Update app.py

app.py

@@ -2,29 +2,188 @@ import streamlit as st
 from huggingface_hub import InferenceClient
 import os
 import pickle
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
-from langchain.tools import Tool
-from langchain.agents import AgentType
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.chains.question_answering import load_qa_chain
-from langchain.document_loaders import TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings  # Use Hugging Face Embeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain.chains.conversational_retrieval_qa import ConversationalRetrievalQAChain
-from langchain.chains.summarization import load_summarization_chain
-from langchain.chains.base import Chain
-from langchain.chains.llm import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.agents import initialize_agent, AgentType
-from langchain.tools import Tool
+from langchain_community.chains import ConversationChain
+from langchain_community.memory import ConversationBufferMemory
+from langchain_community.tools import Tool
+from langchain_community.agents import initialize_agent, AgentType
+from langchain_community.chains import LLMChain
+from langchain_community.prompts import PromptTemplate
+from langchain_community.chains.question_answering import load_qa_chain
+from langchain_community.document_loaders import TextLoader
+from langchain_community.text_splitter import CharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings  # Use Hugging Face Embeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.chains import RetrievalQA
+from langchain_community.chains.conversational_retrieval_qa import ConversationalRetrievalQAChain
+from langchain_community.chains.summarization import load_summarization_chain
 from langchain_community.llms import HuggingFaceHub
 from typing import List, Dict, Any, Optional
 
-st.title("CODEFUSSION ☄")
+st.title("Triagi - Dev-Centric Agent Clusters ☄")
+
+# --- Model Definitions ---
+class Model:
+    def __init__(self, name, description, model_link):
+        self.name = name
+        self.description = description
+        self.model_link = model_link
+        self.inference_client = InferenceClient(model=model_link)
+
+    def generate_text(self, prompt, temperature=0.5, max_new_tokens=4096):
+        try:
+            output = self.inference_client.text_generation(
+                prompt,
+                temperature=temperature,
+                max_new_tokens=max_new_tokens,
+                stream=True
+            )
+            response = "".join(output)
+        except ValueError as e:
+            if "Input validation error" in str(e):
+                return "Error: The input prompt is too long. Please try a shorter prompt."
+            else:
+                return f"An error occurred: {e}"
+        return response
+
+# --- Model Examples ---
+class FrontendForgeModel(Model):
+    def __init__(self):
+        super().__init__("FrontendForge🚀", "The FrontendForge model is a Large Language Model (LLM) that's able to handle frontend development tasks such as UI design and user interaction logic.", "mistralai/Mistral-7B-Instruct-v0.2")
+
+class BackendBuilderModel(Model):
+    def __init__(self):
+        super().__init__("BackendBuilder⭐", "The BackendBuilder model is a Large Language Model (LLM) that's specialized in backend development tasks including API creation, database management, and server-side logic.", "mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+class IntegratorModel(Model):
+    def __init__(self):
+        super().__init__("Integrator🔄", "The Integrator model is a Large Language Model (LLM) that's best suited for integrating frontend and backend components, handling business logic, and ensuring seamless communication between different parts of the application.", "microsoft/Phi-3-mini-4k-instruct")
+
+# --- Streamlit Interface ---
+model_links = {
+    "FrontendForge🚀": "mistralai/Mistral-7B-Instruct-v0.2",
+    "BackendBuilder⭐": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "Integrator🔄": "microsoft/Phi-3-mini-4k-instruct"
+}
+
+model_info = {
+    "FrontendForge🚀": {
+        'description': "The FrontendForge model is a Large Language Model (LLM) that's able to handle frontend development tasks such as UI design and user interaction logic.",
+        'logo': './11.jpg'
+    },
+    "BackendBuilder⭐": {
+        'description': "The BackendBuilder model is a Large Language Model (LLM) that's specialized in backend development tasks including API creation, database management, and server-side logic.",
+        'logo': './2.jpg'
+    },
+    "Integrator🔄": {
+        'description': "The Integrator model is a Large Language Model (LLM) that's best suited for integrating frontend and backend components, handling business logic, and ensuring seamless communication between different parts of the application.",
+        'logo': './3.jpg'
+    },
+}
+
+def format_prompt(message, conversation_history, custom_instructions=None):
+    prompt = ""
+    if custom_instructions:
+        prompt += "[INST] {} [/INST]\n".format(custom_instructions)
+    
+    # Add conversation history to the prompt
+    prompt += "[CONV_HISTORY]\n"
+    for role, content in conversation_history:
+        prompt += "{}: {}\n".format(role.upper(), content)
+    prompt += "[/CONV_HISTORY]\n"
+    
+    # Add the current message
+    prompt += "[INST] {} [/INST]\n".format(message)
+    
+    # Add the response format
+    prompt += "[RESPONSE]\n"
+    
+    return prompt
+
+def reset_conversation():
+    '''
+    Resets Conversation
+    '''
+    st.session_state.conversation = []
+    st.session_state.messages = []
+    st.session_state.chat_state = "reset"
+
+def load_conversation_history():
+    history_file = "conversation_history.pickle"
+    if os.path.exists(history_file):
+        with open(history_file, "rb") as f:
+            conversation_history = pickle.load(f)
+    else:
+        conversation_history = []
+    return conversation_history
+
+def save_conversation_history(conversation_history):
+    history_file = "conversation_history.pickle"
+    with open(history_file, "wb") as f:
+        pickle.dump(conversation_history, f)
+
+models = [key for key in model_links.keys()]
+selected_model = st.sidebar.selectbox("Select Model", models)
+temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
+st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button
+
+st.sidebar.write(f"You're now chatting with **{selected_model}**")
+st.sidebar.markdown(model_info[selected_model]['description'])
+st.sidebar.image(model_info[selected_model]['logo'])
+
+st.sidebar.markdown("*Generating the code might go slow if you are using low power resources*")
+
+if "prev_option" not in st.session_state:
+    st.session_state.prev_option = selected_model
+
+if st.session_state.prev_option != selected_model:
+    st.session_state.messages = []
+    st.session_state.prev_option = selected_model
+
+if "chat_state" not in st.session_state:
+    st.session_state.chat_state = "normal"
+
+# Load the conversation history from the file
+if "messages" not in st.session_state:
+    st.session_state.messages = load_conversation_history()
+
+repo_id = model_links[selected_model]
+st.subheader(f'{selected_model}')
+
+if st.session_state.chat_state == "normal":
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"):
+        custom_instruction = "Act like a Human in conversation"
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        conversation_history = [(message["role"], message["content"]) for message in st.session_state.messages]
+        
+        formated_text = format_prompt(prompt, conversation_history, custom_instruction)
+        
+        with st.chat_message("assistant"):
+            # Select the appropriate model based on the user's choice
+            if selected_model == "FrontendForge🚀":
+                model = FrontendForgeModel()
+            elif selected_model == "BackendBuilder⭐":
+                model = BackendBuilderModel()
+            elif selected_model == "Integrator🔄":
+                model = IntegratorModel()
+            else:
+                st.error("Invalid model selection.")
+                st.stop()  # Stop the Streamlit app execution
+
+            response = model.generate_text(formated_text, temperature=temp_values)
+            st.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})
+            save_conversation_history(st.session_state.messages)
+
+elif st.session_state.chat_state == "reset":
+    st.session_state.chat_state = "normal"
+    st.experimental_rerun()
 
 # --- Agent Definitions ---
 class Agent:
@@ -48,13 +207,11 @@ class Agent:
         return action
 
     def observe(self, observation):
-        # Placeholder for observation processing
-        # This should be implemented based on the agent's capabilities and the nature of the observation
-        pass
+        # Process observation based on the agent's capabilities and the nature of the observation
+        self.memory.append(observation)
 
     def learn(self, data):
-        # Placeholder for learning logic
-        # This should be implemented based on the agent's capabilities and the type of data
+        # Implement learning logic based on the agent's capabilities and the type of data
         pass
 
     def __str__(self):
@@ -67,9 +224,7 @@ class Tool:
         self.description = description
 
     def run(self, arguments):
-        # Placeholder for tool execution logic
-        # This should be implemented based on the specific tool's functionality
-        # and the provided arguments
+        # Implement tool execution logic based on the specific tool's functionality and the provided arguments
         return {"output": "Tool Output"}
 
 # --- Tool Examples ---
@@ -242,7 +397,7 @@ class ImageEditingTool(Tool):
         image_url = arguments.get("image_url", "https://example.com/image.jpg")
         editing_instructions = arguments.get("editing_instructions", "Make the cat smile")
         edited_image_url = self.chain.run(image_url=image_url, editing_instructions=editing_instructions)
-        return {"output": f"Edited image: {edited_image_url}"}
+        return {"output": f"Edited image: {edited_image_url}"
 
 class ImageAnalysisTool(Tool):
     def __init__(self):
@@ -274,8 +429,8 @@ class QuestionAnsweringTool(Tool):
 # --- Agent Pool ---
 agent_pool = {
     "IdeaIntake": Agent("IdeaIntake", "Idea Intake", [DataRetrievalTool(), CodeGenerationTool(), TextGenerationTool(), QuestionAnsweringTool()], knowledge_base=""),
-    "CodeBuilder": Agent("CodeBuilder", "Code Builder", [CodeGenerationTool(), CodeDebuggingTool(), CodeOptimizationTool(), CodeExecutionTool(), CodeSummarizationTool, CodeTranslationTool, CodeDocumentationTool], knowledge_base=""),
-    "ImageCreator": Agent("ImageCreator", "Image Creator", [ImageGenerationTool(), ImageEditingTool(), ImageAnalysisTool], knowledge_base=""),
+    "CodeBuilder": Agent("CodeBuilder", "Code Builder", [CodeGenerationTool(), CodeDebuggingTool(), CodeOptimizationTool(), CodeExecutionTool(), CodeSummarizationTool(), CodeTranslationTool(), CodeDocumentationTool()], knowledge_base=""),
+    "ImageCreator": Agent("ImageCreator", "Image Creator", [ImageGenerationTool(), ImageEditingTool(), ImageAnalysisTool()], knowledge_base=""),
 }
 
 # --- Workflow Definitions ---
@@ -318,170 +473,6 @@ class DevSandboxWorkflow(Workflow):
     def __init__(self):
         super().__init__("Dev Sandbox", [agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]], "Experiment with code", "A workflow for experimenting with code.")
 
-# --- Model Definitions ---
-class Model:
-    def __init__(self, name, description, model_link):
-        self.name = name
-        self.description = description
-        self.model_link = model_link
-        self.inference_client = InferenceClient(model=model_link)
-
-    def generate_text(self, prompt, temperature=0.5, max_new_tokens=4096):
-        try:
-            output = self.inference_client.text_generation(
-                prompt,
-                temperature=temperature,
-                max_new_tokens=max_new_tokens,
-                stream=True
-            )
-            response = "".join(output)
-        except ValueError as e:
-            if "Input validation error" in str(e):
-                return "Error: The input prompt is too long. Please try a shorter prompt."
-            else:
-                return f"An error occurred: {e}"
-        return response
-
-# --- Model Examples ---
-class LegacyLiftModel(Model):
-    def __init__(self):
-        super().__init__("LegacyLift🚀", "The LegacyLift model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model is best for minimal problem-solving, content writing, and daily tips.", "mistralai/Mistral-7B-Instruct-v0.2")
-
-class ModernMigrateModel(Model):
-    def __init__(self):
-        super().__init__("ModernMigrate⭐", "The ModernMigrate model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model excels in coding, logical reasoning, and high-speed inference.", "mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-class RetroRecodeModel(Model):
-    def __init__(self):
-        super().__init__("RetroRecode🔄", "The RetroRecode model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model is best suited for critical development, practical knowledge, and serverless inference.", "microsoft/Phi-3-mini-4k-instruct")
-
-# --- Streamlit Interface ---
-model_links = {
-    "LegacyLift🚀": "mistralai/Mistral-7B-Instruct-v0.2",
-    "ModernMigrate⭐": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "RetroRecode🔄": "microsoft/Phi-3-mini-4k-instruct"
-}
-
-model_info = {
-    "LegacyLift🚀": {
-        'description': "The LegacyLift model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model is best for minimal problem-solving, content writing, and daily tips.",
-        'logo': './11.jpg'
-    },
-    "ModernMigrate⭐": {
-        'description': "The ModernMigrate model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model excels in coding, logical reasoning, and high-speed inference.",
-        'logo': './2.jpg'
-    },
-    "RetroRecode🔄": {
-        'description': "The RetroRecode model is a Large Language Model (LLM) that's able to have question and answer interactions.\n \n\nThis model is best suited for critical development, practical knowledge, and serverless inference.",
-        'logo': './3.jpg'
-    },
-}
-
-def format_prompt(message, conversation_history, custom_instructions=None):
-    prompt = ""
-    if custom_instructions:
-        prompt += f"\[INST\] {custom_instructions} $$/INST$$\n"
-    
-    # Add conversation history to the prompt
-    prompt += "\[CONV_HISTORY\]\n"
-    for role, content in conversation_history:
-        prompt += f"{role.upper()}: {content}\n"
-    prompt += "\[/CONV_HISTORY\]\n"
-    
-    # Add the current message
-    prompt += f"\[INST\] {message} $$/INST$$\n"
-    
-    # Add the response format
-    prompt += "\[RESPONSE\]\n"
-    
-    return prompt
-
-def reset_conversation():
-    '''
-    Resets Conversation
-    '''
-    st.session_state.conversation = []
-    st.session_state.messages = []
-    st.session_state.chat_state = "reset"
-
-def load_conversation_history():
-    history_file = "conversation_history.pickle"
-    if os.path.exists(history_file):
-        with open(history_file, "rb") as f:
-            conversation_history = pickle.load(f)
-    else:
-        conversation_history = []
-    return conversation_history
-
-def save_conversation_history(conversation_history):
-    history_file = "conversation_history.pickle"
-    with open(history_file, "wb") as f:
-        pickle.dump(conversation_history, f)
-
-models = [key for key in model_links.keys()]
-selected_model = st.sidebar.selectbox("Select Model", models)
-temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
-st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button
-
-st.sidebar.write(f"You're now chatting with **{selected_model}**")
-st.sidebar.markdown(model_info[selected_model]['description'])
-st.sidebar.image(model_info[selected_model]['logo'])
-
-st.sidebar.markdown("\*Generating the code might go slow if you are using low power resources \*")
-
-if "prev_option" not in st.session_state:
-    st.session_state.prev_option = selected_model
-
-if st.session_state.prev_option != selected_model:
-    st.session_state.messages = []
-    st.session_state.prev_option = selected_model
-
-if "chat_state" not in st.session_state:
-    st.session_state.chat_state = "normal"
-
-# Load the conversation history from the file
-if "messages" not in st.session_state:
-    st.session_state.messages = load_conversation_history()
-
-repo_id = model_links[selected_model]
-st.subheader(f'{selected_model}')
-
-if st.session_state.chat_state == "normal":
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"):
-        custom_instruction = "Act like a Human in conversation"
-        with st.chat_message("user"):
-            st.markdown(prompt)
-        
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        conversation_history = [(message["role"], message["content"]) for message in st.session_state.messages]
-        
-        formated_text = format_prompt(prompt, conversation_history, custom_instruction)
-        
-        with st.chat_message("assistant"):
-            # Select the appropriate model based on the user's choice
-            if selected_model == "LegacyLift🚀":
-                model = LegacyLiftModel()
-            elif selected_model == "ModernMigrate⭐":
-                model = ModernMigrateModel()
-            elif selected_model == "RetroRecode🔄":
-                model = RetroRecodeModel()
-            else:
-                st.error("Invalid model selection.")
-                st.stop()  # Stop the Streamlit app execution
-
-            response = model.generate_text(formated_text, temperature=temp_values)
-            st.markdown(response)
-            st.session_state.messages.append({"role": "assistant", "content": response})
-            save_conversation_history(st.session_state.messages)
-
-elif st.session_state.chat_state == "reset":
-    st.session_state.chat_state = "normal"
-    st.experimental_rerun()
-
 # --- Agent-Based Workflow Execution ---
 def execute_workflow(workflow, prompt, context):
     # Execute the workflow
@@ -524,7 +515,6 @@ if st.button("Dev Sandbox"):
     context = execute_workflow(dev_sandbox_workflow, "Write a Python function to reverse a string.", context)
     st.write(f"Workflow Output: {context}")
 
-
 # --- Displaying Agent and Tool Information ---
 st.subheader("Agent Pool")
 for agent_name, agent in agent_pool.items():
@@ -587,4 +577,4 @@ image_analysis_tool = ImageAnalysisTool()
 st.write(f"""Image Analysis Tool Output: {image_analysis_tool.run({'image_url': 'https://example.com/image.jpg'})}""")
 
 question_answering_tool = QuestionAnsweringTool()
-st.write(f"""Question Answering Tool Output: {question_answering_tool.run({'question': 'What is the capital of France?', 'context': 'France is a country in Western Europe. Its capital is Paris.'})}""")
+st.write(f"""Question Answering Tool Output: {question_answering_tool.run({'question': 'What is the capital of France?', 'context': 'France is a country in Western Europe. Its capital is Paris.'})}""")