Update app.py

app.py

@@ -1,25 +1,7 @@
 import gradio as gr
 import os
-import subprocess
 import random
 from huggingface_hub import InferenceClient
-from safe_search import safe_search
-from i_search import google
-from i_search import i_search as i_s
-from agent import (
-    ACTION_PROMPT,
-    ADD_PROMPT,
-    COMPRESS_HISTORY_PROMPT,
-    LOG_PROMPT,
-    LOG_RESPONSE,
-    MODIFY_PROMPT,
-    PREFIX,
-    SEARCH_QUERY,
-    READ_PROMPT,
-    TASK_PROMPT,
-    UNDERSTAND_TEST_RESULTS_PROMPT,
-)
-from utils import parse_action, parse_file_content, read_python_module_structure
 from datetime import datetime
 import yaml
 import logging
@@ -140,8 +122,6 @@ date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
 
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
-############################################
-
 VERBOSE = True
 MAX_HISTORY = 125
 
@@ -153,209 +133,24 @@ def format_prompt(message, history):
     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,
-        top_p=0.99,
-        repetition_penalty=1.7,
-        do_sample=True,
-        seed=seed,
-    )
-
-    content = PREFIX.format(
-        date_time_str=date_time_str,
-        purpose=purpose,
-        safe_search=safe_search,
-    ) + prompt_template.format(**prompt_kwargs)
-    
-    if VERBOSE:
-        print(LOG_PROMPT.format(content))
-
-    stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    resp = ""
-    for response in stream:
-        resp += response.token.text
-
-    if VERBOSE:
-        print(LOG_RESPONSE.format(resp))
-    
-    return resp
-
-def compress_history(purpose, task, history, directory):
-    resp = run_gpt(
-        COMPRESS_HISTORY_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=5096,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    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)
-            print(response)
-            history += "observation: search result is: {}\n".format(response)
-        else:
-            history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
-    except Exception as e:
-        history += "observation: {}'\n".format(e)
-    
-    return "MAIN", None, history, task
-
-def call_main(purpose, task, history, directory, action_input):
-    resp = run_gpt(
-        ACTION_PROMPT,
-        stop_tokens=["observation:", "task:", "action:", "thought:"],
-        max_tokens=5096,
-        purpose=purpose,
-        task=task,
-        history=history,
-    )
-    
-    lines = resp.strip().strip("\n").split("\n")
-    for line in lines:
-        if line == "":
-            continue
-        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}')
-            history += "{}\n".format(line)
-            if "COMPLETE" in action_name or "COMPLETE" in action_input:
-                task = "END"
-                return action_name, action_input, history, task
-            else:
-                return action_name, action_input, history, task
-        else:
-            history += "{}\n".format(line)
-    
-    return "MAIN", None, history, task
-
-def call_set_task(purpose, task, history, directory, action_input):
-    task = run_gpt(
-        TASK_PROMPT,
-        stop_tokens=[],
-        max_tokens=2048,
-        purpose=purpose,
-        task=task,
-        history=history,
-    ).strip("\n")
-    
-    history += "observation: task has been updated to: {}\n".format(task)
-    return "MAIN", None, history, task
-
-def end_fn(purpose, task, history, directory, action_input):
-    task = "END"
-    return "COMPLETE", "COMPLETE", history, task
-
-NAME_TO_FUNC = {
-    "MAIN": call_main,
-    "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"
-            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 action_name not in NAME_TO_FUNC:
-            action_name = "MAIN"
-        if action_name == "" or action_name is None:
-            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):
-    task = None
-    directory = "./"
-    if history:
-        history = str(history).strip("[]")
-    if not history:
-        history = ""
-
-    action_name = "UPDATE-TASK" if task is None else "MAIN"
-    action_input = None
-    while True:
-        print("")
-        print("")
-        print("---")
-        print("purpose:", purpose)
-        print("task:", task)
-        print("---")
-        print(history)
-        print("---")
-
-        action_name, action_input, history, task = run_action(
-            purpose,
-            task,
-            history,
-            directory,
-            action_name,
-            action_input,
-        )
-        
-        yield history
-        
-        if task == "END":
-            return history
-
-################################################
-
 agents = [
     "WEB_DEV",
     "AI_SYSTEM_PROMPT",
-    "PYTHON_CODE_DEV"
+    "PYTHON_CODE_DEV",
+    "CODE_GENERATION",
+    "CODE_INTERPRETATION",
+    "CODE_TRANSLATION",
+    "CODE_IMPLEMENTATION"
 ]
 
 def generate(
         prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.7,
 ):
     seed = random.randint(1, 1111111111111111)
-    agent = custom_prompts["WEB_DEV"]
-
-    if agent_name == "WEB_DEV":
-        agent = custom_prompts["WEB_DEV"]
-    elif agent_name == "AI_SYSTEM_PROMPT":
-        agent = custom_prompts["AI_SYSTEM_PROMPT"]
-    elif agent_name == "PYTHON_CODE_DEV":
-        agent = custom_prompts["PYTHON_CODE_DEV"]
+    agent = custom_prompts[agent_name]
     
-    system_prompt = agent
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
+    system_prompt = agent if sys_prompt == "" else sys_prompt
+    temperature = max(float(temperature), 1e-2)
     top_p = float(top_p)
 
     generate_kwargs = dict(
@@ -367,135 +162,45 @@ def generate(
         seed=seed,
     )
 
-    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    output = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, return_full_text=True)
+    formatted_prompt = format_prompt(f"{system_prompt}\n\n{prompt}", history)
+    output = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, return_full_text=False)
     
     return output
 
-# Define input and output components
-with gr.Blocks() as iface:
-    # Input components
-    input_text = gr.Textbox(label="Input Text")
-    # Other input components...
-
-    # Output components
-    output_text = gr.Textbox(label="Output Text")
-    # Other output components...
-
-    # Specify inputs and events
-    inputs = [input_text, agents]  # List of input components
-    events = [output_text, agents]  # List of output components
-
-    iface.load(inputs, events)
-    def log_messages(inputs, outputs):
-        logger.info(f'Input: {inputs}, Output: {outputs}')
-        
-@gr.Interface.load(inputs, agents)
-def log_messages(inputs, outputs):
-    logger.info(f'Input: {inputs}, Output: {outputs}')
-
-@gr.Interface.load(inputs, events)
-def log_messages(inputs, outputs):
-    logger.info(f'Input: {inputs}, Output: {outputs}')
-
 def update_sys_prompt(agent):
-    global SYSTEM_PROMPT
-    SYSTEM_PROMPT = globals()[agent]
+    return custom_prompts[agent]
 
 def get_helpful_tip(agent):
-    if agent == 'WEB_DEV':
-        return "Provide information related to Web Development tasks."
-    elif agent == 'AI_SYSTEM_PROMPT':
-        return "Update the system instructions for the assistant here."
-    elif agent == 'PYTHON_CODE_DEV':
-        return "Describe what you want me to help you with regarding Python coding tasks."
-    elif agent == 'CODE_GENERATION':
-        return "Provide requirements for the code you want me to generate."
-    elif agent == 'CODE_INTERPRETATION':
-        return "Share the code you want me to analyze and explain."
-    elif agent == 'CODE_TRANSLATION':
-        return "Specify the source and target programming languages, and provide the code you want me to translate."
-    elif agent == 'CODE_IMPLEMENTATION':
-        return "Provide the code or requirements you want me to implement in a production-ready environment."
+    tips = {
+        'WEB_DEV': "Provide information related to Web Development tasks.",
+        'AI_SYSTEM_PROMPT': "Update the system instructions for the assistant here.",
+        'PYTHON_CODE_DEV': "Describe what you want me to help you with regarding Python coding tasks.",
+        'CODE_GENERATION': "Provide requirements for the code you want me to generate.",
+        'CODE_INTERPRETATION': "Share the code you want me to analyze and explain.",
+        'CODE_TRANSLATION': "Specify the source and target programming languages, and provide the code you want me to translate.",
+        'CODE_IMPLEMENTATION': "Provide the code or requirements you want me to implement in a production-ready environment."
+    }
+    return tips.get(agent, "Select an agent to get started.")
 
 def chat_interface(prompt, history, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty):
     generated_text = generate(prompt, history, agent_name, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty)
-    chatbot_response = [(prompt, generated_text)]
-    return chatbot_response, ""  # Return the chatbot response and an empty string for the message textbox
-
-additional_inputs = [
-    gr.Dropdown(
-        label="Agents",
-        choices=[s for s in agents],
-        value=agents[0],
-        interactive=True,
-    ),
-    gr.Textbox(
-        label="System Prompt",
-        max_lines=1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Temperature",
-        value=0.9,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-    gr.Slider(
-        label="Max new tokens",
-        value=1048 * 10,
-        minimum=0,
-        maximum=1048 * 10,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        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",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.2,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    ),
-]
+    history.append((prompt, generated_text))
+    return history, ""
+
+def log_messages(*args):
+    logger.info(f'Input: {args}')
 
 examples = [
-    ["Based on previous interactions, generate an interactive preview of the user's requested application.", None, None, None, None, None],
-    ["Utilize the relevant code snippets and components from previous interactions.", None, None, None, None, None],
-    ["Assemble a working demo that showcases the core functionality of the application.", None, None, None, None, None],
-    ["Present the demo in an interactive environment within the Gradio interface.", None, None, None, None, None],
-    ["Allow the user to explore and interact with the demo to test its features.", None, None, None, None, None],
-    ["Gather feedback from the user about the demo and potential improvements.", None, None, None, None, None],
-    ["If the user approves of the app's running state, provide a bash script that will automate all aspects of a local run and a docker image for ease-of-launch in addition to the huggingface-ready app.py with all functions and GUI, and the requirements.txt file comprised of all required libraries and packages the application is dependent on, avoiding OpenAI API at all points since we only use Hugging Face transformers, models, agents, libraries, and API.", None, None, None, None, None],
+    ["Based on previous interactions, generate an interactive preview of the user's requested application.", "WEB_DEV", "", 0.9, 1024, 0.95, 1.2],
+    ["Utilize the relevant code snippets and components from previous interactions.", "PYTHON_CODE_DEV", "", 0.9, 1024, 0.95, 1.2],
+    ["Assemble a working demo that showcases the core functionality of the application.", "CODE_IMPLEMENTATION", "", 0.9, 1024, 0.95, 1.2],
+    ["Present the demo in an interactive environment within the Gradio interface.", "WEB_DEV", "", 0.9, 1024, 0.95, 1.2],
+    ["Allow the user to explore and interact with the demo to test its features.", "CODE_GENERATION", "", 0.9, 1024, 0.95, 1.2],
+    ["Gather feedback from the user about the demo and potential improvements.", "AI_SYSTEM_PROMPT", "", 0.9, 1024, 0.95, 1.2],
+    ["If the user approves of the app's running state, provide a bash script that will automate all aspects of a local run and a docker image for ease-of-launch in addition to the huggingface-ready app.py with all functions and GUI, and the requirements.txt file comprised of all required libraries and packages the application is dependent on, avoiding OpenAI API at all points since we only use Hugging Face transformers, models, agents, libraries, and API.", "CODE_IMPLEMENTATION", "", 0.9, 2048, 0.95, 1.2],
 ]
 
-def create_interface():
-    with gr.Blocks() as iface:
-        gr.ChatInterface(
-            fn=generate,
-            title="Fragmixt\nAgents With Agents,\nSurf With a Purpose",
-            examples=examples,
-            additional_inputs=additional_inputs,
-        )
-    return iface
-
-iface = gr.Blocks()
-
-with iface:
+with gr.Blocks() as iface:
     gr.Markdown("# Fragmixt\nAgents With Agents,\nSurf With a Purpose")
     
     chatbot = gr.Chatbot()
@@ -505,10 +210,12 @@ with iface:
     agent_dropdown = gr.Dropdown(label="Agents", choices=agents, value=agents[0])
     sys_prompt = gr.Textbox(label="System Prompt", max_lines=1)
     temperature = gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-    max_new_tokens = gr.Slider(label="Max new tokens", value=1048 * 10, minimum=0, maximum=1048 * 10, step=64)
+    max_new_tokens = gr.Slider(label="Max new tokens", value=1048, minimum=0, maximum=2048, step=64)
     top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05)
     repetition_penalty = gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05)
 
+    helpful_tip = gr.Markdown()
+
     msg.submit(chat_interface, 
                [msg, chatbot, agent_dropdown, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty], 
                [chatbot, msg])
@@ -516,7 +223,11 @@ with iface:
 
     gr.Examples(examples, [msg, agent_dropdown, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty])
 
-    agent_dropdown.change(fn=get_helpful_tip, inputs=agent_dropdown, outputs=gr.components.Markdown())
+    agent_dropdown.change(fn=get_helpful_tip, inputs=agent_dropdown, outputs=helpful_tip)
     agent_dropdown.change(fn=update_sys_prompt, inputs=agent_dropdown, outputs=sys_prompt)
 
-iface.launch()
+    for component in [msg, agent_dropdown, sys_prompt, temperature, max_new_tokens, top_p, repetition_penalty]:
+        component.change(fn=log_messages, inputs=[component])
+
+if __name__ == "__main__":
+    iface.launch()