import modal

app = modal.App("distributed-mistral-orchestra")  # <--- app, not stub
@app.function(gpu="A100")
def repository_cloner_agent(github_url):
    import subprocess
    subprocess.run(["git", "clone", github_url, "/root/repo"], check=True)
    return f"Cloned repository: {github_url}"

@app.function(gpu="A100")
def code_analysis_agent(prompt):
    from transformers import AutoTokenizer, AutoModelForCausalLM
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-Large")
    model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Large").to("cuda")
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=300)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

@app.function(gpu="H100")
def metadata_agent(prompt):
    from transformers import AutoTokenizer, AutoModelForCausalLM
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
    model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1").to("cuda")
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=250)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

@app.function(gpu="T4")
def documentation_agent(prompt):
    from transformers import AutoTokenizer, AutoModelForCausalLM
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
    model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2").to("cuda")
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=200)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

@app.function(gpu="A100")
def orchestrator_agent(summary):
    from transformers import AutoTokenizer, AutoModelForCausalLM
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-Large")
    model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Large").to("cuda")
    inputs = tokenizer(summary, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=400)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)