Update app.py

app.py

@@ -14,7 +14,6 @@ from transformers import AutoModelForCausalLM
 from transformers import AutoTokenizer
 import time
 
-convoState = gr.State([""])
 
 EXAMPLE_PROMPT = """This is a tool for helping someone with memory issues remember the next word. 
 The predictions follow a few rules:
@@ -34,11 +33,33 @@ Transcript: I need to buy a birthday
 Prediction: Present, Gift, Cake, Card
 Transcript: """
 
+
 # whisper model specification
 asr_model = whisper.load_model("tiny")
 
 openai.api_key = os.environ["Openai_APIkey"]
 
+
+# get audio from microphone 
+with gr.Blocks() as face:
+    
+    with gr.Row():
+        convoState = gr.State([""])
+        with gr.Column():
+            audio = gr.Audio(source="microphone", type="filepath")
+            promptText = gr.Textbox(lines=15, placeholder="Enter a prompt here")
+            dropChoice = gr.Dropdown(choices=["text-ada-001", "text-davinci-002", "text-davinci-003", "gpt-3.5-turbo"], label="Model")
+            sliderChoice = gr.Slider(minimum=0.0, maximum=1.0, default=0.8, step=0.1, label="Temperature")
+            transcribe_btn = gr.Button(value="Transcribe")
+        with gr.Column():
+            script = gr.Textbox(label="Transcribed text")
+            options = gr.Textbox(label="Predictions")
+            latestConvo = gr.Textbox(label="Running conversation")
+            #transcribe_btn.click(inference)
+    transcribe_btn.click(fn=inference, inputs=[audio, promptText, dropChoice, sliderChoice, convoState], outputs=[latestConvo, script, options])
+    examples = gr.Examples(examples=["Sedan, Truck, SUV", "Dalmaion, Shepherd, Lab, Mutt"], inputs=[options])
+
+    
 # Transcribe function
 def transcribe(audio_file):
     print("Transcribing")
@@ -80,22 +101,6 @@ def inference(audio, prompt, model, temperature, latest):
     return transcript, infers, convoState
 
 
-# get audio from microphone 
-with gr.Blocks() as face:
-    
-    with gr.Row():
-        with gr.Column():
-            audio = gr.Audio(source="microphone", type="filepath")
-            promptText = gr.Textbox(lines=15, placeholder="Enter a prompt here")
-            dropChoice = gr.Dropdown(choices=["text-ada-001", "text-davinci-002", "text-davinci-003", "gpt-3.5-turbo"], label="Model")
-            sliderChoice = gr.Slider(minimum=0.0, maximum=1.0, default=0.8, step=0.1, label="Temperature")
-            transcribe_btn = gr.Button(value="Transcribe")
-        with gr.Column():
-            script = gr.Textbox(label="Transcribed text")
-            options = gr.Textbox(label="Predictions")
-            latestConvo = gr.Textbox(label="Running conversation")
-            #transcribe_btn.click(inference)
-    transcribe_btn.click(fn=inference, inputs=[audio, promptText, dropChoice, sliderChoice, convoState], outputs=[latestConvo, script, options])
-    examples = gr.Examples(examples=["Sedan, Truck, SUV", "Dalmaion, Shepherd, Lab, Mutt"], inputs=[options])
+
 
 face.launch()