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# app.py
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import requests
import importlib.util
import os
import hashlib
import json
from datetime import datetime
# Configuration
CONFIG = {
"model_name": "deepseek-ai/deepseek-coder-1.3b-instruct",
"update_url": "https://raw.githubusercontent.com/[YOUR_USERNAME]/deepseek-updates/main/",
"manifest_url": "https://raw.githubusercontent.com/[YOUR_USERNAME]/deepseek-updates/main/manifest.json",
"allowed_modules": ["response_handler", "updater"],
"update_frequency": 6 # hours
}
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(CONFIG["model_name"])
model = AutoModelForCausalLM.from_pretrained(CONFIG["model_name"])
# Security verification
def verify_file_integrity(content, expected_hash):
sha256 = hashlib.sha256()
sha256.update(content.encode('utf-8'))
return sha256.hexdigest() == expected_hash
# Update mechanism
def check_for_updates():
try:
# Get update manifest
response = requests.get(CONFIG["manifest_url"])
manifest = response.json()
# Check last update time
last_update_path = "last_update.txt"
if os.path.exists(last_update_path):
with open(last_update_path, 'r') as f:
last_update = datetime.fromisoformat(f.read().strip())
time_since_update = (datetime.utcnow() - last_update).total_seconds() / 3600
if time_since_update < CONFIG["update_frequency"]:
return "Too soon for update check"
# Process updates
updates_applied = []
for module_name in CONFIG["allowed_modules"]:
if module_name in manifest["modules"]:
module_info = manifest["modules"][module_name]
file_path = f"{module_name}.py"
# Download and verify update
response = requests.get(CONFIG["update_url"] + file_path)
if response.status_code == 200:
content = response.text
if verify_file_integrity(content, module_info["sha256"]):
# Save new version
with open(file_path, 'w') as f:
f.write(content)
updates_applied.append(module_name)
# Update timestamp
with open(last_update_path, 'w') as f:
f.write(datetime.utcnow().isoformat())
return f"Updates applied to: {', '.join(updates_applied)}" if updates_applied else "No updates available"
except Exception as e:
return f"Update failed: {str(e)}"
# Dynamic module loader
def load_module(module_name):
spec = importlib.util.spec_from_file_location(module_name, f"{module_name}.py")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
# Load core modules
response_handler = load_module("response_handler")
updater = load_module("updater")
# Main processing function
def process_query(prompt):
# Check for update command
if "/update" in prompt:
return check_for_updates()
# Normal processing
return response_handler.generate_response(prompt, tokenizer, model)
# Create Gradio interface
interface = gr.Interface(
fn=process_query,
inputs=gr.Textbox(lines=3, placeholder="Enter your query..."),
outputs="text",
title="Self-Updating DeepSeek AI",
description="This AI can update its own code. Type '/update' to check for improvements."
)
if __name__ == "__main__":
interface.launch(server_port=7860, share=True) |