Update app.py

app.py

@@ -3,6 +3,15 @@ import gradio as gr
 import numpy as np
 from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig,BitsAndBytesConfig
 import torch
+from dotenv import load_dotenv
+import os
+import openai
+
+# GPT config in .env file
+load_dotenv()
+key = os.environ.get('OPENAI_API_KEY')
+openai.api_key = key
+
 
 Medical_finetunned_model = "truongghieu/deci-finetuned_Prj2"
 answer_text = "This is an answer"
@@ -54,9 +63,18 @@ def recognize_speech(audio_data):
         return f"Could not request results from Google Speech Recognition service; {e}"
 
 
+def pgt_generate(text):
+    response = openai.Completion.create(
+        model = "gpt-3.5-turbo",
+        messages = text,
+        temperature = 0.2,
+    )
+    response_msg = response.choices[0].message.content
+    return response_msg
+
+
 with gr.Blocks() as demo:
     with gr.Row():
-        inp_text = gr.Textbox(label="Input Text")
         inp = gr.Audio(type="numpy")
         out_text_predict = gr.Textbox(label="Recognized Speech")
         button = gr.Button("Recognize Speech" , size="lg")
@@ -69,9 +87,13 @@ with gr.Blocks() as demo:
         with gr.Row():
             temperature_slider = gr.Slider(minimum=0, maximum=1, step=0.1, label="temperature",value=0.5)
             repetition_penalty_slider = gr.Slider(minimum=0, maximum=2, step=0.1, label="repetition penalty",value=1.0)
-            max_new_tokens_slider = gr.Slider(minimum=0, maximum=100, step=1, label="max new tokens",value=30)
+            max_new_tokens_slider = gr.Slider(minimum=0, maximum=200, step=1, label="max new tokens",value=30)
     with gr.Row():
         out_answer = gr.Textbox(label="Answer")
         button_answer = gr.Button("Answer")
         button_answer.click(generate_text, [out_text_predict, penalty_alpha_slider, do_sample_checkbox, top_k_slider, temperature_slider, repetition_penalty_slider, max_new_tokens_slider], out_answer)
+    with gr.Row():
+        gpt_output = gr.Textbox(label="GPT-3.5 Turbo Output")
+        button_gpt = gr.Button("GPT-3.5 Answer")
+        button_gpt.click(pgt_generate,out_text_predict,gpt_output)
 demo.launch()