Initial commit

README.md

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----
-title: HarmonyAI
-emoji: 🏢
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 4.37.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# HarmonyAI
+
+HarmonyAI is a powerful, self-improving AI system designed to understand and execute commands with human-like comprehension and intention. It continuously self-evaluates and optimizes its capabilities, focusing on generating financial success through ethical, innovative software solutions.
+
+## Directory Structure
+
+\```
+harmonyai/
+  ├── dataset/
+  │   └── commands_dataset.csv
+  ├── scripts/
+  │   ├── execute_command.py
+  │   ├── fine_tune.py
+  │   └── web_scrape.py
+  ├── models/
+  │   └── model_config.py
+  ├── server/
+  │   └── app.py
+  ├── workflows/
+  │   └── airflow_dags.py
+  ├── monitoring/
+  │   ├── prometheus_config.yaml
+  │   └── grafana_dashboard.json
+  └── README.md
+\```
+
+## Components
+
+### Dataset
+
+The dataset includes a variety of commands, intents, effective methods, permissions, safety checks, self-improvement directives, and user interaction examples.
+
+### Scripts
+
+- `execute_command.py`: Handles the execution of commands sent to HarmonyAI.
+- `fine_tune.py`: Fine-tunes the selected models using the prepared dataset.
+- `web_scrape.py`: Scrapes websites for data to be used in fine-tuning the model.
+
+### Models
+
+- `model_config.py`: Configuration for the models used by HarmonyAI.
+
+### Server
+
+- `app.py`: Flask server to handle command execution requests.
+
+### Workflows
+
+- `airflow_dags.py`: Apache Airflow DAGs for workflow automation.
+
+### Monitoring
+
+- `prometheus_config.yaml`: Configuration for Prometheus monitoring.
+- `grafana_dashboard.json`: Configuration for Grafana dashboard.
+
+## Setup
+
+1. **Run the setup script**:
+    \```
+    bash setup_harmonyai_auto.sh
+    \```
+
+2. **Fine-tune the model**:
+    \```
+    python ~/harmonyai/scripts/fine_tune.py
+    \```
+
+3. **Start the server**:
+    \```
+    python ~/harmonyai/server/app.py
+    \```
+
+4. **Run Airflow**:
+    \```
+    airflow initdb
+    airflow webserver -p 8080
+    airflow scheduler
+    \```
+
+5. **Monitor using Prometheus and Grafana**:
+    - Follow the configurations in `prometheus_config.yaml` and `grafana_dashboard.json`.
+

models/model_config.py

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+"""
+model_config.py
+---------------
+Configuration for the models used by HarmonyAI.
+"""
+
+MODEL_NAME = "redpajama/incite-chat-3b-v1"
+
+# Additional model configuration settings can be added here
+

monitoring/grafana_dashboard.json

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+{
+  "dashboard": {
+    "id": null,
+    "title": "HarmonyAI Monitoring",
+    "tags": [],
+    "timezone": "browser",
+    "panels": [
+      {
+        "type": "graph",
+        "title": "CPU Usage",
+        "targets": [
+          {
+            "expr": "rate(node_cpu_seconds_total[5m])",
+            "interval": "",
+            "refId": "A"
+          }
+        ],
+        "datasource": "Prometheus"
+      }
+    ]
+  }
+}

monitoring/prometheus_config.yaml

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+global:
+  scrape_interval: 15s
+
+scrape_configs:
+  - job_name: "flask_server"
+    static_configs:
+      - targets: ["localhost:5000"]
+

scripts/execute_command.py

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+"""
+execute_command.py
+-------------------
+This script handles the execution of commands sent to HarmonyAI.
+"""
+
+import requests
+
+def is_safe(command, script):
+    # Implement safety checks here
+    return True
+
+def send_command_to_server(command, script):
+    response = requests.post("http://localhost:5000/execute", json={"command": command, "script": script})
+    return response.json()
+
+def execute_command(command, script):
+    if is_safe(command, script):
+        result = send_command_to_server(command, script)
+        print(result)
+    else:
+        print("Command is not safe to execute.")
+
+# Example usage
+command = "Go through my email, find the website I signed up for, and cancel my subscription."
+script = """#!/bin/bash
+# Script to search emails and cancel subscription
+python search_emails.py --action cancel_subscription"""
+execute_command(command, script)
+

scripts/fine_tune.py

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+"""
+fine_tune.py
+------------
+This script fine-tunes the selected models using the prepared dataset.
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+import pandas as pd
+import torch
+
+# Load dataset
+df = pd.read_csv("../dataset/commands_dataset.csv")
+commands = df["command"].tolist()
+intents = df["intent"].tolist()
+methods = df["effective_method"].tolist()
+checks = df["safety_check"].tolist()
+permissions = df["permissions"].tolist()
+
+class CommandsDataset(torch.utils.data.Dataset):
+    def __init__(self, tokenizer, commands, intents, methods, checks, permissions, max_length=512):
+        self.tokenizer = tokenizer
+        self.commands = commands
+        self.intents = intents
+        self.methods = methods
+        self.checks = checks
+        self.permissions = permissions
+        self.max_length = max_length
+
+    def __len__(self):
+        return len(self.commands)
+
+    def __getitem__(self, idx):
+        input_text = f"Command: {self.commands[idx]} Intent: {self.intents[idx]} Method: {self.methods[idx]} Safety Check: {self.checks[idx]} Permissions: {self.permissions[idx]}"
+        encoding = self.tokenizer(input_text, truncation=True, padding="max_length", max_length=self.max_length, return_tensors="pt")
+        return {key: val.squeeze() for key, val in encoding.items()}
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("redpajama/incite-chat-3b-v1")
+model = AutoModelForCausalLM.from_pretrained("redpajama/incite-chat-3b-v1")
+
+# Prepare dataset
+dataset = CommandsDataset(tokenizer, commands, intents, methods, checks, permissions)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    warmup_steps=10,
+    weight_decay=0.01,
+    logging_dir="./logs",
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+)
+
+# Fine-tune the model
+trainer.train()
+

scripts/test_file.txt

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+test content

scripts/web_scrape.py

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+"""
+web_scrape.py
+-------------
+This script scrapes websites for data to be used in fine-tuning the model.
+"""
+
+import requests
+from bs4 import BeautifulSoup
+
+def scrape_site(url):
+    response = requests.get(url)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.content, "html.parser")
+        # Implement data extraction logic here
+        return soup.get_text()
+    return ""
+
+# Example usage
+url = "https://example.com"
+data = scrape_site(url)
+with open("../data/scraped_data.txt", "w") as file:
+    file.write(data)
+

server/app.py

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+"""
+app.py
+------
+Flask server to handle command execution requests.
+"""
+
+from flask import Flask, request, jsonify
+import subprocess
+
+app = Flask(__name__)
+
+@app.route("/execute", methods=["POST"])
+def execute():
+    data = request.json
+    script = data["script"]
+    with open("temp_script.sh", "w") as file:
+        file.write(script)
+    subprocess.run(["chmod", "+x", "temp_script.sh"])
+    result = subprocess.run(["./temp_script.sh"], capture_output=True, text=True)
+    return jsonify({"output": result.stdout, "error": result.stderr})
+
+if __name__ == "__main__":
+    app.run(port=5000)
+

workflows/airflow_dags.py

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+"""
+airflow_dags.py
+---------------
+Apache Airflow DAGs for workflow automation.
+"""
+
+from airflow import DAG
+from airflow.operators.python_operator import PythonOperator
+from datetime import datetime
+
+default_args = {
+    "owner": "airflow",
+    "start_date": datetime(2023, 1, 1),
+    "retries": 1,
+}
+
+dag = DAG(
+    "harmonyai_workflow",
+    default_args=default_args,
+    description="HarmonyAI workflow automation",
+    schedule_interval="@daily",
+)
+
+def example_task():
+    print("Executing example task")
+
+task = PythonOperator(
+    task_id="example_task",
+    python_callable=example_task,
+    dag=dag,
+)
+