Update app.py

app.py

@@ -1,126 +1,29 @@
-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)
-    result_len = len(result.text)
-    
-    # Added prompt below
-    input_prompt = "The following is a transcript of someone talking, please predict what they will say next. \n"
-    ### code 
-    input_total = input_prompt + result.text
-    input_ids = tokenizer(input_total, return_tensors="pt").input_ids
-    print("inputs ", input_ids)
-
-    # prompt length
-    # prompt_length = len(tokenizer.decode(inputs_ids[0]))
-    
-    # length penalty for gpt2.generate??? 
-    #Prompt
-    #generated_outputs = gpt2.generate(input_ids, do_sample=True, num_return_sequences=3, output_scores=True, max_length=4)
-    output = gpt2.generate(input_ids, max_length=5, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=5)
-    print("output ", output)
-    #outputs = [output[-4:] for output in output.tolist()]
-    # print("outputs generated ", generated_outputs[0])
-    # only use id's that were generated
-    # gen_sequences has shape [3, 15]
-    
-    #gen_sequences = outputs.sequences[:, input_ids.shape[-1]:]
-    #print("gen sequences: ", gen_sequences)
-    
-    # let's stack the logits generated at each step to a tensor and transform
-    # logits to probs
-    #probs = torch.stack(generated_outputs.scores, dim=1).softmax(-1)  # -> shape [3, 15, vocab_size]
-    
-    # now we need to collect the probability of the generated token
-    # we need to add a dummy dim in the end to make gather work
-    #gen_probs = torch.gather(probs, 2, gen_sequences[:, :, None]).squeeze(-1)
-    #print("gen probs result: ", gen_probs)
-    # now we can do all kinds of things with the probs
-    
-    # 1) the probs that exactly those sequences are generated again
-    # those are normally going to be very small
-    # unique_prob_per_sequence = gen_probs.prod(-1)
-    
-    # 2) normalize the probs over the three sequences
-    # normed_gen_probs = gen_probs / gen_probs.sum(0)
-    # assert normed_gen_probs[:, 0].sum() == 1.0, "probs should be normalized"
-    
-    # 3) compare normalized probs to each other like in 1)
-    # unique_normed_prob_per_sequence = normed_gen_probs.prod(-1)
-    
-    ### end code
-    # print audio data as text
-    # print(result.text)
-    # prompt
-    getText = generator(result.text, max_new_tokens=10, num_return_sequences=5)
-    state = getText
-    print(state)
-    gt = [gt['generated_text'] for gt in state]
-    print(type(gt))
-    gtTrim = [gt[:reasult_len] for val in gt]
-        
-    # result.text
-    #return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    return result.text, state, gtTrim
-
-
-
-# get audio from microphone 
-
-gr.Interface(
-        fn=inference, 
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath"), 
-        "state"
-    ],
-    outputs=[
-        "textbox",
-        "state",
-        "textbox"
-    ],
-    live=True).launch()
+'''
+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'])