Update app.py

app.py

@@ -1,18 +1,10 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Use the correct GPT-2 model ("gpttrash" in this case)
+# Initialize the Inference Client for the GPT-2 model (or "gpttrash")
 client = InferenceClient("gpt2")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,  # This will be ignored
-    max_tokens,
-    temperature,
-    top_p,
-):
-    # Remove the system message from the list of messages
+def respond(message, history, max_tokens, temperature, top_p):
     messages = []
 
     # Add the conversation history (user and assistant exchanges)
@@ -27,33 +19,32 @@ def respond(
 
     response = ""
 
-    # Get the model's response
-    for message in client.chat_completion(
+    # Get the model's response using chat completion
+    for response_chunk in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
+        token = response_chunk.choices[0].delta.content
         response += token
         yield response
 
-# Create the Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        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 Gradio Blocks layout for Hugging Face Spaces
+with gr.Blocks() as demo:
+    with gr.Row():
+        user_input = gr.Textbox(label="User Input")
+        history = gr.State()  # Keeps conversation history
+    with gr.Row():
+        max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+        temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+        top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    with gr.Row():
+        output = gr.Textbox(label="Model Output")
+
+    # Set up the chatbot functionality
+    user_input.submit(respond, [user_input, history, max_tokens_slider, temperature_slider, top_p_slider], output)
 
 if __name__ == "__main__":
     demo.launch()