first commit- basic example phidata

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "debug streamlit",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "./.venv/Lib/site-packages/streamlit",  // /home/xx/tmp/venv/bin/streamlit",
+            "args": [
+                "run",
+                "app.py"
+            ],
+            "justMyCode": false
+        }
+    ]
+}

README.md

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+---
+title: Phidata
+emoji: 📈
+colorFrom: purple
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: phidata
+---
+## UseCases
+### 1. Basic Example
+reference:
+![alt text](basic_example.png)

app.py

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+import streamlit as st
+
+from src.agentphidata.agentphidata import Phidata
+from configfile import Config
+from src.streamlitui.loadui import LoadStreamlitUI
+from src.LLMS.groqllm import GroqLLM
+
+
+
+# MAIN Function START
+
+
+if __name__ == "__main__":
+    # config
+    obj_config = Config()
+    # load ui
+    ui = LoadStreamlitUI()
+    user_input = ui.load_streamlit_ui()
+
+
+    # userInput
+    problem = st.chat_input("Start Chat ")
+    if problem:
+        
+        # Configure LLM
+        obj_llm_config = GroqLLM(user_controls_input=user_input)
+        model = obj_llm_config.get_llm_model()
+        is_add_message_to_history = st.session_state["chat_with_history"] 
+        
+        agent_descriptions = user_input['agent_descriptions'] 
+        num_history_responses = user_input['num_history_responses'] 
+        obj_phidata= Phidata(model=model,problem=problem,is_add_message_to_history = is_add_message_to_history,num_history_responses = num_history_responses,agent_descriptions=agent_descriptions)
+        obj_phidata_agent = obj_phidata.agent()
+        if is_add_message_to_history:
+            #TODO
+            st.write(obj_phidata.run_agent_with_memory(obj_phidata_agent))
+        else:
+            st.write(obj_phidata.run_agent(obj_phidata_agent))
+        
+
+
+            
+
+        
+                
+                
+                

configfile.ini

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+[DEFAULT]
+PAGE_TITLE = PHIDATA IN ACTION
+LLM_OPTIONS = Groq
+USECASE_OPTIONS = Basic Example
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+

configfile.py

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+from configparser import ConfigParser
+
+
+class Config:
+  def __init__(self, config_file="configfile.ini"):
+    self.config = ConfigParser()
+    self.config.read(config_file)
+
+  def get_llm_options(self):
+    return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
+
+  def get_usecase_options(self):
+    return self.config["DEFAULT"].get("USECASE_OPTIONS").split(", ")
+
+  def get_groq_model_options(self):
+    return self.config["DEFAULT"].get("GROQ_MODEL_OPTIONS").split(", ")
+
+  def get_page_title(self):
+    return self.config["DEFAULT"].get("PAGE_TITLE")
+

requirements.txt

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+streamlit==1.41.1
+groq==0.13.1
+phidata==2.7.5

src/LICENSE

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src/LLMS/groqllm.py

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+import os
+
+import streamlit as st
+from phi.model.groq import Groq
+
+
+
+class GroqLLM:
+    def __init__(self,user_controls_input):
+        self.user_controls_input = user_controls_input
+
+    def get_llm_model(self):
+        try :
+            groq_api_key = self.user_controls_input['GROQ_API_KEY']
+            selected_groq_model = self.user_controls_input['selected_groq_model']
+            if groq_api_key=='' and os.environ["GROQ_API_KEY"] =='':
+                st.error("Please Enter the Groq API KEY")
+            llm = Groq(api_key =groq_api_key, id=selected_groq_model)
+            
+        except Exception as e:
+            raise ValueError(f"Error Occurred with Exception : {e}")
+        return llm

src/README.md

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+---
+title: Phidata
+emoji: 📈
+colorFrom: purple
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: phidata
+---
+## UseCases
+### 1. Basic Example
+reference:
+![alt text](basic_example.png)

src/agentphidata/agentphidata.py

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+import pprint
+from phi.agent import Agent, RunResponse
+class Phidata:
+    def __init__(self,model,problem,is_add_message_to_history,num_history_responses,agent_descriptions):
+        self.problem = problem
+        self.model = model
+        self.is_add_message_to_history = is_add_message_to_history
+        self.num_history_responses = num_history_responses
+        self.agent_descriptions = agent_descriptions
+    
+    def agent(self):
+        agent = Agent(
+            model=self.model,
+            markdown=True,
+            # Set add_history_to_messages=true to add the previous chat history to the messages sent to the Model.
+            add_history_to_messages= self.is_add_message_to_history,
+            num_history_responses=self.num_history_responses,
+            description=self.agent_descriptions,
+        )
+
+        # Get the response in a variable
+        return agent
+    def run_agent(self,agent):
+        run: RunResponse = agent.run(self.problem)
+        return run.content
+    def run_agent_with_memory(self,agent):
+        # -*- Create a run
+        agent.print_response(self.problem, stream=True)
+        # -*- Print the messages in the memory
+        return ([m.model_dump(include={"role", "content"}) for m in agent.memory.messages])
+
+        # # -*- Ask a follow up question that continues the conversation
+        # agent.print_response(self.problem, stream=True)
+        # # -*- Print the messages in the memory
+        # return pprint([m.model_dump(include={"role", "content"}) for m in agent.memory.messages])
+                

src/configfile.ini

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+[DEFAULT]
+PAGE_TITLE = PHIDATA IN ACTION
+LLM_OPTIONS = Groq
+USECASE_OPTIONS = Basic Example
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+

src/configfile.py

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+from configparser import ConfigParser
+
+
+class Config:
+  def __init__(self, config_file="configfile.ini"):
+    self.config = ConfigParser()
+    self.config.read(config_file)
+
+  def get_llm_options(self):
+    return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
+
+  def get_usecase_options(self):
+    return self.config["DEFAULT"].get("USECASE_OPTIONS").split(", ")
+
+  def get_groq_model_options(self):
+    return self.config["DEFAULT"].get("GROQ_MODEL_OPTIONS").split(", ")
+
+  def get_page_title(self):
+    return self.config["DEFAULT"].get("PAGE_TITLE")
+

src/requirements.txt

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+streamlit==1.41.1
+groq==0.13.1
+phidata==2.7.5

src/streamlitui/loadui.py

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+import streamlit as st
+from configfile import Config  # Import the Config class
+
+
+class LoadStreamlitUI:
+    def __init__(self):
+        self.config = Config()  # Create a Config instance
+        self.user_controls = {}
+
+    def load_streamlit_ui(self):
+        st.set_page_config(page_title= "🤖 " + self.config.get_page_title(), layout="wide")
+        st.header("🤖 " + self.config.get_page_title())
+
+        with st.sidebar:
+            # Get options from config
+            llm_options = self.config.get_llm_options()
+            usecase_options = self.config.get_usecase_options()
+
+            # LLM selection
+            self.user_controls["selected_llm"] = st.selectbox("Select LLM", llm_options)
+
+            if self.user_controls["selected_llm"] == 'Groq':
+                # Model selection
+                model_options = self.config.get_groq_model_options()
+                self.user_controls["selected_groq_model"] = st.selectbox("Select Model", model_options)
+                # API key input
+                self.user_controls["GROQ_API_KEY"] = st.session_state["GROQ_API_KEY"] = st.text_input("API Key",
+                                                                                                      type="password")
+            # Use case selection
+            self.user_controls["selected_usecase"] = st.selectbox("Select Usecases", usecase_options)
+            if self.user_controls['selected_usecase'] == "RAG Chat":
+                st.session_state["docs_path"]  = st.text_input("Enter Docs path or filename")
+                
+            # Use agent description about role
+            self.user_controls['agent_descriptions'] = st.text_input("Enter the description about an agent",placeholder='eg. You are a helpful assistant that always responds in a polite, upbeat and positive manner.')
+            st.session_state["chat_with_history"] = st.sidebar.toggle("Chat With History")
+            self.user_controls['num_history_responses']  = 0
+            if st.session_state["chat_with_history"]:
+                self.user_controls['num_history_responses'] = st.number_input("Enter number of history include from last chat",placeholder="eg. 1",step=1)
+
+        return self.user_controls