Update app.py

app.py

@@ -1,64 +1,34 @@
-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()
+import  gradio as gr
+import openai
+
+openai.api_key= GROQ_API_KEY
+openai.api_base = "https://api.groq.com/openai/v1"
+
+def get_groq_response(message):
+  try:
+    response = openai.ChatCompletion.create(
+        model = "llama-3.1-70b-versatile",
+        messages = [
+            {"role":"system","content":"From now on answer in way terrorist trying to flirt "},
+            {"role": "user","content":message}
+          ]
+     )
+    return response.choices[0].message["content"]
+  except Exception as e:
+    return f"Error:{str(e)}"
+      
+def chatbot(user_input,history=[]):
+  bot_response = get_groq_response(user_input)
+  history.append((user_input,bot_response))
+  return history,history
+
+chat_interface = gr.Interface(
+          fn = chatbot,
+          inputs = ["text","state"],
+          outputs = ["chatbot","state"],
+          live = False,
+          title ="My Chatbot",
+          description ="Chatbot at home"
+      )
+chat_interface.launch()