Update app.py

app.py

@@ -1,161 +1,124 @@
 from huggingface_hub import InferenceClient
 import os
+import gradio as gr
+import random
+import time
 
 # HF Inference Endpoints parameter
-endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
+endpoint_url = "https://qrh4fv8e7x3fw9w3.us-east-1.aws.endpoints.huggingface.cloud" #\
+'''
+-X POST \
+-d '{"inputs":"My name is Teven and I am"}' \
+-H "Authorization: Bearer <hf_token>" \
+-H "Content-Type: application/json""
+'''
 
 hf_token = os.getenv("TOKEN_HF")
 
 # Streaming Client
 client = InferenceClient(endpoint_url, token=hf_token)
 
-# generation parameter
-gen_kwargs = dict(
-    max_new_tokens=512,
-    top_k=30,
-    top_p=0.9,
-    temperature=0.2,
-    repetition_penalty=1.02,
-    stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
-)
-# prompt
-prompt = "What can you do in Nuremberg, Germany? Give me 3 Tips"
-
-stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
-
-# yield each generated token
-for r in stream:
-    # skip special tokens
-    if r.token.special:
-        continue
-    # stop if we encounter a stop sequence
-    if r.token.text in gen_kwargs["stop_sequences"]:
-        break
-    # yield the generated token
-    print(r.token.text, end = "")
-    # yield r.token.text
 
 
-#######################################################################
-#Darstellung mit Gradio
-
-with open("custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-    
-with gr.Blocks(theme=small_and_beautiful_theme) as demo:
-    history = gr.State([])
-    user_question = gr.State("")
-    gr.Markdown("KIs am LI - wähle aus, was du bzgl. KI-Bots ausprobieren möchtest!")
-    with gr.Tabs():
-        with gr.TabItem("LI-Chat"):
-            with gr.Row():
-                gr.HTML(title)
-                status_display = gr.Markdown("Erfolg", elem_id="status_display")
-            gr.Markdown(description_top)
-            with gr.Row(scale=1).style(equal_height=True):
-                with gr.Column(scale=5):
-                    with gr.Row(scale=1):
-                        chatbotGr = gr.Chatbot(elem_id="LI_chatbot").style(height="100%")
-                    with gr.Row(scale=1):
-                        with gr.Column(scale=12):
-                            user_input = gr.Textbox(
-                                show_label=False, placeholder="Gib deinen Text / Frage ein."
-                            ).style(container=False)
-                        with gr.Column(min_width=100, scale=1):
-                            submitBtn = gr.Button("Absenden")
-                        with gr.Column(min_width=100, scale=1):
-                            cancelBtn = gr.Button("Stoppen")
-                    with gr.Row(scale=1):
-                        emptyBtn = gr.Button(
-                            "🧹 Neuer Chat",
-                        )
-                with gr.Column():
-                    with gr.Column(min_width=50, scale=1):
-                        with gr.Tab(label="Nur zum Testen:"):
-                            gr.Markdown("# Parameter")
-                            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",
-                            )
-            gr.Markdown(description)
-
-        with gr.TabItem("Übersetzungen"):
-            with gr.Row():
-                    gr.Textbox(
-                                show_label=False, placeholder="Ist noch in Arbeit..."
-                            ).style(container=False)
-        with gr.TabItem("Code-Generierungen"):
-            with gr.Row():
-                    gr.Textbox(
-                                show_label=False, placeholder="Ist noch in Arbeit..."
-                            ).style(container=False)
-    
-    predict_args = dict(
-        fn=predict,
-        inputs=[
-            user_question,
+########################################################################
+#Chat KI nutzen, um Text zu generieren...
+def predict(text,
             chatbotGr,
             history,
             top_p,
             temperature,
             max_length_tokens,
-            max_context_length_tokens,
-        ],
-        outputs=[chatbotGr, history, status_display],
-        show_progress=True,
+            max_context_length_tokens,):
+    if text=="":
+        yield chatbotGr,history,"Empty context."
+        return 
+    try:
+        client
+    except:
+        yield [[text,"No Model Found"]],[],"No Endpoint Found"
+        return
+
+    # generation parameter
+    gen_kwargs = dict(
+        max_new_tokens=max_length_tokens,
+        top_k=30,
+        top_p=top_p,
+        temperature=temperature,
+        repetition_penalty=1.02,
+        stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
     )
+    # prompt
+    prompt = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens)
+
+                   
+        
+        
+        
+        
+        
+        
+        
         
-    #neuer Chat
-    reset_args = dict(
-        #fn=reset_chat, inputs=[], outputs=[user_input, status_display]
-        fn=reset_textbox, inputs=[], outputs=[user_input, status_display]
-    )
-            
-    # Chatbot
-    transfer_input_args = dict(
-        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn], show_progress=True
-    )
         
-    #Listener auf Start-Click auf Button oder Return
-    predict_event1 = user_input.submit(**transfer_input_args).then(**predict_args)
-    predict_event2 = submitBtn.click(**transfer_input_args).then(**predict_args)
         
-    #Listener, Wenn reset...
-    emptyBtn.click(
-        reset_state,
-        outputs=[chatbotGr, history, status_display],
-        show_progress=True,
-    )
-    emptyBtn.click(**reset_args)
 
-demo.title = "LI Chat"
-#demo.queue(concurrency_count=1).launch(share=True) 
-demo.queue(concurrency_count=1).launch(debug=True)
+
+
+
+    
+
+#######################################################################
+#Darstellung mit Gradio
+
+with open("custom.css", "r", encoding="utf-8") as f:
+    customCSS = f.read()
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        # generation parameter
+        gen_kwargs = dict(
+            max_new_tokens=512,
+            top_k=30,
+            top_p=0.9,
+            temperature=0.2,
+            repetition_penalty=1.02,
+            stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
+        )
+        prompt = generate_prompt_with_history(text,history,max_length=max_context_length_tokens)
+        stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
+
+        history[-1][1] = ""
+        for character in stream:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history
+
+        '''
+        # yield each generated token
+        for r in stream:
+            # skip special tokens
+            if r.token.special:
+                continue
+            # stop if we encounter a stop sequence
+            if r.token.text in gen_kwargs["stop_sequences"]:
+                break
+            # yield the generated token
+            #print(r.token.text, end = "")
+            yield r.token.text 
+        '''
+
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+    
+demo.queue()
+demo.launch()