Update app.py

app.py

@@ -3,37 +3,272 @@ from huggingface_hub import InferenceClient
 import os
 import sys
 import pickle
+import json
 
 st.title("CODEFUSSION ☄")
 
-base_url = "https://api-inference.huggingface.co/models/"
-API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
+# --- 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 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(), SentimentAnalysisTool(), 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🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",
-    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct"
+    "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.\n""",
+        '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. \n""",
+        '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.\n""",
+        '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_promt(message, conversation_history, custom_instructions=None):
+def format_prompt(message, conversation_history, custom_instructions=None):
     prompt = ""
     if custom_instructions:
-        prompt += f"\[INST\] {custom_instructions} \[/INST\]\n"
+        prompt += f"\[INST\] {custom_instructions} $$/INST$$\n"
     
     # Add conversation history to the prompt
     prompt += "\[CONV_HISTORY\]\n"
@@ -42,7 +277,7 @@ def format_promt(message, conversation_history, custom_instructions=None):
     prompt += "\[/CONV_HISTORY\]\n"
     
     # Add the current message
-    prompt += f"\[INST\] {message} \[/INST\]\n"
+    prompt += f"\[INST\] {message} $$/INST$$\n"
     
     # Add the response format
     prompt += "\[RESPONSE\]\n"
@@ -115,26 +350,125 @@ if st.session_state.chat_state == "normal":
         formated_text = format_promt(prompt, conversation_history, custom_instruction)
         
         with st.chat_message("assistant"):
-            client = InferenceClient(
-                model=model_links[selected_model], )
-            max_new_tokens = 2048  # Adjust this value as needed
-            try:
-                output = client.text_generation(
-                    formated_text,
-                    temperature=temp_values,
-                    max_new_tokens=max_new_tokens,
-                    stream=True
-                )
-                response = st.write_stream(output)
-            except ValueError as e:
-                if "Input validation error" in str(e):
-                    st.error("Error: The input prompt is too long. Please try a shorter prompt.")
-                else:
-                    st.error(f"An error occurred: {e}")
+            # 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.session_state.messages.append({"role": "assistant", "content": response})
-                save_conversation_history(st.session_state.messages)
+                st.error("Invalid model selection.")
+                return
+
+            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()
+    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]:
+    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'})}")