formating and mointoring change (#136)

model_server/app/__init__.py

@@ -10,6 +10,7 @@ import tempfile
 # Path to the file where the server process ID will be stored
 PID_FILE = os.path.join(tempfile.gettempdir(), "model_server.pid")
 
+
 def run_server():
     """Start, stop, or restart the Uvicorn server based on command-line arguments."""
     if len(sys.argv) > 1:
@@ -45,10 +46,11 @@ def start_server():
             f.write(str(process.pid))
         print(f"ARCH GW Model Server started with PID {process.pid}")
     else:
-        #Add model_server boot-up logs
+        # Add model_server boot-up logs
         print(f"ARCH GW Model Server - Didn't Sart In Time. Shutting Down")
         process.terminate()
 
+
 def wait_for_health_check(url, timeout=180):
     """Wait for the Uvicorn server to respond to health-check requests."""
     start_time = time.time()
@@ -92,6 +94,7 @@ def stop_server():
         process.kill()  # Forcefully kill the process
         os.remove(PID_FILE)
 
+
 def restart_server():
     """Restart the Uvicorn server."""
     print("Check: Is Archgw Model Server running?")

model_server/app/arch_fc/arch_fc.py

@@ -50,15 +50,16 @@ logger.info(f"serving mode: {mode}")
 logger.info(f"using model: {chosen_model}")
 logger.info(f"using endpoint: {endpoint}")
 
+
 def process_state(arch_state, history: list[Message]):
     print("state: {}".format(arch_state))
     state_json = json.loads(arch_state)
 
     state_map = {}
     if state_json:
-      for tools_state in state_json:
-          for tool_state in tools_state:
-              state_map[tool_state['key']] = tool_state
+        for tools_state in state_json:
+            for tool_state in tools_state:
+                state_map[tool_state["key"]] = tool_state
 
     print(f"state_map: {json.dumps(state_map)}")
 
@@ -66,27 +67,38 @@ def process_state(arch_state, history: list[Message]):
     updated_history = []
     for hist in history:
         updated_history.append({"role": hist.role, "content": hist.content})
-        if hist.role == 'user':
+        if hist.role == "user":
             sha_history.append(hist.content)
             sha256_hash = hashlib.sha256()
-            joined_key_str = ('#.#').join(sha_history)
+            joined_key_str = ("#.#").join(sha_history)
             sha256_hash.update(joined_key_str.encode())
             sha_key = sha256_hash.hexdigest()
             print(f"sha_key: {sha_key}")
             if sha_key in state_map:
                 tool_call_state = state_map[sha_key]
-                if 'tool_call' in tool_call_state:
-                    tool_call_str = json.dumps(tool_call_state['tool_call'])
-                    updated_history.append({"role": "assistant", "content": f"<tool_call>\n{tool_call_str}\n</tool_call>"})
-                if 'tool_response' in tool_call_state:
-                    tool_resp = tool_call_state['tool_response']
-                    #TODO: try with role = user as well
-                    updated_history.append({"role": "user", "content": f"<tool_response>\n{tool_resp}\n</tool_response>"})
+                if "tool_call" in tool_call_state:
+                    tool_call_str = json.dumps(tool_call_state["tool_call"])
+                    updated_history.append(
+                        {
+                            "role": "assistant",
+                            "content": f"<tool_call>\n{tool_call_str}\n</tool_call>",
+                        }
+                    )
+                if "tool_response" in tool_call_state:
+                    tool_resp = tool_call_state["tool_response"]
+                    # TODO: try with role = user as well
+                    updated_history.append(
+                        {
+                            "role": "user",
+                            "content": f"<tool_response>\n{tool_resp}\n</tool_response>",
+                        }
+                    )
                 # we dont want to match this state with any other messages
-                del(state_map[sha_key])
+                del state_map[sha_key]
 
     return updated_history
 
+
 async def chat_completion(req: ChatMessage, res: Response):
     logger.info("starting request")
     tools_encoded = handler._format_system(req.tools)
@@ -98,7 +110,9 @@ async def chat_completion(req: ChatMessage, res: Response):
     for message in updated_history:
         messages.append({"role": message["role"], "content": message["content"]})
 
-    logger.info(f"model_server => arch_fc: {chosen_model}, messages: {json.dumps(messages)}")
+    logger.info(
+        f"model_server => arch_fc: {chosen_model}, messages: {json.dumps(messages)}"
+    )
     completions_params = params["params"]
     resp = client.chat.completions.create(
         messages=messages,

model_server/app/arch_fc/arch_handler.py

@@ -52,7 +52,6 @@ class ArchHandler:
         messages: list[dict],
         execution_results: list,
     ) -> dict:
-
         content = []
         for result in execution_results:
             content.append(f"<tool_response>\n{json.dumps(result)}\n</tool_response>")

model_server/app/arch_fc/test_arch_fc.py

@@ -2,16 +2,18 @@ import json
 import pytest
 from app.arch_fc.arch_fc import process_state
 from app.arch_fc.common import ChatMessage, Message
+
 # test process_state
 
 arch_state = '[[{"key":"02ea8ec721b130dc30ec836b79ec675116cd5889bca7d63720bc64baed994fc1","message":{"role":"user","content":"how is the weather in new york?"},"tool_call":{"name":"weather_forecast","arguments":{"city":"new york"}},"tool_response":"{\\"city\\":\\"new york\\",\\"temperature\\":[{\\"date\\":\\"2024-10-07\\",\\"temperature\\":{\\"min\\":68,\\"max\\":79}},{\\"date\\":\\"2024-10-08\\",\\"temperature\\":{\\"min\\":70,\\"max\\":76}},{\\"date\\":\\"2024-10-09\\",\\"temperature\\":{\\"min\\":71,\\"max\\":84}},{\\"date\\":\\"2024-10-10\\",\\"temperature\\":{\\"min\\":61,\\"max\\":79}},{\\"date\\":\\"2024-10-11\\",\\"temperature\\":{\\"min\\":86,\\"max\\":91}},{\\"date\\":\\"2024-10-12\\",\\"temperature\\":{\\"min\\":85,\\"max\\":90}},{\\"date\\":\\"2024-10-13\\",\\"temperature\\":{\\"min\\":72,\\"max\\":89}}],\\"unit\\":\\"F\\"}"}],[{"key":"566b9a2197cba89f35c1e3fbeee55882772ae7627fcf4411dae90282f98a1067","message":{"role":"user","content":"how is the weather in chicago?"},"tool_call":{"name":"weather_forecast","arguments":{"city":"chicago"}},"tool_response":"{\\"city\\":\\"chicago\\",\\"temperature\\":[{\\"date\\":\\"2024-10-07\\",\\"temperature\\":{\\"min\\":54,\\"max\\":64}},{\\"date\\":\\"2024-10-08\\",\\"temperature\\":{\\"min\\":84,\\"max\\":99}},{\\"date\\":\\"2024-10-09\\",\\"temperature\\":{\\"min\\":85,\\"max\\":100}},{\\"date\\":\\"2024-10-10\\",\\"temperature\\":{\\"min\\":50,\\"max\\":62}},{\\"date\\":\\"2024-10-11\\",\\"temperature\\":{\\"min\\":79,\\"max\\":85}},{\\"date\\":\\"2024-10-12\\",\\"temperature\\":{\\"min\\":88,\\"max\\":100}},{\\"date\\":\\"2024-10-13\\",\\"temperature\\":{\\"min\\":56,\\"max\\":61}}],\\"unit\\":\\"F\\"}"}]]'
 
+
 def test_process_state():
-  history = []
-  history.append(Message(role="user", content="how is the weather in new york?"))
-  history.append(Message(role="user", content="how is the weather in chicago?"))
-  updated_history = process_state(arch_state, history)
-  print(json.dumps(updated_history, indent=2))
+    history = []
+    history.append(Message(role="user", content="how is the weather in new york?"))
+    history.append(Message(role="user", content="how is the weather in chicago?"))
+    updated_history = process_state(arch_state, history)
+    print(json.dumps(updated_history, indent=2))
 
 
 if __name__ == "__main__":

model_server/app/load_models.py

@@ -4,6 +4,7 @@ from transformers import AutoTokenizer, pipeline
 import sqlite3
 import torch
 
+
 def get_device():
     if torch.cuda.is_available():
         device = "cuda"
@@ -14,14 +15,18 @@ def get_device():
 
     return device
 
+
 def load_transformers(models=os.getenv("MODELS", "BAAI/bge-large-en-v1.5")):
     transformers = {}
     device = get_device()
     for model in models.split(","):
-        transformers[model] = sentence_transformers.SentenceTransformer(model, device=device)
+        transformers[model] = sentence_transformers.SentenceTransformer(
+            model, device=device
+        )
 
     return transformers
 
+
 def load_guard_model(
     model_name,
     hardware_config="cpu",
@@ -57,9 +62,12 @@ def load_zero_shot_models(
     zero_shot_models = {}
     device = get_device()
     for model in models.split(","):
-        zero_shot_models[model] = pipeline("zero-shot-classification", model=model, device=device)
+        zero_shot_models[model] = pipeline(
+            "zero-shot-classification", model=model, device=device
+        )
 
     return zero_shot_models
 
-if __name__ =="__main__":
+
+if __name__ == "__main__":
     print(get_device())

model_server/app/main.py

@@ -5,7 +5,7 @@ from app.load_models import (
     load_transformers,
     load_guard_model,
     load_zero_shot_models,
-    get_device
+    get_device,
 )
 import os
 from app.utils import GuardHandler, split_text_into_chunks, load_yaml_config
@@ -21,17 +21,17 @@ logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
 )
 logger = logging.getLogger(__name__)
-
+logger.info("Device used: " + get_device())
 transformers = load_transformers()
 zero_shot_models = load_zero_shot_models()
 guard_model_config = load_yaml_config("guard_model_config.yaml")
 
 mode = os.getenv("MODE", "cloud")
 logger.info(f"Serving model mode: {mode}")
-if mode not in ['cloud', 'local-gpu', 'local-cpu']:
+if mode not in ["cloud", "local-gpu", "local-cpu"]:
     raise ValueError(f"Invalid mode: {mode}")
-if mode == 'local-cpu':
-    hardware = 'cpu'
+if mode == "local-cpu":
+    hardware = "cpu"
 else:
     hardware = "gpu" if torch.cuda.is_available() else "cpu"
 
@@ -40,6 +40,7 @@ guard_handler = GuardHandler(toxic_model=None, jailbreak_model=jailbreak_model)
 
 app = FastAPI()
 
+
 class EmbeddingRequest(BaseModel):
     input: str
     model: str
@@ -49,6 +50,7 @@ class EmbeddingRequest(BaseModel):
 async def healthz():
     return {"status": "ok"}
 
+
 @app.get("/models")
 async def models():
     models = []
@@ -61,12 +63,11 @@ async def models():
 
 @app.post("/embeddings")
 async def embedding(req: EmbeddingRequest, res: Response):
-    print(f"Embedding Call Start Time: {time.time()}")
     if req.model not in transformers:
         raise HTTPException(status_code=400, detail="unknown model: " + req.model)
-
+    start = time.time()
     embeddings = transformers[req.model].encode([req.input])
-
+    print(f"Embedding Call Complete Time: {time.time()-start}")
     data = []
 
     for embedding in embeddings.tolist():
@@ -76,7 +77,7 @@ async def embedding(req: EmbeddingRequest, res: Response):
         "prompt_tokens": 0,
         "total_tokens": 0,
     }
-    print(f"Embedding Call Complete Time: {time.time()}")
+
     return {"data": data, "model": req.model, "object": "list", "usage": usage}
 
 
@@ -197,10 +198,10 @@ class HallucinationRequest(BaseModel):
 @app.post("/hallucination")
 async def hallucination(req: HallucinationRequest, res: Response):
     """
-        Hallucination API, take input as text and return the prediction of hallucination for each parameter
-        parameters: dictionary of parameters and values
-            example     {"name": "John", "age": "25"}
-        prompt: input prompt from the user
+    Hallucination API, take input as text and return the prediction of hallucination for each parameter
+    parameters: dictionary of parameters and values
+        example     {"name": "John", "age": "25"}
+    prompt: input prompt from the user
     """
     if req.model not in zero_shot_models:
         raise HTTPException(status_code=400, detail="unknown model: " + req.model)
@@ -209,9 +210,12 @@ async def hallucination(req: HallucinationRequest, res: Response):
     candidate_labels = [f"{k} is {v}" for k, v in req.parameters.items()]
     hypothesis_template = "{}"
     result = classifier(
-        req.prompt, candidate_labels=candidate_labels, hypothesis_template=hypothesis_template, multi_label=True
+        req.prompt,
+        candidate_labels=candidate_labels,
+        hypothesis_template=hypothesis_template,
+        multi_label=True,
     )
-    result_score = result['scores']
+    result_score = result["scores"]
     result_params = {k[0]: s for k, s in zip(req.parameters.items(), result_score)}
 
     return {

model_server/app/utils.py

@@ -5,10 +5,11 @@ import torch
 import pkg_resources
 import yaml
 
+
 def load_yaml_config(file_name):
     # Load the YAML file from the package
-    yaml_path = pkg_resources.resource_filename('app', file_name)
-    with open(yaml_path, 'r') as yaml_file:
+    yaml_path = pkg_resources.resource_filename("app", file_name)
+    with open(yaml_path, "r") as yaml_file:
         return yaml.safe_load(yaml_file)
 
 
@@ -29,6 +30,7 @@ def split_text_into_chunks(text, max_words=300):
 def softmax(x):
     return np.exp(x) / np.exp(x).sum(axis=0)
 
+
 class PredictionHandler:
     def __init__(self, model, tokenizer, device, task="toxic", hardware_config="cpu"):
         self.model = model

model_server/setup.py

@@ -1,12 +1,16 @@
 from setuptools import setup, find_packages
 
+
 # Function to read requirements.txt
 def parse_requirements(filename):
-    with open(filename, 'r') as file:
-        return [line.strip() for line in file if line.strip() and not line.startswith("#")]
+    with open(filename, "r") as file:
+        return [
+            line.strip() for line in file if line.strip() and not line.startswith("#")
+        ]
+
 
 # Call the parse_requirements function to get the list of dependencies
-requirements = parse_requirements('requirements.txt')
+requirements = parse_requirements("requirements.txt")
 print(f"packages to install: {find_packages()}")
 
 setup(
@@ -16,11 +20,11 @@ setup(
     install_requires=requirements,
     package_data={
         # Specify the package and the data files you want to include
-        'app': ['/*.yaml'],  # Includes all .yaml files in the config/ folder
+        "app": ["/*.yaml"],  # Includes all .yaml files in the config/ folder
     },
     entry_points={
-        'console_scripts': [
-            'model_server=app:run_server',
+        "console_scripts": [
+            "model_server=app:run_server",
         ],
     },
 )