Spaces:
Sleeping
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Update app.py
Browse files
app.py
CHANGED
@@ -1,11 +1,15 @@
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import os
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import subprocess
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import random
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-
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import gradio as gr
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from safe_search import safe_search
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from i_search import google
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from agent import (
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ACTION_PROMPT,
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ADD_PROMPT,
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TASK_PROMPT,
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UNDERSTAND_TEST_RESULTS_PROMPT,
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)
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from utils import parse_action, parse_file_content, read_python_module_structure
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from datetime import datetime
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now = datetime.now()
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date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
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client = InferenceClient(
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"mistralai/Mixtral-8x7B-Instruct-v0.1"
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)
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############################################
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VERBOSE = True
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MAX_HISTORY = 100
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#MODEL = "gpt-3.5-turbo" # "gpt-4"
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prompt = "<s>"
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for user_prompt, bot_response in history:
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prompt += f"[INST] {user_prompt} [/INST]"
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prompt += f" {bot_response}</s> "
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prompt += f"[INST] {message} [/INST]"
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return prompt
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def run_gpt(
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prompt_template,
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stop_tokens,
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max_tokens,
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purpose,
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**prompt_kwargs,
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):
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seed = random.randint(1,1111111111111111)
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print (seed)
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generate_kwargs = dict(
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temperature=1.0,
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max_new_tokens=2096,
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top_p=0.99,
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repetition_penalty=1.0,
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do_sample=True,
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seed=seed,
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)
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content = PREFIX.format(
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date_time_str=date_time_str,
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purpose=purpose,
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safe_search=safe_search,
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) + prompt_template.format(**prompt_kwargs)
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if VERBOSE:
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print(LOG_PROMPT.format(content))
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#formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
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#formatted_prompt = format_prompt(f'{content}', history)
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stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
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resp = ""
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for response in stream:
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resp += response.token.text
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if VERBOSE:
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print(LOG_RESPONSE.format(resp))
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return resp
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else:
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NAME_TO_FUNC = {
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"MAIN": call_main,
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"UPDATE-TASK": call_set_task,
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"SEARCH": call_search,
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"COMPLETE": end_fn,
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}
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def
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print(
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try:
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history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
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return "MAIN", None, history, task
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def run(purpose, history):
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#print(purpose)
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#print(hist)
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task=None
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directory="./"
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if history:
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history=str(history).strip("[]")
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if not history:
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history = ""
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action_name = "UPDATE-TASK" if task is None else "MAIN"
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action_input = None
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while True:
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print("")
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print("")
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print("---")
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print("purpose:", purpose)
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print("task:", task)
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print("---")
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print(history)
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print("---")
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action_name, action_input, history, task = run_action(
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purpose,
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task,
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history,
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directory,
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action_name,
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action_input,
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)
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yield (history) # Yield the updated chat history
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#yield ("",[(purpose,history)])
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if task == "END":
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return "", history # Return an empty string for the Textbox and the chat history for the Chatbot
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#return ("", [(purpose,history)])
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################################################
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def format_prompt(message, history):
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prompt = "<s>"
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for user_prompt, bot_response in history:
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prompt += f"[INST] {user_prompt} [/INST]"
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prompt += f" {bot_response}</s> "
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prompt += f"[INST] {message} [/INST]"
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return prompt
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agents =[
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"WEB_DEV",
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"AI_SYSTEM_PROMPT",
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"PYTHON_CODE_DEV"
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]
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def generate(
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prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
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):
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seed = random.randint(1,1111111111111111)
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agent=prompts.WEB_DEV
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if agent_name == "WEB_DEV":
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agent = prompts.WEB_DEV
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if agent_name == "AI_SYSTEM_PROMPT":
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agent = prompts.AI_SYSTEM_PROMPT
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if agent_name == "PYTHON_CODE_DEV":
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agent = prompts.PYTHON_CODE_DEV
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system_prompt=agent
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temperature = float(temperature)
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if temperature < 1e-2:
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temperature = 1e-2
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top_p = float(top_p)
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generate_kwargs = dict(
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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do_sample=True,
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seed=seed,
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)
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formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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output += response.token.text
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yield output
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return output
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additional_inputs=[
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gr.Dropdown(
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label="Agents",
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choices=[s for s in agents],
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value=agents[0],
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interactive=True,
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),
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gr.Textbox(
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label="System Prompt",
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max_lines=1,
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interactive=True,
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),
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gr.Slider(
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label="Temperature",
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value=0.9,
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minimum=0.0,
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maximum=1.0,
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step=0.05,
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interactive=True,
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info="Higher values produce more diverse outputs",
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),
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gr.Slider(
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label="Max new tokens",
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value=1048*10,
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minimum=0,
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maximum=1048*10,
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step=64,
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interactive=True,
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info="The maximum numbers of new tokens",
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),
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gr.Slider(
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label="Top-p (nucleus sampling)",
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value=0.90,
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minimum=0.0,
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maximum=1,
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step=0.05,
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interactive=True,
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info="Higher values sample more low-probability tokens",
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),
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gr.Slider(
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label="Repetition penalty",
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value=1.2,
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minimum=1.0,
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maximum=2.0,
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step=0.05,
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interactive=True,
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info="Penalize repeated tokens",
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),
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]
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examples=[["What are the biggest news stories today?", None, None, None, None, None, ],
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["When is the next full moon?", None, None, None, None, None, ],
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["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
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["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
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["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
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["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
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["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
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["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
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]
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'''
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gr.ChatInterface(
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fn=run,
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chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
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title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
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examples=examples,
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concurrency_limit=20,
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with gr.Blocks() as ifacea:
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gr.HTML("""TEST""")
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ifacea.launch()
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).launch()
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with gr.Blocks() as iface:
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#chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
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chatbot=gr.Chatbot()
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msg = gr.Textbox()
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with gr.Row():
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submit_b = gr.Button()
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clear = gr.ClearButton([msg, chatbot])
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submit_b.click(run, [msg,chatbot],[msg,chatbot])
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msg.submit(run, [msg, chatbot], [msg, chatbot])
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iface.launch()
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'''
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with gr.Blocks() as iface:
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chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, layout="panel")
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msg = gr.Textbox()
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with gr.Row():
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submit_b = gr.Button()
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clear = gr.ClearButton([msg, chatbot])
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submit_b.click(run, [msg,chatbot],[msg,chatbot])
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msg.submit(run, [msg, chatbot], [msg, chatbot])
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iface.launch()
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import os
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import subprocess
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import random
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import json
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from datetime import datetime
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from huggingface_hub import InferenceClient, cached_download, hf_hub_url
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import gradio as gr
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from safe_search import safe_search
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from i_search import google, i_search as i_s
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from agent import (
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ACTION_PROMPT,
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ADD_PROMPT,
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TASK_PROMPT,
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UNDERSTAND_TEST_RESULTS_PROMPT,
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)
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from utils import parse_action, parse_file_content, read_python_module_structure
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class App:
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def __init__(self):
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self.app_state = {"components": []}
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self.terminal_history = ""
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self.components_registry = {
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"Button": {
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"properties": {
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"label": "Click Me",
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"onclick": ""
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},
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"description": "A clickable button",
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"code_snippet": "gr.Button(value='{{label}}', variant='primary')"
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},
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"Text Input": {
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"properties": {
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"value": "",
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"placeholder": "Enter text"
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},
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"description": "A field for entering text",
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"code_snippet": "gr.Textbox(label='{{placeholder}}')"
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+
},
|
51 |
+
"Image": {
|
52 |
+
"properties": {
|
53 |
+
"src": "#",
|
54 |
+
"alt": "Image"
|
55 |
+
},
|
56 |
+
"description": "Displays an image",
|
57 |
+
"code_snippet": "gr.Image(label='{{alt}}')"
|
58 |
+
},
|
59 |
+
"Dropdown": {
|
60 |
+
"properties": {
|
61 |
+
"choices": ["Option 1", "Option 2"],
|
62 |
+
"value": ""
|
63 |
+
},
|
64 |
+
"description": "A dropdown menu for selecting options",
|
65 |
+
"code_snippet": "gr.Dropdown(choices={{choices}}, label='Dropdown')"
|
66 |
+
}
|
67 |
+
}
|
68 |
+
self.nlp_model_names = [
|
69 |
+
"google/flan-t5-small",
|
70 |
+
"Qwen/CodeQwen1.5-7B-Chat-GGUF",
|
71 |
+
"bartowski/Codestral-22B-v0.1-GGUF",
|
72 |
+
"bartowski/AutoCoder-GGUF"
|
73 |
+
]
|
74 |
+
self.nlp_models = []
|
75 |
+
self.initialize_nlp_models()
|
76 |
+
|
77 |
+
def initialize_nlp_models(self):
|
78 |
+
for nlp_model_name in self.nlp_model_names:
|
79 |
+
try:
|
80 |
+
cached_download(hf_hub_url(nlp_model_name, revision="main"))
|
81 |
+
self.nlp_models.append(InferenceClient(nlp_model_name))
|
82 |
+
except:
|
83 |
+
self.nlp_models.append(None)
|
84 |
+
|
85 |
+
def get_nlp_response(self, input_text, model_index):
|
86 |
+
if self.nlp_models[model_index]:
|
87 |
+
response = self.nlp_models[model_index].text_generation(input_text)
|
88 |
+
return response.generated_text
|
89 |
else:
|
90 |
+
return "NLP model not available."
|
91 |
+
|
92 |
+
class Component:
|
93 |
+
def __init__(self, type, properties=None, id=None):
|
94 |
+
self.id = id or random.randint(1000, 9999)
|
95 |
+
self.type = type
|
96 |
+
self.properties = properties or self.components_registry[type]["properties"].copy()
|
97 |
+
|
98 |
+
def to_dict(self):
|
99 |
+
return {
|
100 |
+
"id": self.id,
|
101 |
+
"type": self.type,
|
102 |
+
"properties": self.properties,
|
103 |
+
}
|
104 |
+
|
105 |
+
def render(self):
|
106 |
+
if self.type == "Dropdown":
|
107 |
+
self.properties["choices"] = str(self.properties["choices"]).replace("[", "").replace("]", "").replace("'", "")
|
108 |
+
return self.components_registry[self.type]["code_snippet"].format(**self.properties)
|
109 |
+
|
110 |
+
def update_app_canvas(self):
|
111 |
+
components_html = "".join([f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in self.app_state["components"]])
|
112 |
+
return components_html
|
113 |
+
|
114 |
+
def add_component(self, component_type):
|
115 |
+
if component_type in self.components_registry:
|
116 |
+
new_component = self.Component(component_type)
|
117 |
+
self.app_state["components"].append(new_component.to_dict())
|
118 |
+
return (
|
119 |
+
self.update_app_canvas(),
|
120 |
+
f"System: Added component: {component_type}\n",
|
121 |
+
)
|
122 |
+
else:
|
123 |
+
return None, f"Error: Invalid component type: {component_type}\n"
|
124 |
+
|
125 |
+
def run_terminal_command(self, command, history):
|
126 |
+
output = ""
|
127 |
+
try:
|
128 |
+
if command.startswith("add "):
|
129 |
+
component_type = command.split("add ")[1]
|
130 |
+
return self.add_component(component_type)
|
131 |
+
elif command.startswith("search "):
|
132 |
+
query = command.split("search ")[1]
|
133 |
+
return google(query)
|
134 |
+
elif command.startswith("i search "):
|
135 |
+
query = command.split("i search ")[1]
|
136 |
+
return i_s(query)
|
137 |
+
elif command.startswith("safe search "):
|
138 |
+
query = command.split("safesearch ")[1]
|
139 |
+
return safe_search(query)
|
140 |
+
elif command.startswith("read "):
|
141 |
+
file_path = command.split("read ")[1]
|
142 |
+
return parse_file_content(file_path)
|
143 |
+
elif command == "task":
|
144 |
+
return TASK_PROMPT
|
145 |
+
elif command == "modify":
|
146 |
+
return MODIFY_PROMPT
|
147 |
+
elif command == "log":
|
148 |
+
return LOG_PROMPT
|
149 |
+
elif command.startswith("understand test results "):
|
150 |
+
test_results = command.split("understand test results ")[1]
|
151 |
+
return self.understand_test_results(test_results)
|
152 |
+
elif command.startswith("compress history"):
|
153 |
+
return self.compress_history(history)
|
154 |
+
elif command == "help":
|
155 |
+
return self.get_help_message()
|
156 |
+
elif command == "exit":
|
157 |
+
exit()
|
158 |
else:
|
159 |
+
output = subprocess.check_output(command, shell=True).decode("utf-8")
|
160 |
+
except Exception as e:
|
161 |
+
output = str(e)
|
162 |
+
return output or "No output\n"
|
163 |
+
|
164 |
+
def compress_history(self, history):
|
165 |
+
compressed_history = ""
|
166 |
+
lines = history.strip().split("\n")
|
167 |
+
for line in lines:
|
168 |
+
if not line.strip().startswith("#"):
|
169 |
+
compressed_history += line + "\n"
|
170 |
+
return compressed_history
|
171 |
+
|
172 |
+
def understand_test_results(self, test_results):
|
173 |
+
# Logic to understand test results
|
174 |
+
return UNDERSTAND_TEST_RESULTS_PROMPT
|
175 |
+
|
176 |
+
def get_help_message(self):
|
177 |
+
return """
|
178 |
+
Available commands:
|
179 |
+
- add [component_type]: Add a component to the app canvas
|
180 |
+
- search [query]: Perform a Google search
|
181 |
+
- i search [query]: Perform an intelligent search
|
182 |
+
- safe search [query]: Perform a safe search
|
183 |
+
- read [file_path]: Read and parse the content of a Python module
|
184 |
+
- task: Prompt for a task to perform
|
185 |
+
- modify: Prompt to modify a component property
|
186 |
+
- log: Prompt to log a response
|
187 |
+
- understand test results [test_results]: Understand test results
|
188 |
+
- compress history: Compress the terminal history by removing comments
|
189 |
+
- help: Show this help message
|
190 |
+
- exit: Exit the program
|
191 |
+
"""
|
192 |
+
|
193 |
+
def process_input(self, input_text):
|
194 |
+
if input_text.strip().startswith("/"):
|
195 |
+
command = input_text.strip().lstrip("/")
|
196 |
+
output = self.run_terminal_command(command, self.terminal_history)
|
197 |
+
self.terminal_history += f"{input_text}\n{output}\n"
|
198 |
+
return output
|
199 |
else:
|
200 |
+
model_index = random.randint(0, len(self.nlp_models)-1)
|
201 |
+
response = self.get_nlp_response(input_text, model_index)
|
202 |
+
component_id, action, property_name, property_value = parse_action(response)
|
203 |
+
if component_id:
|
204 |
+
component = next((comp for comp in self.app_state["components"] if comp["id"] == component_id), None)
|
205 |
+
if component:
|
206 |
+
if action == "update":
|
207 |
+
component["properties"][property_name] = property_value
|
208 |
+
return (
|
209 |
+
self.update_app_canvas(),
|
210 |
+
f"System: Updated property '{property_name}' of component with ID {component_id}\n",
|
211 |
+
)
|
212 |
+
elif action == "remove":
|
213 |
+
self.app_state["components"].remove(component)
|
214 |
+
return (
|
215 |
+
self.update_app_canvas(),
|
216 |
+
f"System: Removed component with ID {component_id}\n",
|
217 |
+
)
|
218 |
+
else:
|
219 |
+
return None, f"Error: Invalid action: {action}\n"
|
220 |
+
else:
|
221 |
+
return None, f"Error: Component with ID {component_id} not found\n"
|
222 |
+
else:
|
223 |
+
return None, f"Error: Failed to parse action from NLP response\n"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
224 |
|
225 |
+
def run(self):
|
226 |
+
print("Welcome to the Python App Builder!")
|
227 |
+
print("Type 'help' to see the available commands.")
|
228 |
+
print("-" * 50)
|
229 |
try:
|
230 |
+
while True:
|
231 |
+
try:
|
232 |
+
input_text = input("Enter input: ")
|
233 |
+
except EOFError:
|
234 |
+
print("Error: Input reading interrupted. Please provide valid input.")
|
235 |
+
continue
|
236 |
+
|
237 |
+
output, system_message = self.process_input(input_text)
|
238 |
+
if output:
|
239 |
+
print(output)
|
240 |
+
if system_message:
|
241 |
+
print(system_message)
|
242 |
+
except KeyboardInterrupt:
|
243 |
+
print("\nApplication stopped by user.")
|
244 |
+
|
245 |
+
|
246 |
+
if __name__ == "__main__":
|
247 |
+
app = App()
|
248 |
+
app.run()
|
|
|
|
|
|
|
|
|
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