Update app.py

app.py

@@ -3,13 +3,17 @@ import random
 import gradio as gr
 from groq import Groq
 
+# Initialize the Groq client with your API key
 client = Groq(
-    api_key = os.environ.get("Groq_Api_Key")
+    api_key=os.environ.get("Groq_Api_Key")
 )
-    
+
 def create_history_messages(history):
-    history_messages = [{"role": "user", "content": m[0]} for m in history]
-    history_messages.extend([{"role": "assistant", "content": m[1]} for m in history])
+    # Interleave user and assistant messages in the order they occurred
+    history_messages = []
+    for user_msg, assistant_msg in history:
+        history_messages.append({"role": "user", "content": user_msg})
+        history_messages.append({"role": "assistant", "content": assistant_msg})
     return history_messages
 
 def generate_response(prompt, history, model, temperature, max_tokens, top_p, seed):
@@ -38,18 +42,51 @@ def generate_response(prompt, history, model, temperature, max_tokens, top_p, se
             response += delta_content
             yield response
 
-    return response
-
 additional_inputs = [
-    gr.Dropdown(choices=["llama-3.1-405b-reasoning", "llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
-    gr.Slider(minimum=1, maximum=131000, step=1, value=8100, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
-    gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
+    gr.Dropdown(
+        choices=[
+            "llama-3.1-405b-reasoning",
+            "llama-3.1-70b-versatile",
+            "llama-3.1-8b-instant",
+            "llama3-70b-8192",
+            "llama3-8b-8192",
+            "mixtral-8x7b-32768",
+            "gemma2-9b-it",
+            "gemma-7b-it"
+        ],
+        value="llama-3.1-70b-versatile",
+        label="Model"
+    ),
+    gr.Slider(
+        minimum=0.0, maximum=1.0, step=0.01, value=0.5,
+        label="Temperature",
+        info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."
+    ),
+    gr.Slider(
+        minimum=1, maximum=131000, step=1, value=8100,
+        label="Max Tokens",
+        info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."
+    ),
+    gr.Slider(
+        minimum=0.0, maximum=1.0, step=0.01, value=0.5,
+        label="Top P",
+        info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."
+    ),
+    gr.Number(
+        precision=0, value=0, label="Seed",
+        info="A starting point to initiate generation, use 0 for random"
+    )
 ]
 
 gr.ChatInterface(
-    fn=generate_response, theme="Nymbo/Alyx_Theme",
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    fn=generate_response,
+    theme="Nymbo/Alyx_Theme",
+    chatbot=gr.Chatbot(
+        show_label=False,
+        show_share_button=False,
+        show_copy_button=True,
+        likeable=True,
+        layout="panel"
+    ),
     additional_inputs=additional_inputs,
 ).launch()