Major Changes changed approach to now do debugging

app.py

@@ -1,6 +1,6 @@
 
 '''
-This script calls the ada model from openai api to predict the next few words.
+This script calls the model from openai api to predict the next few words.
 '''
 import os
 os.system("pip install --upgrade pip")
@@ -17,27 +17,32 @@ import torch
 from transformers import AutoModelForCausalLM
 from transformers import AutoTokenizer
 import time
-# import streaming.py
-# from next_word_prediction import GPT2
 
+# PROMPT = """This is a tool for helping someone with memory issues remember the next word. 
 
-
-
-#gpt2 = AutoModelForCausalLM.from_pretrained("gpt2", return_dict_in_generate=True)
-#tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-### /code snippet
-
-
-# get gpt2 model
-#generator = pipeline('text-generation', model='gpt2')
+# The predictions follow a few rules:
+# 1) The predictions are suggestions of ways to continue the transcript as if someone forgot what the next word was.
+# 2) The predictions do not repeat themselves.
+# 3) The predictions focus on suggesting nouns, adjectives, and verbs.
+# 4) The predictions are related to the context in the transcript.
+    
+# EXAMPLES:
+# Transcript: Tomorrow night we're going out to 
+# Prediction: The Movies, A Restaurant, A Baseball Game, The Theater, A Party for a friend   
+# Transcript: I would like to order a cheeseburger with a side of
+# Prediction: Frnech fries, Milkshake, Apple slices, Side salad, Extra katsup 
+# Transcript: My friend Savanah is
+# Prediction: An elecrical engineer, A marine biologist, A classical musician 
+# Transcript: I need to buy a birthday
+# Prediction: Present, Gift, Cake, Card
+# Transcript: """
 
 # whisper model specification 
 model = whisper.load_model("tiny")
 
-
+openai.api_key = os.environ["Openai_APIkey"]
         
-def inference(audio, state=""):
+def debug_inference(audio, prompt, model, temperature, state=""):
     # load audio data
     audio = whisper.load_audio(audio)
     # ensure sample is in correct format for inference
@@ -54,32 +59,12 @@ def inference(audio, state=""):
     result = whisper.decode(model, mel, options)
     print("result pre gp model from whisper: ", result, ".text ", result.text, "and the data type: ", type(result.text))
 
-    PROMPT = """This is a tool for helping someone with memory issues remember the next word. 
-
-The predictions follow a few rules:
-1) The predictions are suggestions of ways to continue the transcript as if someone forgot what the next word was.
-2) The predictions do not repeat themselves.
-3) The predictions focus on suggesting nouns, adjectives, and verbs.
-4) The predictions are related to the context in the transcript.
-    
-EXAMPLES:
-Transcript: Tomorrow night we're going out to 
-Prediction: The Movies, A Restaurant, A Baseball Game, The Theater, A Party for a friend   
-Transcript: I would like to order a cheeseburger with a side of
-Prediction: Frnech fries, Milkshake, Apple slices, Side salad, Extra katsup 
-Transcript: My friend Savanah is
-Prediction: An elecrical engineer, A marine biologist, A classical musician 
-Transcript: I need to buy a birthday
-Prediction: Present, Gift, Cake, Card
-Transcript: """
-    text = PROMPT + result.text + "\nPrediction: "
-    
-    openai.api_key = os.environ["Openai_APIkey"]
+    text = prompt + result.text + "\nPrediction: "
     
     response = openai.Completion.create(
-                        model="text-davinci-003",
+                        model=model,
                         prompt=text,
-                        temperature=0.9,
+                        temperature=temperature,
                         max_tokens=8,
                         n=5)
 
@@ -97,19 +82,16 @@ Transcript: """
         infers = list(map(lambda x: x.replace("\n", ""), temp))
         #infered = list(map(lambda x: x.split(','), infers))
 
-        
-        
-
-    # result.text
-    #return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    return result.text, state, infers
-
-
+    return result.text, state, infers, text
 
 # get audio from microphone 
 gr.Interface(
-    fn=inference, 
-    inputs=[gr.inputs.Audio(source="microphone", type="filepath"), "state"],
-    outputs=["textbox","state","textbox"],
-    live=True).launch()
-
+    fn=debug_inference, 
+    inputs=[gr.inputs.Audio(source="microphone", type="filepath"),
+            gr.inputs.Textbox(lines=15, placeholder="Enter a prompt here"),
+            gr.inputs.Dropdown(["text-ada-001", "text-davinci-002", "text-davinci-003", "gpt-3.5-turbo"], label="Model"),
+            gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.8, step=0.1, label="Temperature"),
+            "state"
+            ],
+    outputs=["textbox","state","textbox", "textbox"],
+    live=True).launch()