Update app.py

Added ask_question feature w/ OpenAI text-davinci-002

app.py

@@ -1,3 +1,4 @@
+import os
 import spaces
 import torch
 import gradio as gr
@@ -23,8 +24,37 @@ def audio_transcribe(inputs, task):
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    ask_question.visible = True
+    
     return  text
 
+@spaces.GPU
+def respond_to_question(transcript, question):
+    # Optionally, use OpenAI API to generate a response to the user's question
+    # based on the transcript
+    response = ""
+    # Replace this with your OpenAI API key
+    openai.api_key = os.environ["OPENAI_API_KEY"]
+    response = openai.Completion.create(
+        engine="text-davinci-002",
+        prompt=f"Transcript: {transcript}\n\nUser: {question}\n\nAI:",
+        temperature=0.7,
+        max_tokens=60,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0
+    ).choices[0].text
+    return response
+    
+def ask_question_callback():
+    if ask_question.value:
+        response = respond_to_question(transcript_output.value, ask_question.value)
+        response_output.visible = True
+        response_output.value = response
+    else:
+        response_output.value = "No question asked"
+
+
 with gr.Blocks() as transcriberUI:
     gr.Markdown(
     """
@@ -34,6 +64,12 @@ with gr.Blocks() as transcriberUI:
     """)
     inp = gr.File(label="Arquivo de Audio", show_label=True, type="filepath", file_count="single", file_types=["mp3"])
     transcribe = gr.Textbox(label="Transcricao", show_label=True, show_copy_button=True)
+    ask_question = gr.Textbox(label="Ask a question", visible=False)
+    response_output = gr.Textbox(label="Response", visible=False)
+    submit_question = gr.Button("Submit question")
+    
     inp.upload(audio_transcribe, inp, transcribe)
+    submit_question.click(ask_question_callback, outputs=[response_output], inputs=[transcribe, ask_question])
+
 
 transcriberUI.queue().launch()