Update app.py

app.py

@@ -1,5 +1,4 @@
 from typing import List, Dict, Optional
-
 from cust_types import (
     Code,
     Prompt,
@@ -14,7 +13,6 @@ from cust_types import (
     ReactApp,
     Code,
 )
-
 from agent import Agent
 
 # Import the translated functions from the .py files
@@ -36,229 +34,10 @@ from isStreamlitAppPrompt import isStreamlitAppPrompt
 from main import main
 from parseTutorial import parseTutorial
 from streamlitDoc import streamlitDoc
-from types import AppType
 from typescript import typescript
 
-class Agent:
-    def __init__(self, prompts: Dict[str, any]):
-        self.prompts = prompts
-        self.client = InferenceClient(
-            "mistralai/Mixtral-8x7B-Instruct-v0.1"
-        )
-
-    def process(self, user_input: str) -> str:
-        """
-        Processes the user's input and generates code.
-        """
-
-        # Parse the user's input
-        app_type, app_name, app_description, app_features, app_dependencies, app_space, app_tutorial = self.parse_input(user_input)
-
-        # Generate a prompt for the Llama model
-        prompt = self.prompts["createLlamaPrompt"](
-            app_type, app_name, app_description, app_features, app_dependencies, app_space, app_tutorial
-        )
-
-        # Generate code using the Llama model
-        code = self.generate_code(prompt)
-
-        # Generate files for the application
-        files = self.prompts["generateFiles"](
-            app_type, app_name, app_description, app_features, app_dependencies, app_space, app_tutorial
-        )
-
-        # Return the generated code and files
-        return f"Code: {code}\nFiles: {files}"
-
-    def parse_input(self, user_input: str) -> tuple:
-        """
-        Parses the user's input and extracts the relevant information.
-        """
-
-        # Extract the app type
-        app_type = self.extract_app_type(user_input)
-
-        # Extract the app name
-        app_name = self.extract_app_name(user_input)
-
-        # Extract the app description
-        app_description = self.extract_app_description(user_input)
-
-        # Extract the app features
-        app_features = self.extract_app_features(user_input)
-
-        # Extract the app dependencies
-        app_dependencies = self.extract_app_dependencies(user_input)
-
-        # Extract the app space
-        app_space = self.extract_app_space(user_input)
-
-        # Extract the app tutorial
-        app_tutorial = self.extract_app_tutorial(user_input)
-
-        return app_type, app_name, app_description, app_features, app_dependencies, app_space, app_tutorial
-
-    def extract_app_type(self, user_input: str) -> AppType:
-        """
-        Extracts the app type from the user's input.
-        """
-
-        # Check if the user specified a specific app type
-        if "web app" in user_input:
-            return AppType.WEB_APP
-        elif "gradio app" in user_input:
-            return AppType.GRADIO_APP
-        elif "streamlit app" in user_input:
-            return AppType.STREAMLIT_APP
-        elif "react app" in user_input:
-            return AppType.REACT_APP
-
-        # Otherwise, assume the user wants a web app
-        return AppType.WEB_APP
-
-    def extract_app_name(self, user_input: str) -> str:
-        """
-        Extracts the app name from the user's input.
-        """
-
-        # Find the substring "app name is:"
-        start_index = user_input.find("app name is:") + len("app name is:")
-
-        # Find the end of the app name
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app name
-        app_name = user_input[start_index:end_index].strip()
-
-        return app_name
-
-    def extract_app_description(self, user_input: str) -> str:
-        """
-        Extracts the app description from the user's input.
-        """
-
-        # Find the substring "app description is:"
-        start_index = user_input.find("app description is:") + len("app description is:")
-
-        # Find the end of the app description
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app description
-        app_description = user_input[start_index:end_index].strip()
-
-        return app_description
-
-    def extract_app_features(self, user_input: str) -> List[str]:
-        """
-        Extracts the app features from the user's input.
-        """
-
-        # Find the substring "app features are:"
-        start_index = user_input.find("app features are:") + len("app features are:")
-
-        # Find the end of the app features
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app features
-        app_features_str = user_input[start_index:end_index].strip()
-
-        # Split the app features string into a list
-        app_features = app_features_str.split(", ")
-
-        return app_features
-
-    def extract_app_dependencies(self, user_input: str) -> List[str]:
-        """
-        Extracts the app dependencies from the user's input.
-        """
-
-        # Find the substring "app dependencies are:"
-        start_index = user_input.find("app dependencies are:") + len("app dependencies are:")
-
-        # Find the end of the app dependencies
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app dependencies
-        app_dependencies_str = user_input[start_index:end_index].strip()
-
-        # Split the app dependencies string into a list
-        app_dependencies = app_dependencies_str.split(", ")
-
-        return app_dependencies
-
-    def extract_app_space(self, user_input: str) -> Optional[Space]:
-        """
-        Extracts the app space from the user's input.
-        """
-
-        # Find the substring "app space is:"
-        start_index = user_input.find("app space is:") + len("app space is:")
-
-        # Find the end of the app space
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app space
-        app_space_str = user_input[start_index:end_index].strip()
-
-        # Create a Space object
-        app_space = Space(space=app_space_str)
-
-        return app_space
-
-    def extract_app_tutorial(self, user_input: str) -> Optional[Tutorial]:
-        """
-        Extracts the app tutorial from the user's input.
-        """
-
-        # Find the substring "app tutorial is:"
-        start_index = user_input.find("app tutorial is:") + len("app tutorial is:")
-
-        # Find the end of the app tutorial
-        end_index = user_input.find(".", start_index)
-
-        # Extract the app tutorial
-        app_tutorial_str = user_input[start_index:end_index].strip()
-
-        # Create a Tutorial object
-        app_tutorial = Tutorial(tutorial=app_tutorial_str)
-
-        return app_tutorial
-
-    def generate_code(self, prompt: Prompt) -> Code:
-        """
-        Generates code using the Llama model.
-        """
-
-        # Send the prompt to the Llama model
-        response = self.client(prompt.prompt)
-
-        # Extract the generated code
-        code = response["generated_text"]
-        code = code.replace("```", "")
-        code = code.replace("```", "")
-
-        # Create a Code object
-        code = Code(code=code, language="python")
-
-        return code
-
-    def generate_files(self, app_type: AppType, app_name: str, app_description: str, app_features: List[str], app_dependencies: List[str], app_space: Optional[Space] = None, app_tutorial: Optional[Tutorial] = None) -> List[File]:
-        """
-        Generates files for the application.
-        """
-
-        # Generate files based on the app type
-        files = self.prompts["generateFiles"](
-            app_type, app_name, app_description, app_features, app_dependencies, app_space, app_tutorial
-        )
-
-        return files
-
 def main():
-    """
-    Main function for the application.
-    """
-
+    """ Main function for the application. """
     # Create an agent
     agent = Agent(
         prompts={