Update app.py

app.py

@@ -1,64 +1,57 @@
 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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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]})
-
+# Initialize the InferenceClient with your chat model.
+client = InferenceClient("amusktweewt/tiny-model-500M-chat-v2")
+
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    """
+    Builds a chat prompt using a simple template:
+      - Optionally includes a system message.
+      - Iterates over conversation history (each exchange as a tuple of (user, assistant)).
+      - Adds the new user message and appends an empty assistant turn.
+    Then it streams the response from the model.
+    """
+    messages = []
+    
+    # Include the system prompt if provided.
+    if system_message:
+        messages.append({"role": "system", "content": system_message})
+    
+    # Append conversation history.
+    if history:
+        for user_msg, bot_msg in history:
+            messages.append({"role": "user", "content": user_msg})
+            messages.append({"role": "assistant", "content": bot_msg})
+    
+    # Add the new user message and an empty assistant response
+    # (this mimics your template where the assistant turn is empty to be filled).
     messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
+    messages.append({"role": "assistant", "content": ""})
+    
+    response_text = ""
+    # Stream the response token-by-token.
+    for resp 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
+        token = resp.choices[0].delta.content
+        response_text += token
+        yield response_text
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create a Gradio 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)"),
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()