agent.py

import sys
import json
import os
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel, PeftConfig

# Load model and tokenizer
def load_model():
    base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
    adapter_path = "Harish2002/cli-lora-tinyllama"  # ✅ fixed path

    tokenizer = AutoTokenizer.from_pretrained(base_model)
    model = AutoModelForCausalLM.from_pretrained(base_model)
    model = PeftModel.from_pretrained(model, adapter_path)
    return tokenizer, model

# Generate plan from input instruction
def generate_plan(prompt, tokenizer, model):
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256)
    output = pipe(prompt)[0]['generated_text']
    return output.strip()

# Check if first line is a shell command
def is_shell_command(line):
    return line.startswith(("git", "bash", "tar", "gzip", "grep", "python", "./", "cd", "ls"))

# Log to logs/trace.jsonl
def log_trace(prompt, response):
    os.makedirs("logs", exist_ok=True)
    trace = {
        "timestamp": datetime.utcnow().isoformat(),
        "input": prompt,
        "response": response
    }
    with open("logs/trace.jsonl", "a") as f:
        f.write(json.dumps(trace) + "\n")

# Main
if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python agent.py \"Your instruction here\"")
        sys.exit(1)

    user_input = sys.argv[1]
    tokenizer, model = load_model()
    result = generate_plan(user_input, tokenizer, model)

    # Print result and echo dry-run if it's a shell command
    print("\nGenerated Plan:\n")
    print(result)

    first_line = result.splitlines()[0]
    if is_shell_command(first_line):
        print("\nDry-run:")
        print(f"echo {first_line}")

    log_trace(user_input, result)