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agent.py

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+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)