Update app.py

app.py

@@ -1,63 +1,79 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer
 
-"""
-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")
+client = InferenceClient(model="AriakimTaiyo/gpt2-chat")
 
+# Load the tokenizer explicitly
+tokenizer = AutoTokenizer.from_pretrained("AriakimTaiyo/gpt2-chat")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, history, system_message):
+    # Prepare messages, starting with the system message
     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]})
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
 
+    # Add the latest user message
     messages.append({"role": "user", "content": message})
 
     response = ""
+    try:
+        # Generate responses
+        for message in client.chat_completion(
+            messages=messages,
+            max_tokens=256,
+            stream=True,
+            temperature=0.7,
+            top_p=0.95,
+        ):
+            token = message.choices[0].delta.get('content', '')
+            response += token
+            yield response
+    except Exception as e:
+        # Return the error message in case of an exception
+        yield f"Hata: {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)",
-        ),
-    ],
-)
+# Create the Gradio interface
+with gr.Blocks(theme=gr.Theme.from_hub('HaleyCH/HaleyCH_Theme')) as demo:
+    system_message = gr.HTML("""
+    <h1 style="color: #fff; text-shadow: 0 0 5px #fff, 0 0 10px #fff, 0 0 15px #fff, 0 0 10px #0000ff, 0 0 15px #0000ff; text-align: center;">
+    SIMULACRA GPT-2
+    </h1>
+    <p>🤖 Welcome to Simulacra user! See our account for more information.</p>
+    """)
 
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Mesajınızı yazın")
+    
+    # Place buttons side by side
+    with gr.Row():
+        clear = gr.Button("Temizle")
+        submit = gr.Button("Gönder")
 
+    def user_input(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot_response(history):
+        last_message = history[-1][0]
+        response_gen = respond(
+            message=last_message,
+            history=history[:-1],
+            system_message=system_message.value,
+        )
+        for response in response_gen:
+            history[-1][1] = response
+            yield history
+
+    msg.submit(user_input, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+    submit.click(lambda: msg.submit(), None, chatbot, queue=False)  # Send the message when the "Gönder" button is clicked
+
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)