Update app.py

app.py

@@ -1,29 +1,98 @@
 
-
-
 '''
 This script calls the ada model from openai api to predict the next few words.
 '''
 import os
-import openai
-PROMPT = """The following is a transcript of a conversation. Predict a few nouns, verbs, or adjectives that may be used next. Predict the next few words as a list of options. 
-A few examples are provided below and then the current transcript is provided.
-Examples:
-Transcript: I'm making spaghetti for dinner
-Next: Tonight, Tomorrow, for us, our neighbors
-Transcript: I would like to order a cheeseburger with a side of
-Next: Fries, Milkshake, Apples
-Current Transcript:
-Transcript: I'm going to the store to buy
-Next:"""
-
-openai.api_key = os.environ["Openai_APIkey"]
-
-response = openai.Completion.create(
-                    model="text-ada-001",
-                    prompt=PROMPT,
-                    temperature=1,
-                    max_tokens=4,
-                    n=4)
-for i in range(4):
-    print(response['choices'][i]['text'])
+#import openai
+import os
+from pprint import pprint
+os.system("pip install git+https://github.com/openai/whisper.git")
+import gradio as gr
+import whisper
+from transformers import pipeline
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+import time
+# import streaming.py
+# from next_word_prediction import GPT2
+
+
+
+
+#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')
+
+# whisper model specification 
+model = whisper.load_model("tiny")
+
+
+        
+def inference(audio, state=""):
+
+    #time.sleep(2)
+    #text = p(audio)["text"]
+    #state += text + " "
+    # load audio data
+    audio = whisper.load_audio(audio)
+    # ensure sample is in correct format for inference
+    audio = whisper.pad_or_trim(audio)
+
+    # generate a log-mel spetrogram of the audio data
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    
+    _, probs = model.detect_language(mel)
+
+    # decode audio data
+    options = whisper.DecodingOptions(fp16 = False)
+    # transcribe speech to text
+    result = whisper.decode(model, mel, options)
+
+    PROMPT = """The following is a transcript of a conversation. Predict a few nouns, verbs, or adjectives that may be used next. Predict the next few words as a list of options. 
+    A few examples are provided below and then the current transcript is provided.
+    Examples:
+    Transcript: Tomorrow night we're going out to 
+    Next: The Movies, A Restaurant, A Baseball Game, The Theater, A Party   
+    Transcript: I would like to order a cheeseburger with a side of
+    Next: Fries, Milkshake, Apples, Salad, Katsup 
+    """
+    text = PROMPT + result.text
+    
+    openai.api_key = os.environ["Openai_APIkey"]
+    
+    response = openai.Completion.create(
+                        model="text-ada-001",
+                        prompt=text,
+                        temperature=1,
+                        max_tokens=4,
+                        n=4)
+    for i in range(4):
+        print(response['choices'][i]['text'])
+    
+
+    # result.text
+    #return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    return result.text, state, response
+
+
+
+# get audio from microphone 
+
+gr.Interface(
+        fn=inference, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath"), 
+        "state"
+    ],
+    outputs=[
+        "textbox",
+        "state",
+        "textbox"
+    ],
+    live=True).launch()