Update app.py

app.py

@@ -3,10 +3,10 @@ import os
 import gradio as gr
 import threading
 import time
-from groq import Groq
+from gradio_client import Client
 
-# Initialize Groq client
-client = Groq()
+# Initialize Gradio client
+client = Client("Nymbo/Llama-3.1-405B-Instruct")
 
 # Constants
 MAX_SIZE = 1.1 * 1024 * 1024 * 1024  # 1.1GB in bytes
@@ -37,52 +37,28 @@ def generate_and_save_data():
     while True:
         try:
             # Generate a prompt
-            completion = client.chat.completions.create(
-                model="llama3-groq-70b-8192-tool-use-preview",
-                messages=[
-                    {
-                        "role": "user",
-                        "content": "give me a single prompt to prompt an ai model, simulating what users could want from you. ensure that it is diverse and high quality. for each, choose a random writing style (though it has to be a common one), random length and random clarity of the prompt. ensure that it is a single prompt, and just the prompt itself, nothing else. eg, don't close the prompt in quotation marks or say Here is a single prompt that meets your requirements or anything similar to that"
-                    }
-                ],
-                temperature=1,
+            prompt_result = client.predict(
+                message="give me a single prompt to prompt an ai model, simulating what users could want from you. ensure that it is diverse and high quality. for each, choose a random writing style (though it has to be a common one), random length and random clarity of the prompt. ensure that it is a single prompt, and just the prompt itself, nothing else. eg, don't close the prompt in quotation marks or say Here is a single prompt that meets your requirements or anything similar to that",
+                system_message="",
                 max_tokens=1024,
+                temperature=1,
                 top_p=1,
-                stream=True,
-                stop=None,
+                api_name="/chat"
             )
-
-            prompt = ""
-            prompt_tokens = 0
-            for chunk in completion:
-                content = chunk.choices[0].delta.content
-                if content:
-                    prompt += content
-                    prompt_tokens += len(content.split())
+            prompt = prompt_result['message']
+            prompt_tokens = len(prompt.split())
 
             # Use the generated prompt to query the model again
-            second_completion = client.chat.completions.create(
-                model="llama3-groq-70b-8192-tool-use-preview",
-                messages=[
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
-                ],
-                temperature=1,
+            response_result = client.predict(
+                message=prompt,
+                system_message="",
                 max_tokens=5000,
+                temperature=1,
                 top_p=1,
-                stream=True,
-                stop=None,
+                api_name="/chat"
             )
-
-            response = ""
-            response_tokens = 0
-            for chunk in second_completion:
-                content = chunk.choices[0].delta.content
-                if content:
-                    response += content
-                    response_tokens += len(content.split())
+            response = response_result['message']
+            response_tokens = len(response.split())
 
             # Update the combined token count
             combined_tokens += (prompt_tokens + response_tokens)
@@ -100,12 +76,10 @@ def generate_and_save_data():
                 current_file = os.path.join(DATA_DIRECTORY, f'data{file_index}.csv')
                 file_paths.append(current_file)
                 # Create the new file with headers
-                with open(current_file, 'w') as f:
-                    data.to_csv(f, header=True, index=False)
+                data.to_csv(current_file, index=False)
             else:
                 # Append data to the current file
-                with open(current_file, 'a') as f:
-                    data.to_csv(f, header=False, index=False)
+                data.to_csv(current_file, mode='a', header=False, index=False)
 
             # Wait for the next update interval
             time.sleep(UPDATE_INTERVAL)