Update app.py

app.py

@@ -21,6 +21,7 @@ from agent import (
 )
 from utils import parse_action, parse_file_content, read_python_module_structure
 from datetime import datetime
+
 now = datetime.now()
 date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
 
@@ -30,31 +31,21 @@ client = InferenceClient(
 
 ############################################
 
-
 VERBOSE = True
 MAX_HISTORY = 100
-#MODEL = "gpt-3.5-turbo"  # "gpt-4"
-
+# MODEL = "gpt-3.5-turbo"  # "gpt-4"
 
 def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-
-
-
-def run_gpt(
-    prompt_template,
-    stop_tokens,
-    max_tokens,
-    purpose,
-    **prompt_kwargs,
-):
-    seed = random.randint(1,1111111111111111)
-    print (seed)
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def run_gpt(prompt_template, stop_tokens, max_tokens, purpose, **prompt_kwargs):
+    seed = random.randint(1, 1111111111111111)
+    print(seed)
     generate_kwargs = dict(
         temperature=1.0,
         max_new_tokens=2096,
@@ -64,7 +55,6 @@ def run_gpt(
         seed=seed,
     )
 
-    
     content = PREFIX.format(
         date_time_str=date_time_str,
         purpose=purpose,
@@ -72,10 +62,9 @@ def run_gpt(
     ) + prompt_template.format(**prompt_kwargs)
     if VERBOSE:
         print(LOG_PROMPT.format(content))
-    
-    
-    #formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    #formatted_prompt = format_prompt(f'{content}', history)
+
+    # formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    # formatted_prompt = format_prompt(f'{content}', history)
 
     stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
     resp = ""
@@ -86,7 +75,6 @@ def run_gpt(
         print(LOG_RESPONSE.format(resp))
     return resp
 
-
 def compress_history(purpose, task, history, directory):
     resp = run_gpt(
         COMPRESS_HISTORY_PROMPT,
@@ -98,19 +86,18 @@ def compress_history(purpose, task, history, directory):
     )
     history = "observation: {}\n".format(resp)
     return history
-    
+
 def call_search(purpose, task, history, directory, action_input):
     print("CALLING SEARCH")
     try:
-        
         if "http" in action_input:
             if "<" in action_input:
                 action_input = action_input.strip("<")
             if ">" in action_input:
                 action_input = action_input.strip(">")
-            
+
             response = i_s(action_input)
-            #response = google(search_return)
+            # response = google(search_return)
             print(response)
             history += "observation: search result is: {}\n".format(response)
         else:
@@ -135,11 +122,10 @@ def call_main(purpose, task, history, directory, action_input):
         if line.startswith("thought: "):
             history += "{}\n".format(line)
         elif line.startswith("action: "):
-            
             action_name, action_input = parse_action(line)
-            print (f'ACTION_NAME :: {action_name}')
-            print (f'ACTION_INPUT :: {action_input}')
-            
+            print(f'ACTION_NAME :: {action_name}')
+            print(f'ACTION_INPUT :: {action_input}')
+
             history += "{}\n".format(line)
             if "COMPLETE" in action_name or "COMPLETE" in action_input:
                 task = "END"
@@ -148,12 +134,11 @@ def call_main(purpose, task, history, directory, action_input):
                 return action_name, action_input, history, task
         else:
             history += "{}\n".format(line)
-            #history += "observation: the following command did not produce any useful output: '{}', I need to check the commands syntax, or use a different command\n".format(line)
-            
-            #return action_name, action_input, history, task
-            #assert False, "unknown action: {}".format(line)
-    return "MAIN", None, history, task
+            # history += "observation: the following command did not produce any useful output: '{}', I need to check the commands syntax, or use a different command\n".format(line)
 
+            # return action_name, action_input, history, task
+            # assert False, "unknown action: {}".format(line)
+    return "MAIN", None, history, task
 
 def call_set_task(purpose, task, history, directory, action_input):
     task = run_gpt(
@@ -176,46 +161,43 @@ NAME_TO_FUNC = {
     "UPDATE-TASK": call_set_task,
     "SEARCH": call_search,
     "COMPLETE": end_fn,
-
 }
 
 def run_action(purpose, task, history, directory, action_name, action_input):
     print(f'action_name::{action_name}')
     try:
         if "RESPONSE" in action_name or "COMPLETE" in action_name:
-            action_name="COMPLETE"
-            task="END"
+            action_name = "COMPLETE"
+            task = "END"
             return action_name, "COMPLETE", history, task
-    
+
         # compress the history when it is long
         if len(history.split("\n")) > MAX_HISTORY:
             if VERBOSE:
                 print("COMPRESSING HISTORY")
             history = compress_history(purpose, task, history, directory)
         if not action_name in NAME_TO_FUNC:
-            action_name="MAIN"
+            action_name = "MAIN"
         if action_name == "" or action_name == None:
-            action_name="MAIN"
+            action_name = "MAIN"
         assert action_name in NAME_TO_FUNC
-    
+
         print("RUN: ", action_name, action_input)
         return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
     except Exception as e:
         history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
-
         return "MAIN", None, history, task
 
-def run(purpose,history):
-    
-    #print(purpose)
-    #print(hist)
-    task=None
-    directory="./"
+def run(purpose, history):
+    # print(purpose)
+    # print(hist)
+    task = None
+    directory = "./"
     if history:
-        history=str(history).strip("[]")
+        history = str(history).strip("[]")
     if not history:
         history = ""
-    
+
     action_name = "UPDATE-TASK" if task is None else "MAIN"
     action_input = None
     while True:
@@ -237,40 +219,38 @@ def run(purpose,history):
             action_input,
         )
         yield (history)
-        #yield ("",[(purpose,history)])
+        # yield ("",[(purpose,history)])
         if task == "END":
             return (history)
-            #return ("", [(purpose,history)])
-
-
+            # return ("", [(purpose,history)])
 
 ################################################
 
 def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-agents =[
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+AGENTS = [
     "WEB_DEV",
     "AI_SYSTEM_PROMPT",
     "PYTHON_CODE_DEV"
 ]
-def generate(
-        prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-    seed = random.randint(1,1111111111111111)
 
-    agent=prompts.WEB_DEV
+def generate(prompt, history, agent_name=AGENTS[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    seed = random.randint(1, 1111111111111111)
+
+    agent = prompts.WEB_DEV
     if agent_name == "WEB_DEV":
         agent = prompts.WEB_DEV
     if agent_name == "AI_SYSTEM_PROMPT":
         agent = prompts.AI_SYSTEM_PROMPT
     if agent_name == "PYTHON_CODE_DEV":
-        agent = prompts.PYTHON_CODE_DEV        
-    system_prompt=agent
+        agent = prompts.PYTHON_CODE_DEV
+    system_prompt = agent
     temperature = float(temperature)
     if temperature < 1e-2:
         temperature = 1e-2
@@ -294,14 +274,13 @@ def generate(
         yield output
     return output
 
-
-additional_inputs=[
+additional_inputs = [
     gr.Dropdown(
         label="Agents",
-        choices=[s for s in agents],
-        value=agents[0],
+        choices=[s for s in AGENTS],
+        value=AGENTS[0],
         interactive=True,
-        ),
+    ),
     gr.Textbox(
         label="System Prompt",
         max_lines=1,
@@ -314,143 +293,48 @@ additional_inputs=[
         maximum=1.0,
         step=0.05,
         interactive=True,
-        info="Higher values produce more diverse outputs",
+        info="Higher values generate more diverse outputs.",
     ),
-
     gr.Slider(
-        label="Max new tokens",
-        value=1048*10,
-        minimum=0,
-        maximum=1048*10,
+        label="Max New Tokens",
+        value=2048,
+        minimum=64,
+        maximum=4096,
         step=64,
         interactive=True,
-        info="The maximum numbers of new tokens",
+        info="The maximum number of new tokens to generate.",
     ),
     gr.Slider(
-        label="Top-p (nucleus sampling)",
+        label="Top-p (Nucleus Sampling)",
         value=0.90,
         minimum=0.0,
         maximum=1,
         step=0.05,
         interactive=True,
-        info="Higher values sample more low-probability tokens",
+        info="Higher values sample more low-probability tokens.",
     ),
     gr.Slider(
-        label="Repetition penalty",
+        label="Repetition Penalty",
         value=1.2,
         minimum=1.0,
         maximum=2.0,
         step=0.05,
         interactive=True,
-        info="Penalize repeated tokens",
-    ),
-
-
+        info="Penalize repeated tokens.",
+    )
 ]
 
-examples=[
-    ["Create a basic Python web app using Flask.", None, None, None, None, None, ],
-    ["Build a simple Streamlit app to display a data visualization.", None, None, None, None, None, ],
-    ["I need a Gradio interface for a machine learning model that takes an image as input and outputs a classification.", None, None, None, None, None, ],
-    ["Generate a Python script to scrape data from a website.", None, None, None, None, None, ],
-    ["I'm building a React app. How can I use Axios to make API calls?", None, None, None, None, None, ],
-    ["Write a Python function to read data from a CSV file.", None, None, None, None, None, ],
-    ["I want to deploy my Flask app to Heroku.", None, None, None, None, None, ],
-    ["Explain the difference between Git and GitHub.", None, None, None, None, None, ],
-    ["How can I use Docker to containerize my Python app?", None, None, None, None, None, ],
-    ["I need a simple API endpoint for my web app using Flask.", None, None, None, None, None, ],
-    ["Create a function in Python to calculate the factorial of a number.", None, None, None, None, None, ],
-]
+customCSS = """
+#component-7 { 
+  height: 1600px; 
+  flex-grow: 4;
+}
+"""
+
+with gr.Blocks(theme='ParityError/Interstellar') as demo:
+    gr.ChatInterface(
+        generate,
+        additional_inputs=additional_inputs,
+    )
 
-'''
-gr.ChatInterface(
-fn=run,
-chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
-examples=examples,
-concurrency_limit=20,
-with gr.Blocks() as ifacea:
-    gr.HTML("""TEST""")
-ifacea.launch()
-).launch()
-with gr.Blocks() as iface:
-    #chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    chatbot=gr.Chatbot()
-    msg = gr.Textbox()
-    with gr.Row():
-        submit_b = gr.Button()
-        clear = gr.ClearButton([msg, chatbot])
-    submit_b.click(run, [msg,chatbot],[msg,chatbot])
-    msg.submit(run, [msg, chatbot], [msg, chatbot])
-iface.launch()
-'''
-gr.ChatInterface(
-    fn=run,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api=False)
-
-
-Implementation of Next Steps:
-
-Terminal Integration:
-
-Install Libraries: Install either streamlit-terminal or gradio-terminal depending on your chosen framework.
-Integrate the Terminal: Use the library's functions to embed a terminal component within your Streamlit or Gradio app.
-Capture Input: Capture the user's input from the terminal and pass it to your command execution function.
-Display Output: Display the output of the terminal commands, including both standard output and errors.
-Code Generation:
-
-LLM Selection: Choose a Hugging Face Transformer model that is suitable for code generation (e.g., google/flan-t5-xl, Salesforce/codet5-base, microsoft/CodeGPT-small).
-Prompt Engineering: Develop effective prompts for the LLM to generate code based on natural language instructions.
-Code Translation Function: Create a function that takes natural language input, passes it to the LLM with the appropriate prompt, and then returns the generated code.
-Code Correction: You can explore ways to automatically correct code errors, perhaps using a combination of syntax checking and LLM assistance.
-Workspace Explorer:
-
-Streamlit or Gradio Filesystem Access: Use Streamlit's st.file_uploader or Gradio's gr.File component to allow users to upload files.
-File Management: Implement functions to create, edit, and delete files and directories within the workspace.
-Display Files: Use Streamlit's st.code or Gradio's gr.File component to display the contents of files in the workspace.
-Directory Structure: Display the directory structure of the workspace using a tree-like representation.
-Dependency Management:
-
-Package Installation: Create a function that takes a package name as input, installs it using pip, and updates the requirements.txt file.
-Workspace Population: Develop a function to create files and directories in the workspace based on installed packages.
-Application Build and Launch:
-
-Build Logic: Develop a function to build the web app based on the user's code and dependencies.
-Launch Functionality: Implement a mechanism to launch the built app.
-Error Correction: Identify and correct errors during the build and launch process.
-Automated Assistance: Provide automated assistance during the build and launch process, with a gradient slider to adjust the level of user override.
-
-Recommendations, Enhancements, Optimizations, and Workflow:
-
-1. LLM Selection for Code Generation:
-    *  **Google/Flan-T5-XL:** Excellent for code generation, particularly for Python.
-    *  **Salesforce/CodeT5-Base:**  Strong for code generation, with a focus on code summarization and translation.
-    *  **Microsoft/CodeGPT-Small:**  A smaller model that is suitable for code generation tasks, especially if you have limited computational resources.
-
-2. Prompt Engineering for Code Generation:
-    *  **Contextual Prompts:** Provide the LLM with as much context as possible, including the desired programming language, libraries, and any specific requirements.
-    *  **Code Snippets:**  If possible, include code snippets as part of the prompt to guide the LLM's code generation.
-    *  **Iterative Refinement:**  Use iterative prompting to refine the generated code.  Start with a basic prompt and then provide feedback to the LLM to improve the code.
-
-3. Workspace Exploration:
-    *   **Tree-Like View:**  Use a tree-like representation to display the workspace's directory structure.
-    *   **Search Functionality:** Implement a search bar to allow users to quickly find specific files or directories.
-    *   **Code Highlighting:**  Provide code highlighting for files in the workspace to improve readability.
-
-4. Dependency Management:
-    *   **Virtual Environments:** Use virtual environments to isolate project dependencies and prevent conflicts.
-    *   **Automatic Updates:**  Implement a mechanism to automatically update dependencies when new versions are available.
-    *   **Dependency Locking:**  Use tools like `pip-tools` or `poetry` to lock dependencies to specific versions, ensuring consistent builds.
-
-5. Application Build and Launch:
-    *   **Build Tool Integration:**  Consider integrating a build tool like `poetry` or `pipenv` into your workflow to automate the build process.
-    *   **Containerization:** Containerize the app using Docker to ensure consistent deployments across different environments.
-    *   **Deployment Automation:**  Explore tools like `Heroku`, `AWS Elastic Beanstalk`, or `Google App Engine` to automate the deployment process.
-
-6. Automated Assistance:
-    *   **Error Detection and Correction:**  Implement a system that can detect common coding errors and suggest corrections.
-    *   **Code Completion:**  Use an LLM to provide code completion suggestions as the user types.
+demo.queue().launch(debug=True)