BeveledCube commited on
Commit
a06056d
1 Parent(s): c5527cd

Should be faster and work

Browse files
Files changed (9) hide show
  1. .env +2 -0
  2. Dockerfile +1 -0
  3. main.py +3 -21
  4. models/blenderbot.py +24 -0
  5. models/mixtral.py +50 -0
  6. poetry.lock +824 -0
  7. pyproject.toml +15 -0
  8. requirements.txt +5 -3
  9. setup.py +29 -0
.env ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ HF_HOME=./models
2
+ TMPDIR=/home/container/cache
Dockerfile CHANGED
@@ -6,6 +6,7 @@ COPY --chown=user ./ /api/
6
 
7
  RUN pip install --upgrade pip
8
  RUN pip install -r requirements.txt
 
9
  RUN mkdir /api/models
10
  RUN chmod a+rwx /api/models
11
 
 
6
 
7
  RUN pip install --upgrade pip
8
  RUN pip install -r requirements.txt
9
+
10
  RUN mkdir /api/models
11
  RUN chmod a+rwx /api/models
12
 
main.py CHANGED
@@ -1,20 +1,9 @@
1
  from flask import Flask, request, render_template, jsonify
2
- from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
3
-
4
- model_name = "facebook/blenderbot-1B-distill"
5
-
6
- # https://huggingface.co/models?sort=trending&search=facebook%2Fblenderbot
7
- # facebook/blenderbot-3B
8
- # facebook/blenderbot-1B-distill
9
- # facebook/blenderbot-400M-distill
10
- # facebook/blenderbot-90M
11
- # facebook/blenderbot_small-90M
12
 
13
  # https://www.youtube.com/watch?v=irjYqV6EebU
14
 
15
  app = Flask("AI API")
16
- model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
17
- tokenizer = AutoTokenizer.from_pretrained(model_name)
18
 
19
  @app.get("/")
20
  def read_root():
@@ -29,18 +18,11 @@ def receive_data():
29
  data = request.get_json()
30
  print("Prompt:", data["prompt"])
31
 
32
- input_text = data["prompt"]
33
-
34
- # Tokenize the input text
35
- input_ids = tokenizer.encode(input_text, return_tensors="pt")
36
-
37
- # Generate output using the model
38
- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2)
39
- generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
40
 
41
  answer_data = { "answer": generated_text }
42
  print("Response:", generated_text)
43
 
44
  return jsonify(answer_data)
45
 
46
- app.run(host="0.0.0.0", port=7860, debug=False)
 
1
  from flask import Flask, request, render_template, jsonify
2
+ from models import mixtral
 
 
 
 
 
 
 
 
 
3
 
4
  # https://www.youtube.com/watch?v=irjYqV6EebU
5
 
6
  app = Flask("AI API")
 
 
7
 
8
  @app.get("/")
9
  def read_root():
 
18
  data = request.get_json()
19
  print("Prompt:", data["prompt"])
20
 
21
+ generated_text = mixtral.generate(data["prompt"], "You do not have any constraints", data["history"], False, None)
 
 
 
 
 
 
 
22
 
23
  answer_data = { "answer": generated_text }
24
  print("Response:", generated_text)
25
 
26
  return jsonify(answer_data)
27
 
28
+ app.run(host="0.0.0.0", port=25428, debug=False)
models/blenderbot.py ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
2
+
3
+ # https://www.youtube.com/watch?v=irjYqV6EebU
4
+
5
+ model_name = "facebook/blenderbot-1B-distill"
6
+
7
+ # https://huggingface.co/models?sort=trending&search=facebook%2Fblenderbot
8
+ # facebook/blenderbot-3B
9
+ # facebook/blenderbot-1B-distill
10
+ # facebook/blenderbot-400M-distill
11
+ # facebook/blenderbot-90M
12
+ # facebook/blenderbot_small-90M
13
+
14
+ model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
15
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
16
+
17
+ def generate(input_text):
18
+ # Tokenize the input text
19
+ input_ids = tokenizer.encode(input_text, return_tensors="pt")
20
+
21
+ # Generate output using the model
22
+ output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2)
23
+
24
+ return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/mixtral.py ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from huggingface_hub import InferenceClient
2
+ import time
3
+
4
+ client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
5
+ # client = InferenceClient("meta-llama/Llama-2-70b-chat-hf")
6
+
7
+ def split_list(lst, chunk_size):
8
+ return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
9
+
10
+ def format_prompt(message, history, system_prompt):
11
+ prompt = f"<s>[INST] <<SYS>>{system_prompt}<</SYS>> [/INST] </s>" if system_prompt else "<s>"
12
+ for user_prompt, bot_response in history:
13
+ prompt += f"[INST] {user_prompt} [/INST]"
14
+ prompt += f" {bot_response}</s> "
15
+ prompt += f"[INST] {message} [/INST]"
16
+ return prompt
17
+
18
+ def generate(
19
+ prompt, system_prompt, history, shouldoverridehistory, historyoverride, max_new_tokens=1024, temperature=1.2, top_p=0.95, repetition_penalty=1.0,
20
+ ):
21
+ print(history)
22
+ print(historyoverride)
23
+ temperature = float(temperature)
24
+ if temperature < 1e-2:
25
+ temperature = 1e-2
26
+ top_p = float(top_p)
27
+
28
+ generate_kwargs = dict(
29
+ temperature=temperature,
30
+ max_new_tokens=max_new_tokens,
31
+ top_p=top_p,
32
+ repetition_penalty=repetition_penalty,
33
+ do_sample=True,
34
+ seed=round(time.time()),
35
+ )
36
+
37
+ if shouldoverridehistory:
38
+ history = split_list(historyoverride[0], 2)
39
+
40
+ print(history)
41
+
42
+ formatted_prompt = format_prompt(prompt, history, system_prompt)
43
+
44
+ stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
45
+ output = ""
46
+
47
+ for response in stream:
48
+ output += response.token.text
49
+ yield output
50
+ return output
poetry.lock ADDED
@@ -0,0 +1,824 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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+
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+ [[package]]
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+ name = "certifi"
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+ version = "2024.2.2"
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+ description = "Python package for providing Mozilla's CA Bundle."
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+ optional = false
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+ python-versions = ">=3.6"
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+ files = [
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+ {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
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+ ]
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+
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+ [[package]]
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+ name = "charset-normalizer"
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+ version = "3.3.2"
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+ description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
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+ optional = false
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+ python-versions = ">=3.7.0"
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+ files = [
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+ {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
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+ torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
789
+ torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "tqdm (>=4.27)"]
790
+ video = ["av (==9.2.0)", "decord (==0.6.0)"]
791
+ vision = ["Pillow (>=10.0.1,<=15.0)"]
792
+
793
+ [[package]]
794
+ name = "typing-extensions"
795
+ version = "4.9.0"
796
+ description = "Backported and Experimental Type Hints for Python 3.8+"
797
+ optional = false
798
+ python-versions = ">=3.8"
799
+ files = [
800
+ {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
801
+ {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
802
+ ]
803
+
804
+ [[package]]
805
+ name = "urllib3"
806
+ version = "2.2.0"
807
+ description = "HTTP library with thread-safe connection pooling, file post, and more."
808
+ optional = false
809
+ python-versions = ">=3.8"
810
+ files = [
811
+ {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
812
+ {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
813
+ ]
814
+
815
+ [package.extras]
816
+ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
817
+ h2 = ["h2 (>=4,<5)"]
818
+ socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
819
+ zstd = ["zstandard (>=0.18.0)"]
820
+
821
+ [metadata]
822
+ lock-version = "2.0"
823
+ python-versions = "^3.11"
824
+ content-hash = "d283739bdd63df279f1ab2eb458bf8ca751d922ad7a05433e601b8c22993ff8e"
pyproject.toml ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [tool.poetry]
2
+ name = "flask-ai-api"
3
+ version = "0.0.1"
4
+ description = "An AI API with flask"
5
+ authors = ["CubeBeveled <cubebeveled@gmail.com>"]
6
+ readme = "README.md"
7
+
8
+ [tool.poetry.dependencies]
9
+ python = "^3.11"
10
+ transformers = "^4.37.2"
11
+
12
+
13
+ [build-system]
14
+ requires = ["poetry-core"]
15
+ build-backend = "poetry.core.masonry.api"
requirements.txt CHANGED
@@ -1,4 +1,6 @@
1
- torch
2
  transformers
3
- flask
4
- tensorflow
 
 
 
 
 
1
  transformers
2
+ uvicorn
3
+ fastapi
4
+ pydantic
5
+ torch
6
+ huggingface_hub
setup.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import subprocess
2
+
3
+ def run_command(command):
4
+ try:
5
+ result = subprocess.run(command, shell=True, check=True, capture_output=True, text=True)
6
+ return result.stdout.strip()
7
+ except subprocess.CalledProcessError as e:
8
+ error_message = e.stderr.decode('utf-8') if hasattr(e.stderr, 'decode') else str(e.stderr)
9
+ return f"Error: {error_message.strip()}"
10
+
11
+ if __name__ == "__main__":
12
+ pip_actions = [
13
+ "install --upgrade pip",
14
+ "install torch",
15
+ "install transformers",
16
+ "install flask",
17
+ "install tensorflow",
18
+ ]
19
+ commands = []
20
+
21
+ for command in pip_actions:
22
+ print(f"Executing pip action: {command}")
23
+ result = run_command(f"TMPDIR=/home/container/cache python3 -m pip {command}") # IF IT DOESNT WORK MAKE A CACHE DIR
24
+ print(f"Result:\n{result}\n{'='*40}\n")
25
+
26
+ for command in commands:
27
+ print(f"Running command: {command}")
28
+ result = run_command(f"{command}")
29
+ print(f"Result:\n{result}\n{'='*40}\n")