Bunmi e

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

3.0

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+Collecting opentok
+  Downloading https://files.pythonhosted.org/packages/e5/36/39ff61bab71ad73bfaeab4a5f347dba94ab7889ef7d22958b84c424455b4/opentok-3.1.0.tar.gz
+  Installing build dependencies: started
+  Installing build dependencies: still running...
+  Installing build dependencies: finished with status 'done'
+  Getting requirements to build wheel: started
+  Getting requirements to build wheel: finished with status 'done'
+    Preparing wheel metadata: started
+    Preparing wheel metadata: finished with status 'done'
+Collecting python-jose (from opentok)
+  Downloading https://files.pythonhosted.org/packages/bd/2d/e94b2f7bab6773c70efc70a61d66e312e1febccd9e0db6b9e0adf58cbad1/python_jose-3.3.0-py2.py3-none-any.whl
+Requirement already satisfied: requests in c:\users\bunmi\onedrive\documents\lib\site-packages (from opentok) (2.25.0)
+Requirement already satisfied: six in c:\users\bunmi\onedrive\documents\lib\site-packages (from opentok) (1.15.0)
+Requirement already satisfied: pytz in c:\users\bunmi\onedrive\documents\lib\site-packages (from opentok) (2019.3)
+Requirement already satisfied: pyasn1 in c:\users\bunmi\onedrive\documents\lib\site-packages (from python-jose->opentok) (0.4.8)
+Collecting ecdsa!=0.15 (from python-jose->opentok)
+  Downloading https://files.pythonhosted.org/packages/4a/b6/b678b080967b2696e9a201c096dc076ad756fb35c87dca4e1d1a13496ff7/ecdsa-0.17.0-py2.py3-none-any.whl (119kB)
+Requirement already satisfied: rsa in c:\users\bunmi\onedrive\documents\lib\site-packages (from python-jose->opentok) (4.6)
+Requirement already satisfied: idna<3,>=2.5 in c:\users\bunmi\onedrive\documents\lib\site-packages (from requests->opentok) (2.10)
+Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\bunmi\onedrive\documents\lib\site-packages (from requests->opentok) (1.26.2)
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+Requirement already satisfied: chardet<4,>=3.0.2 in c:\users\bunmi\onedrive\documents\lib\site-packages (from requests->opentok) (3.0.4)
+Building wheels for collected packages: opentok
+  Building wheel for opentok (PEP 517): started
+  Building wheel for opentok (PEP 517): finished with status 'done'
+  Created wheel for opentok: filename=opentok-3.1.0-cp37-none-any.whl size=26318 sha256=db4026e59a81901a990ff2badc1e78a888fe684e8773ef83e0c6464aab24ff45
+  Stored in directory: C:\Users\Bunmi\AppData\Local\pip\Cache\wheels\f4\fb\17\0db41a13ec5785399e8047b1d2437725745a19eda37752d691
+Successfully built opentok
+Installing collected packages: ecdsa, python-jose, opentok
+Successfully installed ecdsa-0.17.0 opentok-3.1.0 python-jose-3.3.0

app.py

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+import uvicorn
+
+if __name__ == "__main__":
+    uvicorn.run("fast:app", host="0.0.0.0", port=8000, reload=True)
+
+

fast.py

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+# # Copyright (c) 2019-present, HuggingFace Inc.
+# All rights reserved.
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import logging
+import random
+from argparse import ArgumentParser
+from itertools import chain
+from pprint import pformat
+import warnings
+
+import torch
+import torch.nn.functional as F
+
+from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, GPT2LMHeadModel, GPT2Tokenizer
+from train import SPECIAL_TOKENS, build_input_from_segments, add_special_tokens_
+from utils import get_dataset, download_pretrained_model
+
+from transformers import AutoTokenizer, AutoModel
+from fastapi import FastAPI
+
+from fastapi import FastAPI
+from fastapi import APIRouter, Body
+
+app = FastAPI()
+
+@app.get("/")
+def hello():
+    return {"message":"Hello TutLinks.com"}
+
+def top_filtering(logits, top_k=0., top_p=0.9, threshold=-float('Inf'), filter_value=-float('Inf')):
+    """ Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
+        Args:
+            logits: logits distribution shape (vocabulary size)
+            top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.
+            top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset
+                whose total probability mass is greater than or equal to the threshold top_p.
+                In practice, we select the highest probability tokens whose cumulative probability mass exceeds
+                the threshold top_p.
+            threshold: a minimal threshold to keep logits
+    """
+    assert logits.dim() == 1  # Only work for batch size 1 for now - could update but it would obfuscate a bit the code
+    top_k = min(top_k, logits.size(-1))
+    if top_k > 0:
+        # Remove all tokens with a probability less than the last token in the top-k tokens
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p > 0.0:
+        # Compute cumulative probabilities of sorted tokens
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold
+        sorted_indices_to_remove = cumulative_probabilities > top_p
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # Back to unsorted indices and set them to -infinity
+        indices_to_remove = sorted_indices[sorted_indices_to_remove]
+        logits[indices_to_remove] = filter_value
+
+    indices_to_remove = logits < threshold
+    logits[indices_to_remove] = filter_value
+
+    return logits
+
+
+def sample_sequence(personality, history, tokenizer, model, args, current_output=None):
+    special_tokens_ids = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS)
+    if current_output is None:
+        current_output = []
+
+    for i in range(args.max_length):
+        instance = build_input_from_segments(personality, history, current_output, tokenizer, with_eos=False)
+
+        input_ids = torch.tensor(instance["input_ids"], device=args.device).unsqueeze(0)
+        token_type_ids = torch.tensor(instance["token_type_ids"], device=args.device).unsqueeze(0)
+
+        logits = model(input_ids, token_type_ids=token_type_ids)
+        if isinstance(logits, tuple):  # for gpt2 and maybe others
+            logits = logits[0]
+        logits = logits[0, -1, :] / args.temperature
+        logits = top_filtering(logits, top_k=args.top_k, top_p=args.top_p)
+        probs = F.softmax(logits, dim=-1)
+
+        prev = torch.topk(probs, 1)[1] if args.no_sample else torch.multinomial(probs, 1)
+        if i < args.min_length and prev.item() in special_tokens_ids:
+            while prev.item() in special_tokens_ids:
+                if probs.max().item() == 1:
+                    warnings.warn("Warning: model generating special token with probability 1.")
+                    break  # avoid infinitely looping over special token
+                prev = torch.multinomial(probs, num_samples=1)
+
+        if prev.item() in special_tokens_ids:
+            break
+        current_output.append(prev.item())
+
+    return current_output
+
+
+def run():
+    parser = ArgumentParser()
+    parser.add_argument("--dataset_path", type=str, default="", help="Path or url of the dataset. If empty download from S3.")
+    parser.add_argument("--dataset_cache", type=str, default='./dataset_cache', help="Path or url of the dataset cache")
+    parser.add_argument("--model", type=str, default="openai-gpt", help="Model type (openai-gpt or gpt2)", choices=['openai-gpt', 'gpt2'])  # anything besides gpt2 will load openai-gpt
+    parser.add_argument("--model_checkpoint", type=str, default="", help="Path, url or short name of the model")
+    parser.add_argument("--max_history", type=int, default=2, help="Number of previous utterances to keep in history")
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device (cuda or cpu)")
+
+    parser.add_argument("--no_sample", action='store_true', help="Set to use greedy decoding instead of sampling")
+    parser.add_argument("--max_length", type=int, default=20, help="Maximum length of the output utterances")
+    parser.add_argument("--min_length", type=int, default=1, help="Minimum length of the output utterances")
+    parser.add_argument("--seed", type=int, default=0, help="Seed")
+    parser.add_argument("--temperature", type=float, default=0.7, help="Sampling softmax temperature")
+    parser.add_argument("--top_k", type=int, default=0, help="Filter top-k tokens before sampling (<=0: no filtering)")
+    parser.add_argument("--top_p", type=float, default=0.9, help="Nucleus filtering (top-p) before sampling (<=0.0: no filtering)")
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger(__file__)
+    logger.info(pformat(args))
+
+    if args.model_checkpoint == "":
+        if args.model == 'gpt2':
+            raise ValueError("Interacting with GPT2 requires passing a finetuned model_checkpoint")
+        else:
+            args.model_checkpoint = download_pretrained_model()
+
+
+    if args.seed != 0:
+    	random.seed(args.seed)
+    	torch.random.manual_seed(args.seed)
+    	torch.cuda.manual_seed(args.seed)
+
+
+    logger.info("Get pretrained model and tokenizer")
+
+
+    tokenizer_class, model_class = (GPT2Tokenizer, GPT2LMHeadModel) if args.model == 'gpt2' else (OpenAIGPTTokenizer, OpenAIGPTLMHeadModel)
+    global tokenizer
+    tokenizer = tokenizer_class.from_pretrained(args.model_checkpoint)
+    global model
+    model = model_class.from_pretrained(args.model_checkpoint)
+    model.to(args.device)
+    add_special_tokens_(model, tokenizer)
+
+    logger.info("Sample a personality")
+    dataset = get_dataset(tokenizer, args.dataset_path, args.dataset_cache)
+    personalities = [dialog["personality"] for dataset in dataset.values() for dialog in dataset]
+    personality = random.choice(personalities)
+    logger.info("Selected personality: %s", tokenizer.decode(chain(*personality)))
+
+    return model, tokenizer, args, personality
+
+model, tokenizer, args, personality = run()
+
+@app.get('/predict/{raw_text}')
+def predictions(raw_text):
+
+    history = []
+    while True:
+        while not raw_text:
+            print('Prompt should not be empty!')
+            raw_text = 'hi'
+        history.append(tokenizer.encode(raw_text))
+        with torch.no_grad():
+            out_ids = sample_sequence(personality, history, tokenizer, model, args)
+        history.append(out_ids)
+        history = history[-(2*args.max_history+1):]
+        out_text = tokenizer.decode(out_ids, skip_special_tokens=True)
+        return {'text': out_text}
+
+
+

interact.py

@@ -133,6 +133,7 @@ def run():
     dataset = get_dataset(tokenizer, args.dataset_path, args.dataset_cache)
     personalities = [dialog["personality"] for dataset in dataset.values() for dialog in dataset]
     personality = random.choice(personalities)
+    print(personalities)
     logger.info("Selected personality: %s", tokenizer.decode(chain(*personality)))
 
     history = []
@@ -151,4 +152,4 @@ def run():
 
 
 if __name__ == "__main__":
-    run()
+    run()

main.py

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+import uvicorn
+
+if __name__ == "__main__":
+    uvicorn.run("fast:app", host="0.0.0.0", port=8118, reload=True)
+
+

requirements.txt

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-torch
+torch == 1.7.1
 pytorch-ignite
 transformers==2.5.1
-tensorboardX==1.8
-tensorflow  # for tensorboardX
+fastapi ==0.70.0
+uvicorn == 0.15.0
 spacy
+tensorboardX==1.8
+tensorflow  # for tensorboardX

style.css

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+
+body{
+	font:15px/1.5 Arial, Helvetica,sans-serif;
+	padding: 0;
+	background-color: #f4f3f3;
+}
+
+.container{
+	text-align:center;
+	width:100%;
+	margin: auto;
+	overflow: hidden;
+}
+
+header{
+	background: #03a9f4;
+	border-bottom: #740a39 3px solid;
+	height:120px;
+	width:100%;
+	padding-top:30px;
+
+}
+
+.main-header{
+			text-align:center;
+			background-color: #03a9f4;
+			height:100px;
+			width:100%;
+			margin:0;
+}
+
+.brandname{
+	text-align:center;
+	font-size:30px;
+	color: #161515;
+	margin: 10px;
+}
+
+header h2{
+	text-align:center;
+	color:#fff;
+}
+
+.results{
+	border-radius: 15px 50px;
+    background: #345fe4;
+    padding: 20px;
+    width: 200px;
+    height: 150px;
+}