Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import pipeline
+from openai import OpenAI
+import numpy as np
+
+transcriber_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+
+
+def voice_commands(api_key, audio):
+    if api_key == '':
+        output= gr.Textbox("*** Please provide API Key ***")
+    else:
+        try:
+            sr, y = audio
+            y = y.astype(np.float32)
+            y /= np.max(np.abs(y))
+            prompt = transcriber_pipe({"sampling_rate": sr, "raw": y})["text"]
+            client = OpenAI(api_key=api_key)
+            messages = [{"role": "user", "content": prompt}] 
+            model = "gpt-3.5-turbo-1106"
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=0
+            )
+            output = response.choices[0].message.content
+        except :
+           output = gr.Textbox(" *** Please check your API-Key and try again ***")
+    return output
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Voice Commands to prompt Chatgpt using Whisper.
+        # OpenAI's Whisper is used to translate voice to text, to prompt ChatGPT.
+        <img src = "https://upload.wikimedia.org/wikipedia/commons/4/4d/OpenAI_Logo.svg" width=300px> 
+        """)
+    
+    gr.Interface(    
+        voice_commands,
+        [
+          gr.Textbox(type = 'password',label="Enter your API-Key", placeholder="API-Key", lines=1),
+          gr.Audio(sources=["microphone"])
+          
+        ],
+        [
+            "text"
+        ]    
+    )
+demo.launch()