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acecalisto3
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•
1115ab9
1
Parent(s):
835d56a
Update app.py
Browse files
app.py
CHANGED
@@ -1,281 +1,456 @@
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import os
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import subprocess
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import
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from
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CURRENT_PROJECT = {} # Store project data (code, packages, etc.)
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MODEL_OPTIONS = {
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"CodeQwen": "Qwen/CodeQwen1.5-7B-Chat-GGUF",
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"Codestral": "bartowski/Codestral-22B-v0.1-GGUF",
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"AutoCoder": "bartowski/AutoCoder-GGUF",
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}
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MODEL_FILENAMES = {
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"CodeQwen": "codeqwen-1_5-7b-chat-q6_k.gguf",
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"Codestral": "Codestral-22B-v0.1-Q6_K.gguf",
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"AutoCoder": "AutoCoder-Q6_K.gguf",
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}
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HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
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PROJECT_ROOT = "projects"
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AGENT_DIRECTORY = "agents"
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# Global state to manage communication between Tool Box and Workspace Chat App
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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if 'terminal_history' not in st.session_state:
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st.session_state.terminal_history = []
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if 'workspace_projects' not in st.session_state:
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st.session_state.workspace_projects = {}
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if 'available_agents' not in st.session_state:
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st.session_state.available_agents = []
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if 'current_state' not in st.session_state:
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st.session_state.current_state = {
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'toolbox': {},
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'workspace_chat': {}
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}
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# --- Load NLP Pipelines ---
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classifier = pipeline("text-classification", model="facebook/bart-large-mnli")
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# --- Load the model and tokenizer ---
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model = AutoModelForCausalLM.from_pretrained(
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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use_auth_token=os.environ.get("huggingface_token")
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try:
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else:
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return function_list, f"Interface for `{package_name}` created."
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except Exception as e:
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while True:
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import os
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import subprocess
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import random
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from huggingface_hub import InferenceClient
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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 i_search import 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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COMPRESS_HISTORY_PROMPT,
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LOG_PROMPT,
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LOG_RESPONSE,
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MODIFY_PROMPT,
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PREFIX,
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SEARCH_QUERY,
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READ_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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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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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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def compress_history(purpose, task, history, directory):
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resp = run_gpt(
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COMPRESS_HISTORY_PROMPT,
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stop_tokens=["observation:", "task:", "action:", "thought:"],
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max_tokens=512,
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purpose=purpose,
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task=task,
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history=history,
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)
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history = "observation: {}\n".format(resp)
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return history
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+
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def call_search(purpose, task, history, directory, action_input):
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print("CALLING SEARCH")
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try:
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if "http" in action_input:
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if "<" in action_input:
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action_input = action_input.strip("<")
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if ">" in action_input:
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action_input = action_input.strip(">")
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response = i_s(action_input)
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#response = google(search_return)
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print(response)
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history += "observation: search result is: {}\n".format(response)
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else:
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history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
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except Exception as e:
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history += "observation: {}'\n".format(e)
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return "MAIN", None, history, task
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def call_main(purpose, task, history, directory, action_input):
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resp = run_gpt(
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ACTION_PROMPT,
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stop_tokens=["observation:", "task:", "action:","thought:"],
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max_tokens=2096,
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purpose=purpose,
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task=task,
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history=history,
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)
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lines = resp.strip().strip("\n").split("\n")
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for line in lines:
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if line == "":
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continue
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if line.startswith("thought: "):
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history += "{}\n".format(line)
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elif line.startswith("action: "):
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action_name, action_input = parse_action(line)
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print (f'ACTION_NAME :: {action_name}')
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print (f'ACTION_INPUT :: {action_input}')
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history += "{}\n".format(line)
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if "COMPLETE" in action_name or "COMPLETE" in action_input:
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task = "END"
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return action_name, action_input, history, task
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else:
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return action_name, action_input, history, task
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else:
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history += "{}\n".format(line)
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#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)
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#return action_name, action_input, history, task
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#assert False, "unknown action: {}".format(line)
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return "MAIN", None, history, task
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def call_set_task(purpose, task, history, directory, action_input):
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task = run_gpt(
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TASK_PROMPT,
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stop_tokens=[],
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max_tokens=64,
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purpose=purpose,
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task=task,
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history=history,
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).strip("\n")
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history += "observation: task has been updated to: {}\n".format(task)
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return "MAIN", None, history, task
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170 |
+
def end_fn(purpose, task, history, directory, action_input):
|
171 |
+
task = "END"
|
172 |
+
return "COMPLETE", "COMPLETE", history, task
|
173 |
+
|
174 |
+
NAME_TO_FUNC = {
|
175 |
+
"MAIN": call_main,
|
176 |
+
"UPDATE-TASK": call_set_task,
|
177 |
+
"SEARCH": call_search,
|
178 |
+
"COMPLETE": end_fn,
|
179 |
|
180 |
+
}
|
|
|
181 |
|
182 |
+
def run_action(purpose, task, history, directory, action_name, action_input):
|
183 |
+
print(f'action_name::{action_name}')
|
184 |
+
try:
|
185 |
+
if "RESPONSE" in action_name or "COMPLETE" in action_name:
|
186 |
+
action_name="COMPLETE"
|
187 |
+
task="END"
|
188 |
+
return action_name, "COMPLETE", history, task
|
189 |
+
|
190 |
+
# compress the history when it is long
|
191 |
+
if len(history.split("\n")) > MAX_HISTORY:
|
192 |
+
if VERBOSE:
|
193 |
+
print("COMPRESSING HISTORY")
|
194 |
+
history = compress_history(purpose, task, history, directory)
|
195 |
+
if not action_name in NAME_TO_FUNC:
|
196 |
+
action_name="MAIN"
|
197 |
+
if action_name == "" or action_name == None:
|
198 |
+
action_name="MAIN"
|
199 |
+
assert action_name in NAME_TO_FUNC
|
200 |
+
|
201 |
+
print("RUN: ", action_name, action_input)
|
202 |
+
return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
|
203 |
except Exception as e:
|
204 |
+
history += "observation: the previous command did not produce any useful output, I need to check the commands syntax, or use a different command\n"
|
205 |
|
206 |
+
return "MAIN", None, history, task
|
207 |
+
|
208 |
+
def run(purpose,history):
|
209 |
+
|
210 |
+
#print(purpose)
|
211 |
+
#print(hist)
|
212 |
+
task=None
|
213 |
+
directory="./"
|
214 |
+
if history:
|
215 |
+
history=str(history).strip("[]")
|
216 |
+
if not history:
|
217 |
+
history = ""
|
218 |
+
|
219 |
+
action_name = "UPDATE-TASK" if task is None else "MAIN"
|
220 |
+
action_input = None
|
221 |
while True:
|
222 |
+
print("")
|
223 |
+
print("")
|
224 |
+
print("---")
|
225 |
+
print("purpose:", purpose)
|
226 |
+
print("task:", task)
|
227 |
+
print("---")
|
228 |
+
print(history)
|
229 |
+
print("---")
|
230 |
+
|
231 |
+
action_name, action_input, history, task = run_action(
|
232 |
+
purpose,
|
233 |
+
task,
|
234 |
+
history,
|
235 |
+
directory,
|
236 |
+
action_name,
|
237 |
+
action_input,
|
238 |
+
)
|
239 |
+
yield (history)
|
240 |
+
#yield ("",[(purpose,history)])
|
241 |
+
if task == "END":
|
242 |
+
return (history)
|
243 |
+
#return ("", [(purpose,history)])
|
244 |
+
|
245 |
+
|
246 |
+
|
247 |
+
################################################
|
248 |
+
|
249 |
+
def format_prompt(message, history):
|
250 |
+
prompt = "<s>"
|
251 |
+
for user_prompt, bot_response in history:
|
252 |
+
prompt += f"[INST] {user_prompt} [/INST]"
|
253 |
+
prompt += f" {bot_response}</s> "
|
254 |
+
prompt += f"[INST] {message} [/INST]"
|
255 |
+
return prompt
|
256 |
+
agents =[
|
257 |
+
"WEB_DEV",
|
258 |
+
"AI_SYSTEM_PROMPT",
|
259 |
+
"PYTHON_CODE_DEV"
|
260 |
+
]
|
261 |
+
def generate(
|
262 |
+
prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
|
263 |
+
):
|
264 |
+
seed = random.randint(1,1111111111111111)
|
265 |
+
|
266 |
+
agent=prompts.WEB_DEV
|
267 |
+
if agent_name == "WEB_DEV":
|
268 |
+
agent = prompts.WEB_DEV
|
269 |
+
if agent_name == "AI_SYSTEM_PROMPT":
|
270 |
+
agent = prompts.AI_SYSTEM_PROMPT
|
271 |
+
if agent_name == "PYTHON_CODE_DEV":
|
272 |
+
agent = prompts.PYTHON_CODE_DEV
|
273 |
+
system_prompt=agent
|
274 |
+
temperature = float(temperature)
|
275 |
+
if temperature < 1e-2:
|
276 |
+
temperature = 1e-2
|
277 |
+
top_p = float(top_p)
|
278 |
+
|
279 |
+
generate_kwargs = dict(
|
280 |
+
temperature=temperature,
|
281 |
+
max_new_tokens=max_new_tokens,
|
282 |
+
top_p=top_p,
|
283 |
+
repetition_penalty=repetition_penalty,
|
284 |
+
do_sample=True,
|
285 |
+
seed=seed,
|
286 |
+
)
|
287 |
+
|
288 |
+
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
|
289 |
+
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
|
290 |
+
output = ""
|
291 |
+
|
292 |
+
for response in stream:
|
293 |
+
output += response.token.text
|
294 |
+
yield output
|
295 |
+
return output
|
296 |
+
|
297 |
+
|
298 |
+
additional_inputs=[
|
299 |
+
gr.Dropdown(
|
300 |
+
label="Agents",
|
301 |
+
choices=[s for s in agents],
|
302 |
+
value=agents[0],
|
303 |
+
interactive=True,
|
304 |
+
),
|
305 |
+
gr.Textbox(
|
306 |
+
label="System Prompt",
|
307 |
+
max_lines=1,
|
308 |
+
interactive=True,
|
309 |
+
),
|
310 |
+
gr.Slider(
|
311 |
+
label="Temperature",
|
312 |
+
value=0.9,
|
313 |
+
minimum=0.0,
|
314 |
+
maximum=1.0,
|
315 |
+
step=0.05,
|
316 |
+
interactive=True,
|
317 |
+
info="Higher values produce more diverse outputs",
|
318 |
+
),
|
319 |
+
|
320 |
+
gr.Slider(
|
321 |
+
label="Max new tokens",
|
322 |
+
value=1048*10,
|
323 |
+
minimum=0,
|
324 |
+
maximum=1048*10,
|
325 |
+
step=64,
|
326 |
+
interactive=True,
|
327 |
+
info="The maximum numbers of new tokens",
|
328 |
+
),
|
329 |
+
gr.Slider(
|
330 |
+
label="Top-p (nucleus sampling)",
|
331 |
+
value=0.90,
|
332 |
+
minimum=0.0,
|
333 |
+
maximum=1,
|
334 |
+
step=0.05,
|
335 |
+
interactive=True,
|
336 |
+
info="Higher values sample more low-probability tokens",
|
337 |
+
),
|
338 |
+
gr.Slider(
|
339 |
+
label="Repetition penalty",
|
340 |
+
value=1.2,
|
341 |
+
minimum=1.0,
|
342 |
+
maximum=2.0,
|
343 |
+
step=0.05,
|
344 |
+
interactive=True,
|
345 |
+
info="Penalize repeated tokens",
|
346 |
+
),
|
347 |
+
|
348 |
+
|
349 |
+
]
|
350 |
+
|
351 |
+
examples=[
|
352 |
+
["Create a basic Python web app using Flask.", None, None, None, None, None, ],
|
353 |
+
["Build a simple Streamlit app to display a data visualization.", None, None, None, None, None, ],
|
354 |
+
["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, ],
|
355 |
+
["Generate a Python script to scrape data from a website.", None, None, None, None, None, ],
|
356 |
+
["I'm building a React app. How can I use Axios to make API calls?", None, None, None, None, None, ],
|
357 |
+
["Write a Python function to read data from a CSV file.", None, None, None, None, None, ],
|
358 |
+
["I want to deploy my Flask app to Heroku.", None, None, None, None, None, ],
|
359 |
+
["Explain the difference between Git and GitHub.", None, None, None, None, None, ],
|
360 |
+
["How can I use Docker to containerize my Python app?", None, None, None, None, None, ],
|
361 |
+
["I need a simple API endpoint for my web app using Flask.", None, None, None, None, None, ],
|
362 |
+
["Create a function in Python to calculate the factorial of a number.", None, None, None, None, None, ],
|
363 |
+
]
|
364 |
+
|
365 |
+
'''
|
366 |
+
gr.ChatInterface(
|
367 |
+
fn=run,
|
368 |
+
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
|
369 |
+
title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
|
370 |
+
examples=examples,
|
371 |
+
concurrency_limit=20,
|
372 |
+
with gr.Blocks() as ifacea:
|
373 |
+
gr.HTML("""TEST""")
|
374 |
+
ifacea.launch()
|
375 |
+
).launch()
|
376 |
+
with gr.Blocks() as iface:
|
377 |
+
#chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
|
378 |
+
chatbot=gr.Chatbot()
|
379 |
+
msg = gr.Textbox()
|
380 |
+
with gr.Row():
|
381 |
+
submit_b = gr.Button()
|
382 |
+
clear = gr.ClearButton([msg, chatbot])
|
383 |
+
submit_b.click(run, [msg,chatbot],[msg,chatbot])
|
384 |
+
msg.submit(run, [msg, chatbot], [msg, chatbot])
|
385 |
+
iface.launch()
|
386 |
+
'''
|
387 |
+
gr.ChatInterface(
|
388 |
+
fn=run,
|
389 |
+
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
|
390 |
+
title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
|
391 |
+
examples=examples,
|
392 |
+
concurrency_limit=20,
|
393 |
+
).launch(show_api=False)
|
394 |
+
|
395 |
+
|
396 |
+
Implementation of Next Steps:
|
397 |
+
|
398 |
+
Terminal Integration:
|
399 |
+
|
400 |
+
Install Libraries: Install either streamlit-terminal or gradio-terminal depending on your chosen framework.
|
401 |
+
Integrate the Terminal: Use the library's functions to embed a terminal component within your Streamlit or Gradio app.
|
402 |
+
Capture Input: Capture the user's input from the terminal and pass it to your command execution function.
|
403 |
+
Display Output: Display the output of the terminal commands, including both standard output and errors.
|
404 |
+
Code Generation:
|
405 |
+
|
406 |
+
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).
|
407 |
+
Prompt Engineering: Develop effective prompts for the LLM to generate code based on natural language instructions.
|
408 |
+
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.
|
409 |
+
Code Correction: You can explore ways to automatically correct code errors, perhaps using a combination of syntax checking and LLM assistance.
|
410 |
+
Workspace Explorer:
|
411 |
+
|
412 |
+
Streamlit or Gradio Filesystem Access: Use Streamlit's st.file_uploader or Gradio's gr.File component to allow users to upload files.
|
413 |
+
File Management: Implement functions to create, edit, and delete files and directories within the workspace.
|
414 |
+
Display Files: Use Streamlit's st.code or Gradio's gr.File component to display the contents of files in the workspace.
|
415 |
+
Directory Structure: Display the directory structure of the workspace using a tree-like representation.
|
416 |
+
Dependency Management:
|
417 |
+
|
418 |
+
Package Installation: Create a function that takes a package name as input, installs it using pip, and updates the requirements.txt file.
|
419 |
+
Workspace Population: Develop a function to create files and directories in the workspace based on installed packages.
|
420 |
+
Application Build and Launch:
|
421 |
+
|
422 |
+
Build Logic: Develop a function to build the web app based on the user's code and dependencies.
|
423 |
+
Launch Functionality: Implement a mechanism to launch the built app.
|
424 |
+
Error Correction: Identify and correct errors during the build and launch process.
|
425 |
+
Automated Assistance: Provide automated assistance during the build and launch process, with a gradient slider to adjust the level of user override.
|
426 |
+
|
427 |
+
Recommendations, Enhancements, Optimizations, and Workflow:
|
428 |
+
|
429 |
+
1. LLM Selection for Code Generation:
|
430 |
+
* **Google/Flan-T5-XL:** Excellent for code generation, particularly for Python.
|
431 |
+
* **Salesforce/CodeT5-Base:** Strong for code generation, with a focus on code summarization and translation.
|
432 |
+
* **Microsoft/CodeGPT-Small:** A smaller model that is suitable for code generation tasks, especially if you have limited computational resources.
|
433 |
+
|
434 |
+
2. Prompt Engineering for Code Generation:
|
435 |
+
* **Contextual Prompts:** Provide the LLM with as much context as possible, including the desired programming language, libraries, and any specific requirements.
|
436 |
+
* **Code Snippets:** If possible, include code snippets as part of the prompt to guide the LLM's code generation.
|
437 |
+
* **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.
|
438 |
+
|
439 |
+
3. Workspace Exploration:
|
440 |
+
* **Tree-Like View:** Use a tree-like representation to display the workspace's directory structure.
|
441 |
+
* **Search Functionality:** Implement a search bar to allow users to quickly find specific files or directories.
|
442 |
+
* **Code Highlighting:** Provide code highlighting for files in the workspace to improve readability.
|
443 |
+
|
444 |
+
4. Dependency Management:
|
445 |
+
* **Virtual Environments:** Use virtual environments to isolate project dependencies and prevent conflicts.
|
446 |
+
* **Automatic Updates:** Implement a mechanism to automatically update dependencies when new versions are available.
|
447 |
+
* **Dependency Locking:** Use tools like `pip-tools` or `poetry` to lock dependencies to specific versions, ensuring consistent builds.
|
448 |
+
|
449 |
+
5. Application Build and Launch:
|
450 |
+
* **Build Tool Integration:** Consider integrating a build tool like `poetry` or `pipenv` into your workflow to automate the build process.
|
451 |
+
* **Containerization:** Containerize the app using Docker to ensure consistent deployments across different environments.
|
452 |
+
* **Deployment Automation:** Explore tools like `Heroku`, `AWS Elastic Beanstalk`, or `Google App Engine` to automate the deployment process.
|
453 |
+
|
454 |
+
6. Automated Assistance:
|
455 |
+
* **Error Detection and Correction:** Implement a system that can detect common coding errors and suggest corrections.
|
456 |
+
* **Code Completion:** Use an LLM to provide code completion suggestions as the user types.
|