Update app.py

app.py

@@ -1,29 +1,17 @@
-import os
-import subprocess
-from huggingface_hub import InferenceClient
 import gradio as gr
 import random
 import time
 from typing import List, Dict
+import transformers
 
-# Simulated agent and tool libraries
+# Define constants
 AGENT_TYPES = ["Task Executor", "Information Retriever", "Decision Maker", "Data Analyzer"]
 TOOL_TYPES = ["Web Scraper", "Database Connector", "API Caller", "File Handler", "Text Processor"]
 
-# Initialize Hugging Face client
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-VERBOSE = False
-MAX_HISTORY = 100
-MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-
-# Import necessary prompts and functions from the existing code
-from .prompts import (
-    ACTION_PROMPT, ADD_PROMPT, COMPRESS_HISTORY_PROMPT, LOG_PROMPT,
-    LOG_RESPONSE, MODIFY_PROMPT, PREFIX, READ_PROMPT, TASK_PROMPT,
-    UNDERSTAND_TEST_RESULTS_PROMPT,
-)
-from .utils import parse_action, parse_file_content, read_python_module_structure
+# Load the language model
+model_name = "t5-small"
+model = transformers.T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = transformers.T5Tokenizer.from_pretrained(model_name)
 
 class Agent:
     def __init__(self, name: str, agent_type: str, complexity: int):
@@ -31,10 +19,36 @@ class Agent:
         self.type = agent_type
         self.complexity = complexity
         self.tools = []
+        self.behavior = {
+            "Task Executor": self.execute_task,
+            "Information Retriever": self.retrieve_information,
+            "Decision Maker": self.make_decision,
+            "Data Analyzer": self.analyze_data,
+        }
 
     def add_tool(self, tool):
         self.tools.append(tool)
 
+    def execute_task(self, task: str):
+        # Simulate task execution
+        time.sleep(2)
+        return f"Task '{task}' executed successfully."
+
+    def retrieve_information(self, query: str):
+        # Simulate information retrieval
+        time.sleep(2)
+        return f"Information retrieved for query '{query}'."
+
+    def make_decision(self, decision: str):
+        # Simulate decision making
+        time.sleep(2)
+        return f"Decision '{decision}' made successfully."
+
+    def analyze_data(self, data: str):
+        # Simulate data analysis
+        time.sleep(2)
+        return f"Data '{data}' analyzed successfully."
+
     def __str__(self):
         return f"{self.name} ({self.type}) - Complexity: {self.complexity}"
 
@@ -42,6 +56,38 @@ class Tool:
     def __init__(self, name: str, tool_type: str):
         self.name = name
         self.type = tool_type
+        self.functionality = {
+            "Web Scraper": self.scrape_web,
+            "Database Connector": self.connect_database,
+            "API Caller": self.call_api,
+            "File Handler": self.handle_file,
+            "Text Processor": self.process_text,
+        }
+
+    def scrape_web(self, url: str):
+        # Simulate web scraping
+        time.sleep(2)
+        return f"Web scraped for URL '{url}'."
+
+    def connect_database(self, database: str):
+        # Simulate database connection
+        time.sleep(2)
+        return f"Connected to database '{database}'."
+
+    def call_api(self, api: str):
+        # Simulate API call
+        time.sleep(2)
+        return f"API '{api}' called successfully."
+
+    def handle_file(self, file: str):
+        # Simulate file handling
+        time.sleep(2)
+        return f"File '{file}' handled successfully."
+
+    def process_text(self, text: str):
+        # Simulate text processing
+        time.sleep(2)
+        return f"Text '{text}' processed successfully."
 
     def __str__(self):
         return f"{self.name} ({self.type})"
@@ -50,10 +96,6 @@ class Pypelyne:
     def __init__(self):
         self.agents: List[Agent] = []
         self.tools: List[Tool] = []
-        self.history = ""
-        self.task = None
-        self.purpose = None
-        self.directory = None
 
     def add_agent(self, agent: Agent):
         self.agents.append(agent)
@@ -62,208 +104,18 @@ class Pypelyne:
         self.tools.append(tool)
 
     def generate_chat_app(self):
-        time.sleep(2)  # Simulate processing time
+        # Simulate chat app generation
+        time.sleep(2)
         return f"Chat app generated with {len(self.agents)} agents and {len(self.tools)} tools."
 
-    def run_gpt(self, prompt_template, stop_tokens, max_tokens, **prompt_kwargs):
-        content = PREFIX.format(
-            module_summary=read_python_module_structure(self.directory)[0],
-            purpose=self.purpose,
-        ) + prompt_template.format(**prompt_kwargs)
-        
-        if VERBOSE:
-            print(LOG_PROMPT.format(content))
-        
-        stream = client.text_generation(
-            prompt=content,
-            max_new_tokens=max_tokens,
-            stop_sequences=stop_tokens if stop_tokens else None,
-            do_sample=True,
-            temperature=0.7,
-        )
-        
-        resp = "".join(token for token in stream)
-        
-        if VERBOSE:
-            print(LOG_RESPONSE.format(resp))
-        return resp
-
-    def compress_history(self):
-        resp = self.run_gpt(
-            COMPRESS_HISTORY_PROMPT,
-            stop_tokens=["observation:", "task:", "action:", "thought:"],
-            max_tokens=512,
-            task=self.task,
-            history=self.history,
-        )
-        self.history = f"observation: {resp}\n"
-
-    def run_action(self, action_name, action_input):
-        if action_name == "COMPLETE":
-            return "Task completed."
-
-        if len(self.history.split("\n")) > MAX_HISTORY:
-            if VERBOSE:
-                print("COMPRESSING HISTORY")
-            self.compress_history()
-
-        action_funcs = {
-            "MAIN": self.call_main,
-            "UPDATE-TASK": self.call_set_task,
-            "MODIFY-FILE": self.call_modify,
-            "READ-FILE": self.call_read,
-            "ADD-FILE": self.call_add,
-            "TEST": self.call_test,
-        }
-
-        if action_name not in action_funcs:
-            return f"Unknown action: {action_name}"
-
-        print(f"RUN: {action_name} {action_input}")
-        return action_funcs[action_name](action_input)
-
-    def call_main(self, action_input):
-        resp = self.run_gpt(
-            ACTION_PROMPT,
-            stop_tokens=["observation:", "task:"],
-            max_tokens=256,
-            task=self.task,
-            history=self.history,
-        )
-        lines = resp.strip().strip("\n").split("\n")
-        for line in lines:
-            if line == "":
-                continue
-            if line.startswith("thought: "):
-                self.history += f"{line}\n"
-            elif line.startswith("action: "):
-                action_name, action_input = parse_action(line)
-                self.history += f"{line}\n"
-                return self.run_action(action_name, action_input)
-        return "No valid action found."
-
-    def call_set_task(self, action_input):
-        self.task = self.run_gpt(
-            TASK_PROMPT,
-            stop_tokens=[],
-            max_tokens=64,
-            task=self.task,
-            history=self.history,
-        ).strip("\n")
-        self.history += f"observation: task has been updated to: {self.task}\n"
-        return f"Task updated: {self.task}"
-
-    def call_modify(self, action_input):
-        if not os.path.exists(action_input):
-            self.history += "observation: file does not exist\n"
-            return "File does not exist."
-        
-        content = read_python_module_structure(self.directory)[1]
-        f_content = content[action_input] if content[action_input] else "< document is empty >"
-        
-        resp = self.run_gpt(
-            MODIFY_PROMPT,
-            stop_tokens=["action:", "thought:", "observation:"],
-            max_tokens=2048,
-            task=self.task,
-            history=self.history,
-            file_path=action_input,
-            file_contents=f_content,
-        )
-        new_contents, description = parse_file_content(resp)
-        if new_contents is None:
-            self.history += "observation: failed to modify file\n"
-            return "Failed to modify file."
-
-        with open(action_input, "w") as f:
-            f.write(new_contents)
-
-        self.history += f"observation: file successfully modified\n"
-        self.history += f"observation: {description}\n"
-        return f"File modified: {action_input}"
-
-    def call_read(self, action_input):
-        if not os.path.exists(action_input):
-            self.history += "observation: file does not exist\n"
-            return "File does not exist."
-        
-        content = read_python_module_structure(self.directory)[1]
-        f_content = content[action_input] if content[action_input] else "< document is empty >"
-        
-        resp = self.run_gpt(
-            READ_PROMPT,
-            stop_tokens=[],
-            max_tokens=256,
-            task=self.task,
-            history=self.history,
-            file_path=action_input,
-            file_contents=f_content,
-        ).strip("\n")
-        self.history += f"observation: {resp}\n"
-        return f"File read: {action_input}"
-
-    def call_add(self, action_input):
-        d = os.path.dirname(action_input)
-        if not d.startswith(self.directory):
-            self.history += f"observation: files must be under directory {self.directory}\n"
-            return f"Invalid directory: {d}"
-        elif not action_input.endswith(".py"):
-            self.history += "observation: can only write .py files\n"
-            return "Only .py files are allowed."
-        else:
-            if d and not os.path.exists(d):
-                os.makedirs(d)
-            if not os.path.exists(action_input):
-                resp = self.run_gpt(
-                    ADD_PROMPT,
-                    stop_tokens=["action:", "thought:", "observation:"],
-                    max_tokens=2048,
-                    task=self.task,
-                    history=self.history,
-                    file_path=action_input,
-                )
-                new_contents, description = parse_file_content(resp)
-                if new_contents is None:
-                    self.history += "observation: failed to write file\n"
-                    return "Failed to write file."
-
-                with open(action_input, "w") as f:
-                    f.write(new_contents)
-
-                self.history += "observation: file successfully written\n"
-                self.history += f"observation: {description}\n"
-                return f"File added: {action_input}"
-            else:
-                self.history += "observation: file already exists\n"
-                return "File already exists."
-
-    def call_test(self, action_input):
-        result = subprocess.run(
-            ["python", "-m", "pytest", "--collect-only", self.directory],
-            capture_output=True,
-            text=True,
-        )
-        if result.returncode != 0:
-            self.history += f"observation: there are no tests! Test should be written in a test folder under {self.directory}\n"
-            return "No tests found."
-        result = subprocess.run(
-            ["python", "-m", "pytest", self.directory], capture_output=True, text=True
-        )
-        if result.returncode == 0:
-            self.history += "observation: tests pass\n"
-            return "All tests passed."
-        
-        resp = self.run_gpt(
-            UNDERSTAND_TEST_RESULTS_PROMPT,
-            stop_tokens=[],
-            max_tokens=256,
-            task=self.task,
-            history=self.history,
-            stdout=result.stdout[:5000],
-            stderr=result.stderr[:5000],
-        )
-        self.history += f"observation: tests failed: {resp}\n"
-        return f"Tests failed: {resp}"
+def chat_with_pypelyne(message: str) -> str:
+    # Tokenize the input message
+    inputs = tokenizer.encode("translate English to Spanish: " + message, return_tensors="pt")
+    # Generate the response
+    outputs = model.generate(inputs)
+    # Decode the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
 pypelyne = Pypelyne()
 
@@ -280,7 +132,7 @@ def create_tool(name: str, tool_type: str) -> str:
 def assign_tool(agent_name: str, tool_name: str) -> str:
     agent = next((a for a in pypelyne.agents if a.name == agent_name), None)
     tool = next((t for t in pypelyne.tools if t.name == tool_name), None)
-    
+
     if agent and tool:
         agent.add_tool(tool)
         return f"Tool '{tool.name}' assigned to agent '{agent.name}'"
@@ -296,25 +148,10 @@ def list_agents() -> str:
 def list_tools() -> str:
     return "\n".join(str(tool) for tool in pypelyne.tools) or "No tools created yet."
 
-def chat_with_pypelyne(message: str) -> str:
-    return pypelyne.run_action("MAIN", message)
-
-def set_purpose_and_directory(purpose: str, directory: str) -> str:
-    pypelyne.purpose = purpose
-    pypelyne.directory = directory
-    return f"Purpose set to: {purpose}\nWorking directory set to: {directory}"
-
 with gr.Blocks() as app:
     gr.Markdown("# Welcome to Pypelyne")
     gr.Markdown("Create your custom pipeline with agents and tools, then chat with it!")
 
-    with gr.Tab("Setup"):
-        purpose_input = gr.Textbox(label="Set Purpose")
-        directory_input = gr.Textbox(label="Set Working Directory")
-        setup_btn = gr.Button("Set Purpose and Directory")
-        setup_output = gr.Textbox(label="Setup Output")
-        setup_btn.click(set_purpose_and_directory, inputs=[purpose_input, directory_input], outputs=setup_output)
-
     with gr.Tab("Create Agents"):
         agent_name = gr.Textbox(label="Agent Name")
         agent_type = gr.Dropdown(choices=AGENT_TYPES, label="Agent Type")