
-def add_file(history, file):
-    history = history + [((file.name,), None)]
-    return history
+print ("Start GUI")
+with open("custom.css", "r", encoding="utf-8") as f:
+    customCSS = f.read()
     
+with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
+    history = gr.State([])
+    user_question = gr.State("")
+    with gr.Row():
+        gr.HTML("LI Chatot")
+        status_display = gr.Markdown("Success", elem_id="status_display")
+    gr.Markdown(description_top)
+    with gr.Row():
+        with gr.Column(scale=5):
+            with gr.Row():
+                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot")
+            with gr.Row():
+                with gr.Column(scale=12):
+                    user_input = gr.Textbox(
+                        show_label=False, placeholder="Gib hier deinen Prompt ein...",
+                        container=False
+                    )
+                with gr.Column(min_width=70, scale=1):
+                    submitBtn = gr.Button("Senden")
+                with gr.Column(min_width=70, scale=1):
+                    cancelBtn = gr.Button("Stop")
+            with gr.Row():
+                emptyBtn = gr.ClearButton( [user_input, chatbot], value="🧹 Neue Session")  
+                btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
+
+        with gr.Column():
+            with gr.Column(min_width=50, scale=1):
+                with gr.Tab(label="Parameter Einstellung"):
+                    gr.Markdown("# Parameters")
+                    rag_option = gr.Radio(["Aus", "An"], label="RAG - LI Erweiterungen", value = "Aus")
+                    model_option = gr.Radio(["HF1", "HF2"], label="Modellauswahl", value = "HF1")
+                    
+                    top_p = gr.Slider(
+                        minimum=-0,
+                        maximum=1.0,
+                        value=0.95,
+                        step=0.05,
+                        interactive=True,
+                        label="Top-p",
+                    )
+                    temperature = gr.Slider(
+                        minimum=0.1,
+                        maximum=2.0,
+                        value=1,
+                        step=0.1,
+                        interactive=True,
+                        label="Temperature",
+                    )
+                    max_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=512,
+                        value=512,
+                        step=8,
+                        interactive=True,
+                        label="Max Generation Tokens",
+                    )
+                    max_context_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=4096,
+                        value=2048,
+                        step=128,
+                        interactive=True,
+                        label="Max History Tokens",
+                    )
+                    repetition_penalty=gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Strafe für wiederholte Tokens", visible=True)
+                    anzahl_docs = gr.Slider(label="Anzahl Dokumente", value=3, minimum=1, maximum=10, step=1, interactive=True, info="wie viele Dokumententeile aus dem Vektorstore an den prompt gehängt werden", visible=True)
+                    openai_key = gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1)
+    gr.Markdown(description)
+
+    #Argumente für generate Funktion als Input
+    predict_args = dict(
+        fn=generate,
+        inputs=[
+            user_question,
+            chatbot,
+            #history,
+            rag_option,
+            model_option,
+            openai_key,
+            anzahl_docs,
+            top_p,
+            temperature,
+            max_length_tokens,
+            max_context_length_tokens,
+            repetition_penalty
+        ],
+        outputs=[ chatbot, status_display], #[ chatbot, history, status_display],
+        show_progress=True,
+    )
+
+
+    reset_args = dict(
+        fn=reset_textbox, inputs=[], outputs=[user_input, status_display]
+    )
+
+    # Chatbot
+    transfer_input_args_text = dict(
+        fn=add_text, inputs=[chatbot, user_input], outputs=[chatbot, user_question, user_input], show_progress=True
+    )
+    transfer_input_args_file = dict(
+        fn=add_file, inputs=[chatbot, btn, user_input], outputs=[chatbot, user_question, user_input], show_progress=True
+    )
+
+    predict_event1 = user_input.submit(**transfer_input_args_text, queue=False,).then(**predict_args)
+    predict_event3 = btn.upload(**transfer_input_args_file,queue=False,).then(**predict_args)
+    predict_event2 = submitBtn.click(**transfer_input_args_text, queue=False,).then(**predict_args)
+
+    cancelBtn.click(
+        cancels=[predict_event1,predict_event2, predict_event3 ]
+    )
+demo.title = "LI-ChatBot"
+
+demo.queue().launch(debug=True)
+
 
+
+
+
+"""
 additional_inputs = [
     #gr.Radio(["Off", "Chroma", "MongoDB"], label="Retrieval Augmented Generation", value = "Off"),
     gr.Radio(["Aus", "An"], label="RAG - LI Erweiterungen", value = "Aus"),
@@ -401,10 +548,6 @@ 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", visible=True)
 ]
 
-
-
-
-
 with gr.Blocks() as demo:
     reference_image  = gr.Image(label="Reference Image")
 
@@ -424,7 +567,7 @@ with gr.Blocks() as demo:
                 ) 
     
     gr.HTML(
-        """
+        
         <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
         <a href="https://github.com/magic-research/magic-animate" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
         </a>
@@ -435,7 +578,7 @@ with gr.Blocks() as demo:
             </div>
         </div>
         </div>
-        """)
+        )
     
     with gr.Row():
         prompt = gr.Textbox(
@@ -453,33 +596,5 @@ with gr.Blocks() as demo:
     #chatbot_stream.like(print_like_dislike, None, None)
 
 
-    """   
-    with gr.Tab("Chatbot"):
-        iface = gr.Interface(
-                fn=chatbot_response,
-                inputs=[reference_image, chat_interface_stream],
-                outputs=chat_interface_stream,
-                title="Chatbot mit Bildeingabe",
-                description="Laden Sie ein Bild hoch oder interagieren Sie über den Chat."
-        )
-
-        iface.launch()
-
-        
-        with gr.Row():
-            chatbot_stream.like(vote, None, None)
-            chat_interface_stream.queue().launch() 
-        #with gr.Row():
-            #reference_image.queue().launch() 
-
-
-    # when `first_frame` is updated
-    reference_image.upload(
-        read_image,
-        reference_image,
-        reference_image,
-        queue=False
-    )
-    """
-
-demo.queue().launch() 
+demo.queue().launch() 
+"""