[CLEANUP][Playground]

playground/models/anthropic_example.py

@@ -1,8 +1,7 @@
-from swarms.models.anthropic import Anthropic
+from swarms.models import Anthropic
 
 model = Anthropic(anthropic_api_key="")
 
-
 task = "What is quantum field theory? What are 3 books on the field?"
 
 print(model(task))

playground/models/azure_openai.py

@@ -1,4 +1,4 @@
-from swarms.models.azure_openai_llm import AzureOpenAI
+from swarms.models import AzureOpenAI
 
 # Initialize Azure OpenAI
 model = AzureOpenAI()

playground/models/bingchat_example.py

@@ -1,35 +0,0 @@
-import os
-
-from swarms.models import OpenAIChat
-from swarms.models.bing_chat import BingChat
-from swarms.tools.autogpt import EdgeGPTTool, tool
-from swarms.workers.worker import Worker
-
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Initialize the EdgeGPTModel
-edgegpt = BingChat(cookies_path="./cookies.txt")
-
-
-@tool
-def edgegpt(task: str = None):
-    """A tool to run infrence on the EdgeGPT Model"""
-    return EdgeGPTTool.run(task)
-
-
-# Initialize the language model,
-# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
-llm = OpenAIChat(
-    openai_api_key=api_key,
-    temperature=0.5,
-)
-
-# Initialize the Worker with the custom tool
-worker = Worker(
-    llm=llm, ai_name="EdgeGPT Worker", external_tools=[edgegpt]
-)
-
-# Use the worker to process a task
-task = "Hello, my name is ChatGPT"
-response = worker.run(task)
-print(response)

playground/models/bioclip_example.py

@@ -1,24 +0,0 @@
-from swarms.models.bioclip import BioClip
-
-clip = BioClip(
-    "hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"
-)
-
-labels = [
-    "adenocarcinoma histopathology",
-    "brain MRI",
-    "covid line chart",
-    "squamous cell carcinoma histopathology",
-    "immunohistochemistry histopathology",
-    "bone X-ray",
-    "chest X-ray",
-    "pie chart",
-    "hematoxylin and eosin histopathology",
-]
-
-result = clip("swarms.jpeg", labels)
-metadata = {
-    "filename": "images/.jpg".split("/")[-1],
-    "top_probs": result,
-}
-clip.plot_image_with_metadata("swarms.jpeg", metadata)

playground/models/biogpt_example.py

@@ -1,7 +0,0 @@
-from swarms.models.biogpt import BioGPTWrapper
-
-model = BioGPTWrapper()
-
-out = model("The patient has a fever")
-
-print(out)

playground/models/cohere_example.py

@@ -1,4 +1,4 @@
-from swarms.models.cohere_chat import Cohere
+from swarms.models import Cohere
 
 cohere = Cohere(model="command-light", cohere_api_key="")
 

playground/models/custom_model_vllm.py

@@ -1,89 +0,0 @@
-from vllm import LLM
-from swarms import AbstractLLM, Agent, ChromaDB
-
-
-# Making an instance of the VLLM class
-class vLLMLM(AbstractLLM):
-    """
-    This class represents a variant of the Language Model (LLM) called vLLMLM.
-    It extends the AbstractLLM class and provides additional functionality.
-
-    Args:
-        model_name (str): The name of the LLM model to use. Defaults to "acebook/opt-13b".
-        tensor_parallel_size (int): The size of the tensor parallelism. Defaults to 4.
-        *args: Variable length argument list.
-        **kwargs: Arbitrary keyword arguments.
-
-    Attributes:
-        model_name (str): The name of the LLM model.
-        tensor_parallel_size (int): The size of the tensor parallelism.
-        llm (LLM): An instance of the LLM class.
-
-    Methods:
-        run(task: str, *args, **kwargs): Runs the LLM model to generate output for the given task.
-
-    """
-
-    def __init__(
-        self,
-        model_name: str = "acebook/opt-13b",
-        tensor_parallel_size: int = 4,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.model_name = model_name
-        self.tensor_parallel_size = tensor_parallel_size
-
-        self.llm = LLM(
-            model_name=self.model_name,
-            tensor_parallel_size=self.tensor_parallel_size,
-        )
-
-    def run(self, task: str, *args, **kwargs):
-        """
-        Runs the LLM model to generate output for the given task.
-
-        Args:
-            task (str): The task for which to generate output.
-            *args: Variable length argument list.
-            **kwargs: Arbitrary keyword arguments.
-
-        Returns:
-            str: The generated output for the given task.
-
-        """
-        return self.llm.generate(task)
-
-
-# Initializing the agent with the vLLMLM instance and other parameters
-model = vLLMLM(
-    "facebook/opt-13b",
-    tensor_parallel_size=4,
-)
-
-# Defining the task
-task = "What are the symptoms of COVID-19?"
-
-# Running the agent with the specified task
-out = model.run(task)
-
-
-# Integrate Agent
-agent = Agent(
-    agent_name="Doctor agent",
-    agent_description=(
-        "This agent provides information about COVID-19 symptoms."
-    ),
-    llm=model,
-    max_loops="auto",
-    autosave=True,
-    verbose=True,
-    long_term_memory=ChromaDB(
-        metric="cosine",
-        n_results=3,
-        output_dir="results",
-        docs_folder="docs",
-    ),
-    stopping_condition="finish",
-)

playground/models/dall3_example.py

@@ -1,6 +0,0 @@
-from swarms.models import Dalle3
-
-dalle3 = Dalle3(openai_api_key="")
-task = "A painting of a dog"
-image_url = dalle3(task)
-print(image_url)

playground/models/fast_vit_example.py

@@ -1,5 +0,0 @@
-from swarms.models.fastvit import FastViT
-
-fastvit = FastViT()
-
-result = fastvit(img="images/swarms.jpeg", confidence_threshold=0.5)

playground/models/jina_embeds_example.py

@@ -1,7 +0,0 @@
-from swarms.models import JinaEmbeddings
-
-model = JinaEmbeddings()
-
-embeddings = model("Encode this text")
-
-print(embeddings)

playground/models/palm_example.py

@@ -1,5 +1,5 @@
-from swarms.models.palm import PALM
+from swarms.models import Palm
 
-palm = PALM()
+palm = Palm()
 
-out = palm("path/to/image.png")
+out = palm("what's your name")

playground/models/roboflow_example.py

@@ -1,12 +0,0 @@
-from swarms import RoboflowMultiModal
-
-# Initialize the model
-model = RoboflowMultiModal(
-    api_key="api",
-    project_id="your project id",
-    hosted=False,
-)
-
-
-# Run the model on an img
-out = model("img.png")

playground/models/yi_200k_example.py

@@ -1,5 +0,0 @@
-from swarms.models.yi_200k import Yi200k
-
-models = Yi200k()
-
-out = models("What is the weather like today?")