Dependencies clean up

README.md

@@ -40,6 +40,7 @@ We have a small gallery of examples to run here, [for more check out the docs to
 
 ### `Flow` Example
 - The `Flow` is a superior iteratioin of the `LLMChain` from Langchain, our intent with `Flow` is to create the most reliable loop structure that gives the agents their "autonomy" through 3 main methods of interaction, one through user specified loops, then dynamic where the agent parses a <DONE> token, and or an interactive human input verison, or a mix of all 3. 
+
 ```python
 
 from swarms.models import OpenAIChat
@@ -47,22 +48,37 @@ from swarms.structs import Flow
 
 api_key = ""
 
-
+# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
-# Initialize the language model,
-# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
 llm = OpenAIChat(
+    # model_name="gpt-4"
     openai_api_key=api_key,
     temperature=0.5,
+    # max_tokens=100,
 )
 
-# Initialize the flow
+## Initialize the workflow
 flow = Flow(
     llm=llm,
-    max_loops=5,
+    max_loops=2,
+    dashboard=True,
+    # stopping_condition=None,  # You can define a stopping condition as needed.
+    # loop_interval=1,
+    # retry_attempts=3,
+    # retry_interval=1,
+    # interactive=False,  # Set to 'True' for interactive mode.
+    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
 )
 
-out = flow.run("Generate a 10,000 word blog, say Stop when done")
+# out = flow.load_state("flow_state.json")
-print(out)
+# temp = flow.dynamic_temperature()
+# filter = flow.add_response_filter("Trump")
+out = flow.run("Generate a 10,000 word blog on health and wellness.")
+# out = flow.validate_response(out)
+# out = flow.analyze_feedback(out)
+# out = flow.print_history_and_memory()
+# # out = flow.save_state("flow_state.json")
+# print(out)
+
 
 
 ```

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "1.9.5"
+version = "1.9.6"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -28,7 +28,6 @@ openai = "*"
 langchain = "*"
 asyncio = "*"
 nest_asyncio = "*"
-pegasusx = "*"
 einops = "*"
 google-generativeai = "*"
 torch = "*"
@@ -48,10 +47,8 @@ beautifulsoup4 = "*"
 huggingface-hub = "*"
 pydantic = "*"
 tenacity = "*"
-redis = "*"
 Pillow = "*"
 chromadb = "*"
-agent-protocol = "*"
 open-interpreter = "*"
 tabulate = "*"
 termcolor = "*"

swarms/models/__init__.py

@@ -16,6 +16,8 @@ from swarms.models.kosmos_two import Kosmos
 from swarms.models.vilt import Vilt
 from swarms.models.nougat import Nougat
 from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA
+from swarms.models.gpt4v import GPT4Vision
+from swarms.models.dalle3 import Dalle3
 
 # from swarms.models.distilled_whisperx import DistilWhisperModel
 
@@ -43,4 +45,6 @@ __all__ = [
     "HuggingfaceLLM",
     "MPT7B",
     "WizardLLMStoryTeller",
+    "GPT4Vision",
+    "Dalle3",
 ]

swarms/models/dalle3.py

@@ -1,15 +1,14 @@
-import openai
 import logging
 import os
 from dataclasses import dataclass
-from functools import lru_cache
-from termcolor import colored
-from openai import OpenAI
-from dotenv import load_dotenv
-from pydantic import BaseModel, validator
-from PIL import Image
 from io import BytesIO
 
+import openai
+from dotenv import load_dotenv
+from openai import OpenAI
+from PIL import Image
+from pydantic import validator
+from termcolor import colored
 
 load_dotenv()
 
@@ -111,10 +110,10 @@ class Dalle3:
         try:
             # Making a call to the the Dalle3 API
             response = self.client.images.generate(
-                # model=self.model,
+                model=self.model,
                 prompt=task,
-                # size=self.size,
+                size=self.size,
-                # quality=self.quality,
+                quality=self.quality,
                 n=self.n,
             )
             # Extracting the image url from the response