Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import os
-import openai
+from openai import OpenAI
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
@@ -8,8 +8,8 @@ import torch
 DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-# Initialize OpenAI client
-openai.api_key = OPENAI_API_KEY
+# Initialize OpenAI client (for openai>=1.0.0)
+client = OpenAI(api_key=OPENAI_API_KEY)
 
 # Load DeepSeek model
 deepseek_model_id = "deepseek-ai/deepseek-llm-7b-chat"
@@ -32,36 +32,42 @@ def generate_response(prompt, model_provider, temperature, top_p, max_tokens, re
             repetition_penalty=repetition_penalty
         )
         return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
     elif model_provider == "OpenAI":
         try:
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",  # or another model of your choice
+            response = client.chat.completions.create(
+                model="gpt-3.5-turbo",  # or "gpt-4" if you have access
                 messages=[{"role": "user", "content": prompt}],
                 temperature=temperature,
                 top_p=top_p,
                 max_tokens=max_tokens,
                 presence_penalty=repetition_penalty
             )
-            return response.choices[0].message["content"].strip()
+            return response.choices[0].message.content.strip()
         except Exception as e:
             return f"OpenAI API Error: {str(e)}"
+
     else:
         return "Invalid model provider selected."
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🔍 LLM Chat Interface")
+
     with gr.Row():
         model_provider = gr.Dropdown(
             choices=["DeepSeek", "OpenAI"],
             value="DeepSeek",
             label="Select Model Provider"
         )
+
     prompt = gr.Textbox(label="Enter your prompt", lines=4, placeholder="Type your message here...")
+
     with gr.Accordion("Advanced Settings", open=False):
         temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
         top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
         max_tokens = gr.Slider(32, 2048, value=512, step=32, label="Max New Tokens")
         repetition_penalty = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty")
+
     output = gr.Textbox(label="Response")
     submit = gr.Button("Generate")