app.py

import streamlit as st
from huggingface_hub import InferenceClient
import os
import pickle

st.title("CODEFUSSION ☄")

# --- Agent Definitions ---
class Agent:
    def __init__(self, name, role, tools, knowledge_base=None):
        self.name = name
        self.role = role
        self.tools = tools
        self.knowledge_base = knowledge_base
        self.memory = []

    def act(self, prompt, context):
        self.memory.append((prompt, context))
        action = self.choose_action(prompt, context)
        return action

    def choose_action(self, prompt, context):
        # Placeholder for action selection logic
        # This should be implemented based on the specific agent's capabilities
        # and the available tools
        return {"tool": "Code Generation", "arguments": {"language": "python", "code": "print('Hello, World!')"}}

    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

    def learn(self, data):
        # Placeholder for learning logic
        # This should be implemented based on the agent's capabilities and the type of data
        pass

    def __str__(self):
        return f"Agent: {self.name} (Role: {self.role})"

# --- Tool Definitions ---
class Tool:
    def __init__(self, name, description):
        self.name = name
        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
        return {"output": "Tool Output"}

# --- Tool Examples ---
class CodeGenerationTool(Tool):
    def __init__(self):
        super().__init__("Code Generation", "Generates code snippets in various languages.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code generation model
        language = arguments.get("language", "python")
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"


{language}\n{code}\n

class DataRetrievalTool(Tool):
    def __init__(self):
        super().__init__("Data Retrieval", "Accesses data from APIs, databases, or files.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use APIs, databases, or file systems
        source = arguments.get("source", "https://example.com/data")
        return {"output": f"Data from {source}"}

class TextGenerationTool(Tool):
    def __init__(self):
        super().__init__("Text Generation", "Generates human-like text based on a given prompt.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a text generation model
        prompt = arguments.get("prompt", "Write a short story about a cat.")
        return {"output": f"Generated text: {prompt}"}

class CodeExecutionTool(Tool):
    def __init__(self):
        super().__init__("Code Execution", "Runs code snippets in various languages.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code execution engine
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Code executed: {code}"}

class CodeDebuggingTool(Tool):
    def __init__(self):
        super().__init__("Code Debugging", "Identifies and resolves errors in code snippets.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code debugger
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Code debugged: {code}"}

class CodeSummarizationTool(Tool):
    def __init__(self):
        super().__init__("Code Summarization", "Provides a concise overview of the functionality of a code snippet.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code summarization model
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Code summary: {code}"}

class CodeTranslationTool(Tool):
    def __init__(self):
        super().__init__("Code Translation", "Translates code snippets between different programming languages.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code translation model
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Translated code: {code}"}

class CodeOptimizationTool(Tool):
    def __init__(self):
        super().__init__("Code Optimization", "Optimizes code for performance and efficiency.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code optimization model
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Optimized code: {code}"}

class CodeDocumentationTool(Tool):
    def __init__(self):
        super().__init__("Code Documentation", "Generates documentation for code snippets.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use a code documentation generator
        code = arguments.get("code", "print('Hello, World!')")
        return {"output": f"Code documentation: {code}"}

class ImageGenerationTool(Tool):
    def __init__(self):
        super().__init__("Image Generation", "Generates images based on text descriptions.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use an image generation model
        description = arguments.get("description", "A cat sitting on a couch")
        return {"output": f"Generated image based on: {description}"}

class ImageEditingTool(Tool):
    def __init__(self):
        super().__init__("Image Editing", "Modifying existing images.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use an image editing library
        image_path = arguments.get("image_path", "path/to/image.jpg")
        return {"output": f"Image edited: {image_path}"}

class ImageAnalysisTool(Tool):
    def __init__(self):
        super().__init__("Image Analysis", "Extracting information from images, such as objects, scenes, and emotions.")

    def run(self, arguments):
        # This is a simplified example, real implementation would use an image analysis model
        image_path = arguments.get("image_path", "path/to/image.jpg")
        return {"output": f"Image analysis results: {image_path}"}

# --- Agent Pool ---
agent_pool = {
    "IdeaIntake": Agent("IdeaIntake", "Idea Intake", [DataRetrievalTool(), CodeGenerationTool(), TextGenerationTool()], knowledge_base=""),
    "CodeBuilder": Agent("CodeBuilder", "Code Builder", [CodeGenerationTool(), CodeDebuggingTool(), CodeOptimizationTool()], knowledge_base=""),
    "ImageCreator": Agent("ImageCreator", "Image Creator", [ImageGenerationTool(), ImageEditingTool()], knowledge_base=""),
}

# --- Workflow Definitions ---
class Workflow:
    def __init__(self, name, agents, task, description):
        self.name = name
        self.agents = agents
        self.task = task
        self.description = description

    def run(self, prompt, context):
        # Placeholder for workflow execution logic
        # This should be implemented based on the specific workflow's steps
        # and the interaction between the agents
        for agent in self.agents:
            action = agent.act(prompt, context)
            # Execute the tool
            if action.get("tool"):
                tool = next((t for t in agent.tools if t.name == action["tool"]), None)
                if tool:
                    output = tool.run(action["arguments"])
                    # Update context
                    context.update(output)
                    # Observe the output
                    agent.observe(output)
        return context

# --- Workflow Examples ---
class AppBuildWorkflow(Workflow):
    def __init__(self):
        super().__init__("App Build", [agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]], "Build a mobile application", "A workflow for building a mobile application.")

class WebsiteBuildWorkflow(Workflow):
    def __init__(self):
        super().__init__("Website Build", [agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]], "Build a website", "A workflow for building a website.")

class GameBuildWorkflow(Workflow):
    def __init__(self):
        super().__init__("Game Build", [agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]], "Build a game", "A workflow for building a game.")

class PluginBuildWorkflow(Workflow):
    def __init__(self):
        super().__init__("Plugin Build", [agent_pool["IdeaIntake"], agent_pool["CodeBuilder"]], "Build a plugin", "A workflow for building a plugin.")

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=2048):
        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
    context = workflow.run(prompt, context)
    # Display the output
    for agent in workflow.agents:
        st.write(f"{agent}: {agent.memory}")
        for action in agent.memory:
            st.write(f"  Action: {action}")
    return context

# --- Example Usage ---
if st.button("Build an App"):
    app_build_workflow = AppBuildWorkflow()
    context = {"task": "Build a mobile application"}
    context = execute_workflow(app_build_workflow, "Build a mobile app for ordering food.", context)
    st.write(f"Workflow Output: {context}")

if st.button("Build a Website"):
    website_build_workflow = WebsiteBuildWorkflow()
    context = {"task": "Build a website"}
    context = execute_workflow(website_build_workflow, "Build a website for a restaurant.", context)
    st.write(f"Workflow Output: {context}")

if st.button("Build a Game"):
    game_build_workflow = GameBuildWorkflow()
    context = {"task": "Build a game"}
    context = execute_workflow(game_build_workflow, "Build a simple 2D platformer game.", context)
    st.write(f"Workflow Output: {context}")

if st.button("Build a Plugin"):
    plugin_build_workflow = PluginBuildWorkflow()
    context = {"task": "Build a plugin"}
    context = execute_workflow(plugin_build_workflow, "Build a plugin for a text editor that adds a new syntax highlighting theme.", context)
    st.write(f"Workflow Output: {context}")

if st.button("Dev Sandbox"):
    dev_sandbox_workflow = DevSandboxWorkflow()
    context = {"task": "Experiment with code"}
    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():
    st.write(f"**{agent_name}**")
    st.write(f"  Role: {agent.role}")
    st.write(f"  Tools: {', '.join([tool.name for tool in agent.tools])}")

st.subheader("Workflows")
st.write("**App Build**")
st.write(f"""  Description: {AppBuildWorkflow().description}""")
st.write("**Website Build**")
st.write(f"""  Description: {WebsiteBuildWorkflow().description}""")
st.write("**Game Build**")
st.write(f"""  Description: {GameBuildWorkflow().description}""")
st.write("**Plugin Build**")
st.write(f"""  Description: {PluginBuildWorkflow().description}""")
st.write("**Dev Sandbox**")
st.write(f"""  Description: {DevSandboxWorkflow().description}""")

# --- Displaying Tool Definitions ---
st.subheader("Tool Definitions")
for tool_class in [CodeGenerationTool, DataRetrievalTool, CodeExecutionTool, CodeDebuggingTool, CodeSummarizationTool, CodeTranslationTool, CodeOptimizationTool, CodeDocumentationTool, ImageGenerationTool, ImageEditingTool, ImageAnalysisTool, TextGenerationTool]:
    tool = tool_class()
    st.write(f"**{tool.name}**")
    st.write(f"  Description: {tool.description}")

# --- Displaying Example Output ---
st.subheader("Example Output")
code_generation_tool = CodeGenerationTool()
st.write(f"""Code Generation Tool Output: {code_generation_tool.run({'language': 'python', 'code': "print('Hello, World!')"})}""")

data_retrieval_tool = DataRetrievalTool()
st.write(f"""Data Retrieval Tool Output: {data_retrieval_tool.run({'source': 'https://example.com/data'})}""")

code_execution_tool = CodeExecutionTool()
st.write(f"""Code Execution Tool Output: {code_execution_tool.run({'code': "print('Hello, World!')"})}""")

code_debugging_tool = CodeDebuggingTool()
st.write(f"""Code Debugging Tool Output: {code_debugging_tool.run({'code': "print('Hello, World!')"})}""")

code_summarization_tool = CodeSummarizationTool()
st.write(f"""Code Summarization Tool Output: {code_summarization_tool.run({'code': "print('Hello, World!')"})}""")

code_translation_tool = CodeTranslationTool()
st.write(f"""Code Translation Tool Output: {code_translation_tool.run({'code': "print('Hello, World!')"})}""")

code_optimization_tool = CodeOptimizationTool()
st.write(f"""Code Optimization Tool Output: {code_optimization_tool.run({'code': "print('Hello, World!')"})}""")

code_documentation_tool = CodeDocumentationTool()
st.write(f"""Code Documentation Tool Output: {code_documentation_tool.run({'code': "print('Hello, World!')"})}""")

image_generation_tool = ImageGenerationTool()
st.write(f"""Image Generation Tool Output: {image_generation_tool.run({'description': 'A cat sitting on a couch'})}""")

image_editing_tool = ImageEditingTool()
st.write(f"""Image Editing Tool Output: {image_editing_tool.run({'image_path': 'path/to/image.jpg'})}""")

image_analysis_tool = ImageAnalysisTool()
st.write(f"""Image Analysis Tool Output: {image_analysis_tool.run({'image_path': 'path/to/image.jpg'})}""")