multi agent coder corrected

README.md

@@ -86,8 +86,9 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
 - Special thanks to all contributors and the open-source community.
 - Inspiration and support from our user base.
 
-## Screeshot
+## Usecases
 ### MultiAgent Coder and planner
+![alt text](coder.png)
 ![crewaidemo](image.png)
 ![alt text](image-1.png)
 

configfile.ini

@@ -2,5 +2,6 @@
 PAGE_TITLE = CrewAI Studio
 LLM_OPTIONS = Groq, Huggingface
 USECASE_OPTIONS = MultiAgent Coder, MultiAgent SearchTool, MultiAgent Image
-GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+GROQ_MODEL_OPTIONS = gemma2-9b-it, mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, 
+
 

requirements.txt

@@ -1,7 +1,7 @@
 streamlit
 langchain-core
-langchain-groq  
 crewai
 crewai_tools
 duckduckgo-search 
-langchain-community
+langchain-community
+groq

src/Handler/customhandler.py

@@ -0,0 +1,35 @@
+from langchain_core.callbacks import BaseCallbackHandler
+
+class CustomHandler(BaseCallbackHandler):
+    """A custom handler for logging interactions within the process chain."""
+
+    def __init__(self, agent_name: str) -> None:
+        super().__init__()
+        self.agent_name = agent_name
+
+    def on_chain_start(self, serialized, outputs, **kwargs) -> None:
+        """Log the start of a chain with user input."""
+        from streamlit import session_state, chat_message
+        session_state.messages.append({"role": "assistant", "content": outputs['input']})
+        chat_message("assistant").write(outputs['input'])
+
+    def on_agent_action(self, serialized, inputs, **kwargs) -> None:
+        """Log the action taken by an agent during a chain run."""
+        from streamlit import session_state, chat_message
+        session_state.messages.append({"role": "assistant", "content": inputs['input']})
+        chat_message("assistant").write(inputs['input'])
+
+    def on_chain_end(self, outputs, **kwargs) -> None:
+        """Log the end of a chain with the output generated by an agent."""
+        import streamlit as st
+        from streamlit import session_state, chat_message
+        session_state.messages.append({"role": self.agent_name, "content": outputs['output']})
+        output = outputs['output']
+        st.write("**********")
+        st.write(self.agent_name)
+        st.write("**********")
+        if self.agent_name == 'Visual Content Creator':
+            chat_message(self.agent_name).image(f"{self.agent_name } : {output}")
+        else : 
+            chat_message(self.agent_name).markdown(f"{self.agent_name } : {output}")
+      

src/agents/agents.py

@@ -1,47 +1,14 @@
-from langchain_core.callbacks import BaseCallbackHandler
 from crewai import Agent
-#from llm import groq
 
-class CustomHandler(BaseCallbackHandler):
-    """A custom handler for logging interactions within the process chain."""
-
-    def __init__(self, agent_name: str) -> None:
-        super().__init__()
-        self.agent_name = agent_name
-
-    def on_chain_start(self, serialized, outputs, **kwargs) -> None:
-        """Log the start of a chain with user input."""
-        from streamlit import session_state, chat_message
-        session_state.messages.append({"role": "assistant", "content": outputs['input']})
-        chat_message("assistant").write(outputs['input'])
-
-    def on_agent_action(self, serialized, inputs, **kwargs) -> None:
-        """Log the action taken by an agent during a chain run."""
-        from streamlit import session_state, chat_message
-        session_state.messages.append({"role": "assistant", "content": inputs['input']})
-        chat_message("assistant").write(inputs['input'])
-
-    def on_chain_end(self, outputs, **kwargs) -> None:
-        """Log the end of a chain with the output generated by an agent."""
-        import streamlit as st
-        from streamlit import session_state, chat_message
-        session_state.messages.append({"role": self.agent_name, "content": outputs['output']})
-        output = outputs['output']
-        st.write("**********")
-        st.write(self.agent_name)
-        st.write("**********")
-        if self.agent_name == 'Visual Content Creator':
-            chat_message(self.agent_name).image(f"{self.agent_name } : {output}")
-        else : 
-            chat_message(self.agent_name).markdown(f"{self.agent_name } : {output}")
-        
+from src.Handler.customhandler import CustomHandler
+#from llm import groq
+  
 class CrewAIAgents:
     def __init__(self,llm):
         self.llm = llm
         # self.image_generate_tool = image_generate_tool
         # self.search_tool = search_tool
         
-        
     def crewai_agents(self):
         project_manager = Agent(
             role='Project Manager',
@@ -50,7 +17,7 @@ class CrewAIAgents:
                         You do not write code.''',
             goal='Generate actionable steps for task completion in brief.',
             llm=self.llm,
-            callbacks=[CustomHandler("Project Manager")],
+            # callbacks=[CustomHandler("Project Manager")],
             max_iter = 3
         )
         coder = Agent(
@@ -61,11 +28,11 @@ class CrewAIAgents:
                         You produce functional, feature complete code.''',
             goal='Develop high-quality, well-structured  code.',
             llm=self.llm,
-            callbacks=[CustomHandler("Coder")],
+            # callbacks=[CustomHandler("Coder")],
             max_iter = 3
         )
         
-        return [project_manager,coder]
+        return [project_manager, coder]
 
     def gen_with_crewai_agents(self,search_tool):
         from crewai import Agent
@@ -185,9 +152,6 @@ class CrewAIAgents:
 
         except Exception as e:
             raise ValueError(e, sys)
-        
-        
-        
     
     def gen_with_image_crewai_agents(self,image_generate):
         from crewai import Agent

src/llmconfig/groqllm.py

@@ -3,19 +3,22 @@ import streamlit as st
 # Initialize the Groq model for use with agents
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_groq import ChatGroq
+from crewai import LLM
 
 
 
 class GroqLLM:
     def __init__(self, user_controls_input):
         self.user_controls_input = user_controls_input
+        self.model = 'groq/'+self.user_controls_input["selected_model"]
 
     def groq_llm_config(self):
-        groq = ChatGroq(
-            max_tokens=4000,
+        llm = LLM(
             max_retries=3,
             temperature=0.7,
-            model= self.user_controls_input["selected_model"],
-            api_key="" # Optional if not set as an environment variable
+            model= self.model,
+            api_key=st.session_state["GROQ_API_KEY"] 
         )
-        return groq
+        # llm = LLM(model="groq/gemma2-9b-it",api_key=st.session_state["GROQ_API_KEY"])
+
+        return llm

src/supportingtools/tools.py

@@ -2,7 +2,7 @@ from PIL import Image
 import io
 import os
 import requests
-from crewai_tools import tool
+from crewai.tools import tool
 from langchain_community.tools import DuckDuckGoSearchRun
 
 class SupportingTools: