Update app.py

app.py

@@ -1,63 +1,39 @@
 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]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from groq import Groq
+client = Groq(api_key = key)
+
+def chat(message, history):
+
+
+  chat_completion = client.chat.completions.create(
+      #
+      # Required parameters
+      #
+      messages=[
+          {
+              "role": "system",
+              "content": "you are a helpful assistant."
+          },
+          # Set a user message for the assistant to respond to.
+          {
+              "role": "user",
+              "content": message,
+          }
+      ],
+
+      # The language model which will generate the completion.
+      model="llama3-8b-8192",
+      temperature=0.5,
+
+      max_tokens=256,
+
+      top_p=1,
+
+      stop=None,
+      stream=False,
+  )
+
+  return chat_completion.choices[0].message.content
+
+demo = gr.ChatInterface(fn=chat, title="Open Source chatbot")
+demo.launch(debug= True)