Spaces:
Running
Running
Rename app.py to run.py
Browse files- app.py → run.py +135 -161
app.py → run.py
RENAMED
@@ -1,14 +1,28 @@
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import os
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import subprocess
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from huggingface_hub import InferenceClient
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import
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import
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AGENT_TYPES = [
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"Task Executor",
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"Information Retriever",
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]
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VERBOSE = False
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MAX_HISTORY = 100
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MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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# Initialize Hugging Face client
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client = InferenceClient(MODEL)
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#
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from .prompts 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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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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class Agent:
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def __init__(self, name: str, agent_type: str, complexity: int):
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self.name = name
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@@ -57,6 +57,7 @@ class Agent:
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def __str__(self):
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return f"{self.name} ({self.type}) - Complexity: {self.complexity}"
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class Tool:
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def __init__(self, name: str, tool_type: str):
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self.name = name
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def __str__(self):
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return f"{self.name} ({self.type})"
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class Pypelyne:
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def __init__(self):
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self.agents: List[Agent] = []
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time.sleep(2) # Simulate processing time
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return f"Chat app generated with {len(self.agents)} agents and {len(self.tools)} tools."
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def run_gpt(
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if VERBOSE:
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print(LOG_PROMPT.format(content))
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)
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self.history = f"observation: {resp}\n"
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def run_action(self, action_name, action_input):
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if action_name == "COMPLETE":
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return "Task completed."
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print(f"RUN: {action_name} {action_input}")
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return action_funcs[action_name](action_input)
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def call_main(self, action_input):
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resp = self.run_gpt(
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ACTION_PROMPT,
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stop_tokens=["observation:", "task:"],
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return self.run_action(action_name, action_input)
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return "No valid action found."
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def call_set_task(self, action_input):
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self.task =
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self.history += f"observation: task has been updated to: {self.task}\n"
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return f"Task updated: {self.task}"
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def call_modify(self, action_input):
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if not os.path.exists(action_input):
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self.history += "observation: file does not exist\n"
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return "File does not exist."
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content = read_python_module_structure(self.directory)[1]
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f_content = (
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content[action_input]
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)
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resp = self.run_gpt(
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self.history += f"observation: {description}\n"
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return f"File modified: {action_input}"
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def call_read(self, action_input):
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if not os.path.exists(action_input):
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self.history += "observation: file does not exist\n"
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return "File does not exist."
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content = read_python_module_structure(self.directory)[1]
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f_content = (
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content[action_input]
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)
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resp = self.run_gpt(
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self.history += f"observation: {resp}\n"
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return f"File read: {action_input}"
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def call_add(self, action_input):
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d = os.path.dirname(action_input)
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if not d.startswith(self.directory):
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self.history += (
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self.history += "observation: file already exists\n"
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return "File already exists."
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def call_test(self, action_input):
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result = subprocess.run(
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["python", "-m", "pytest", "--collect-only", self.directory],
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capture_output=True,
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self.history += f"observation: there are no tests! Test should be written in a test folder under {self.directory}\n"
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return "No tests found."
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result = subprocess.run(
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["python", "-m", "pytest", self.directory],
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)
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if result.returncode == 0:
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self.history += "observation: tests pass\n"
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self.history += f"observation: tests failed: {resp}\n"
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return f"Tests failed: {resp}"
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pypelyne = Pypelyne()
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def create_agent(name: str, agent_type: str, complexity: int) -> Agent:
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agent = Agent(name, agent_type, complexity)
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pypelyne.add_agent(agent)
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return agent
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def create_tool(name: str, tool_type: str) -> Tool:
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tool = Tool(name, tool_type)
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pypelyne.add_tool(tool)
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return tool
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def main():
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# Create a Flask app
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app = Flask(__name__)
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# Define a route for the chat interface
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@app.route("/chat", methods=["GET", "POST"])
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def chat():
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if request.method == "POST":
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# Get the user's input
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user_input = request.form["input"]
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# Run the input through the Pypelyne
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response = pypelyne.run_action("MAIN", user_input)
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# Return the response
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return jsonify({"response": response})
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else:
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# Return the chat interface
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return """
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<html>
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<body>
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<h1>Pypelyne Chat Interface</h1>
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<form action="/chat" method="post">
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<input type="text" name="input" placeholder="Enter your input">
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<input type="submit" value="Submit">
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</form>
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<div id="response"></div>
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<script>
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// Update the response div with the response from the server
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function updateResponse(response) {
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document.getElementById("response").innerHTML = response;
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}
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</script>
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</body>
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</html>
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"""
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# Define a route for the agent creation interface
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@app.route("/create_agent", methods=["GET", "POST"])
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def create_agent_interface():
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if request.method == "POST":
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# Get the agent's name, type, and complexity
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name = request.form["name"]
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agent_type = request.form["type"]
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complexity = int(request.form["complexity"])
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# Create the agent
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agent = create_agent(name, agent_type, complexity)
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# Return a success message
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return jsonify({"message": f"Agent {name} created successfully"})
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else:
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# Return the agent creation interface
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return """
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<html>
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<body>
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<h1>Create Agent</h1>
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<form action="/create_agent" method="post">
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<label for="name">Name:</label>
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<input type="text" id="name" name="name"><br><br>
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<label for="type">Type:</label>
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<select id="type" name="type">
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<option value="Task Executor">Task Executor</option>
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<option value="Information Retriever">Information Retriever</option>
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<option value="Decision Maker">Decision Maker</option>
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<option value="Data Analyzer">Data Analyzer</option>
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</select><br><br>
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<label for="complexity">Complexity:</label>
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<input type="number" id="complexity" name="complexity"><br><br>
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<input type="submit" value="Create Agent">
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</form>
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</body>
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</html>
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"""
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# Define a route for the tool creation interface
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@app.route("/create_tool", methods=["GET", "POST"])
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def create_tool_interface():
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if request.method == "POST":
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# Get the tool's name and type
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name = request.form["name"]
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tool_type = request.form["type"]
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# Create the tool
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tool = create_tool(name, tool_type)
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# Return a success message
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return jsonify({"message": f"Tool {name} created successfully"})
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else:
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# Return the tool creation interface
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return """
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<html>
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<body>
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<h1>Create Tool</h1>
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<form action="/create_tool" method="post">
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<label for="name">Name:</label>
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<input type="text" id="name" name="name"><br><br>
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<label for="type">Type:</label>
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<select id="type" name="type">
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<option value="Web Scraper">Web Scraper</option>
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<option value="Database Connector">Database Connector</option>
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<option value="API Caller">API Caller</option>
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<option value="File Handler">File Handler</option>
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<option value="Text Processor">Text Processor</option>
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</select><br><br>
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<input type="submit" value="Create Tool">
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</form>
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</body>
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</html>
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"""
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# Run the app
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if __name__ == "__main__":
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app.run(debug=True)
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if __name__ == "__main__":
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main()
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import os
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import subprocess
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from typing import List, Dict, Tuple
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from huggingface_hub import InferenceClient
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import streamlit as st
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from app.prompts 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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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 app.utils import (
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parse_action,
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parse_file_content,
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read_python_module_structure,
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)
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# --- Constants ---
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AGENT_TYPES = [
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"Task Executor",
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"Information Retriever",
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]
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VERBOSE = False
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MAX_HISTORY = 100
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MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1" # Consider using a smaller model
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# --- Initialize Hugging Face client ---
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client = InferenceClient(MODEL)
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# --- Classes ---
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class Agent:
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def __init__(self, name: str, agent_type: str, complexity: int):
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self.name = name
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def __str__(self):
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return f"{self.name} ({self.type}) - Complexity: {self.complexity}"
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class Tool:
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def __init__(self, name: str, tool_type: str):
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self.name = name
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def __str__(self):
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return f"{self.name} ({self.type})"
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class Pypelyne:
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def __init__(self):
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self.agents: List[Agent] = []
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time.sleep(2) # Simulate processing time
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return f"Chat app generated with {len(self.agents)} agents and {len(self.tools)} tools."
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def run_gpt(
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self, prompt_template: str, stop_tokens: List[str], max_tokens: int, **prompt_kwargs
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) -> str:
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content = (
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PREFIX.format(
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module_summary=read_python_module_structure(self.directory)[0],
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purpose=self.purpose,
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)
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+ prompt_template.format(**prompt_kwargs)
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)
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if VERBOSE:
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print(LOG_PROMPT.format(content))
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)
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self.history = f"observation: {resp}\n"
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def run_action(self, action_name: str, action_input: str) -> str:
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if action_name == "COMPLETE":
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return "Task completed."
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print(f"RUN: {action_name} {action_input}")
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return action_funcs[action_name](action_input)
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def call_main(self, action_input: str) -> str:
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resp = self.run_gpt(
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ACTION_PROMPT,
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stop_tokens=["observation:", "task:"],
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return self.run_action(action_name, action_input)
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return "No valid action found."
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def call_set_task(self, action_input: str) -> str:
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self.task = (
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self.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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task=self.task,
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history=self.history,
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)
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.strip("\n")
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.strip()
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)
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self.history += f"observation: task has been updated to: {self.task}\n"
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return f"Task updated: {self.task}"
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def call_modify(self, action_input: str) -> str:
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if not os.path.exists(action_input):
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self.history += "observation: file does not exist\n"
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return "File does not exist."
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content = read_python_module_structure(self.directory)[1]
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f_content = (
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content[action_input]
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if content[action_input]
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else "< document is empty >"
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)
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resp = self.run_gpt(
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self.history += f"observation: {description}\n"
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return f"File modified: {action_input}"
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def call_read(self, action_input: str) -> str:
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if not os.path.exists(action_input):
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self.history += "observation: file does not exist\n"
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return "File does not exist."
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content = read_python_module_structure(self.directory)[1]
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f_content = (
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content[action_input]
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if content[action_input]
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else "< document is empty >"
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)
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resp = self.run_gpt(
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self.history += f"observation: {resp}\n"
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return f"File read: {action_input}"
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+
def call_add(self, action_input: str) -> str:
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d = os.path.dirname(action_input)
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if not d.startswith(self.directory):
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self.history += (
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|
280 |
self.history += "observation: file already exists\n"
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return "File already exists."
|
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+
def call_test(self, action_input: str) -> str:
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284 |
result = subprocess.run(
|
285 |
["python", "-m", "pytest", "--collect-only", self.directory],
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286 |
capture_output=True,
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290 |
self.history += f"observation: there are no tests! Test should be written in a test folder under {self.directory}\n"
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291 |
return "No tests found."
|
292 |
result = subprocess.run(
|
293 |
+
["python", "-m", "pytest", self.directory],
|
294 |
+
capture_output=True,
|
295 |
+
text=True,
|
296 |
)
|
297 |
if result.returncode == 0:
|
298 |
self.history += "observation: tests pass\n"
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|
310 |
self.history += f"observation: tests failed: {resp}\n"
|
311 |
return f"Tests failed: {resp}"
|
312 |
|
313 |
+
|
314 |
+
# --- Global Pypelyne Instance ---
|
315 |
pypelyne = Pypelyne()
|
316 |
|
317 |
+
|
318 |
+
# --- Helper Functions ---
|
319 |
def create_agent(name: str, agent_type: str, complexity: int) -> Agent:
|
320 |
agent = Agent(name, agent_type, complexity)
|
321 |
pypelyne.add_agent(agent)
|
322 |
return agent
|
323 |
|
324 |
+
|
325 |
def create_tool(name: str, tool_type: str) -> Tool:
|
326 |
tool = Tool(name, tool_type)
|
327 |
pypelyne.add_tool(tool)
|
328 |
return tool
|
329 |
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|
330 |
|
331 |
+
# --- Streamlit App Code ---
|
332 |
+
def main():
|
333 |
+
st.title("🧠 Pypelyne: Your AI-Powered Coding Assistant")
|
334 |
+
|
335 |
+
# --- Sidebar ---
|
336 |
+
st.sidebar.title("⚙️ Settings")
|
337 |
+
pypelyne.directory = st.sidebar.text_input(
|
338 |
+
"Project Directory:", value=".", help="Path to your coding project"
|
339 |
+
)
|
340 |
+
pypelyne.purpose = st.sidebar.text_area(
|
341 |
+
"Project Purpose:",
|
342 |
+
help="Describe the purpose of your coding project.",
|
343 |
+
)
|
344 |
+
|
345 |
+
# --- Agent and Tool Management ---
|
346 |
+
st.sidebar.header("🤖 Agents")
|
347 |
+
show_agent_creation = st.sidebar.expander(
|
348 |
+
"Create New Agent", expanded=False
|
349 |
+
)
|
350 |
+
with show_agent_creation:
|
351 |
+
agent_name = st.text_input("Agent Name:")
|
352 |
+
agent_type = st.selectbox("Agent Type:", AGENT_TYPES)
|
353 |
+
agent_complexity = st.slider("Complexity (1-5):", 1, 5, 3)
|
354 |
+
if st.button("Add Agent"):
|
355 |
+
create_agent(agent_name, agent_type, agent_complexity)
|
356 |
+
|
357 |
+
st.sidebar.header("🛠️ Tools")
|
358 |
+
show_tool_creation = st.sidebar.expander("Create New Tool", expanded=False)
|
359 |
+
with show_tool_creation:
|
360 |
+
tool_name = st.text_input("Tool Name:")
|
361 |
+
tool_type = st.selectbox("Tool Type:", TOOL_TYPES)
|
362 |
+
if st.button("Add Tool"):
|
363 |
+
create_tool(tool_name, tool_type)
|
364 |
+
|
365 |
+
# --- Display Agents and Tools ---
|
366 |
+
st.sidebar.subheader("Active Agents:")
|
367 |
+
for agent in pypelyne.agents:
|
368 |
+
st.sidebar.write(f"- {agent}")
|
369 |
+
|
370 |
+
st.sidebar.subheader("Available Tools:")
|
371 |
+
for tool in pypelyne.tools:
|
372 |
+
st.sidebar.write(f"- {tool}")
|
373 |
+
|
374 |
+
# --- Main Content Area ---
|
375 |
+
st.header("💻 Code Interaction")
|
376 |
+
|
377 |
+
task_input = st.text_area(
|
378 |
+
"🎯 Task:",
|
379 |
+
value=pypelyne.task if pypelyne.task else "",
|
380 |
+
help="Describe the coding task you want to perform.",
|
381 |
+
)
|
382 |
+
if task_input:
|
383 |
+
pypelyne.task = task_input
|
384 |
+
|
385 |
+
user_input = st.text_input(
|
386 |
+
"💬 Your Input:", help="Provide instructions or ask questions."
|
387 |
+
)
|
388 |
+
|
389 |
+
if st.button("Execute"):
|
390 |
+
if user_input:
|
391 |
+
with st.spinner("Pypelyne is working..."):
|
392 |
+
response = pypelyne.run_action("MAIN", user_input)
|
393 |
+
st.write("Pypelyne Says: ", response)
|
394 |
+
|
395 |
+
|
396 |
+
# --- Run the Streamlit app ---
|
397 |
if __name__ == "__main__":
|
398 |
main()
|