Update app.py

app.py

@@ -1,32 +1,33 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-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="HuggingFaceH4/zephyr-7b-beta")
 
+
+system_message = ("This is MUSK-1, developed by a 14 year old AI engineer Arjun Singh at Elieon.")
+# Function to generate responses using the LLM
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,
     max_tokens,
     temperature,
     top_p,
 ):
+    # Construct the message history for the API request
     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})
 
     messages.append({"role": "user", "content": message})
 
     response = ""
 
+    # Stream the response tokens from the model
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,29 +36,27 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
+# Create a Gradio ChatInterface with custom styling
+custom_css = """
+.css-1rw10a3 {
+    height: 500px; /* Adjust height as needed */
+    overflow-y: scroll; /* Add scrollbar if content exceeds height */
+}
 """
-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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
+    css=custom_css,  # Apply custom CSS styling
 )
 
-
+# Launch the Gradio app
 if __name__ == "__main__":
     demo.launch()