import os
import torch
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

# ——— CONFIG ———
REPO_ID = "CodCodingCode/llama-3.1-8b-clinical"
SUBFOLDER = "checkpoint-45000"
HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
if not HF_TOKEN:
    raise RuntimeError("Missing HUGGINGFACE_HUB_TOKEN in env")

# ——— 1) Download the full repo ———
local_cache = snapshot_download(
    repo_id=REPO_ID,
    token=HF_TOKEN,
)
print("[DEBUG] snapshot_download → local_cache:", local_cache)
import pathlib

print(
    "[DEBUG] MODEL root contents:",
    list(pathlib.Path(local_cache).glob(f"{SUBFOLDER}/*")),
)

# ——— 2) Repo root contains tokenizer.json; model shards live in the checkpoint subfolder ———
MODEL_DIR = local_cache
MODEL_SUBFOLDER = SUBFOLDER
print("[DEBUG] MODEL_DIR:", MODEL_DIR)
print("[DEBUG] MODEL_DIR files:", os.listdir(MODEL_DIR))
print("[DEBUG] Checkpoint files:", os.listdir(os.path.join(MODEL_DIR, MODEL_SUBFOLDER)))

# ——— 3) Load tokenizer & model from disk ———
tokenizer = AutoTokenizer.from_pretrained(
    MODEL_DIR,
    use_fast=True,
)
print("[DEBUG] Loaded fast tokenizer object:", tokenizer, "type:", type(tokenizer))
# Confirm tokenizer files are present
import os

print("[DEBUG] Files in MODEL_DIR for tokenizer:", os.listdir(MODEL_DIR))
# Inspect tokenizer's initialization arguments
try:
    print("[DEBUG] Tokenizer init_kwargs:", tokenizer.init_kwargs)
except AttributeError:
    print("[DEBUG] No init_kwargs attribute on tokenizer.")

model = AutoModelForCausalLM.from_pretrained(
    MODEL_DIR,
    subfolder=MODEL_SUBFOLDER,
    device_map="auto",
    torch_dtype=torch.float16,
)
model.eval()
print(
    "[DEBUG] Loaded model object:",
    model.__class__.__name__,
    "device:",
    next(model.parameters()).device,
)


# === Role Agent with instruction/input/output format ===
class RoleAgent:
    def __init__(self, role_instruction, tokenizer, model):
        self.tokenizer = tokenizer
        self.model = model
        self.role_instruction = role_instruction

    def act(self, input_text):
        prompt = (
            f"Instruction: {self.role_instruction}\n"
            f"Input: {input_text}\n"
            f"Output:"
        )
        print("[DEBUG] prompt:", prompt)
        encoding = self.tokenizer(prompt, return_tensors="pt")
        inputs = {k: v.to(self.model.device) for k, v in encoding.items()}

        outputs = self.model.generate(
            **inputs,
            max_new_tokens=256,
            do_sample=True,
            temperature=0.7,
            pad_token_id=self.tokenizer.eos_token_id,
        )
        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

        thinking = ""
        print(response)
        answer = response
        if all(tag in response for tag in ("THINKING:", "ANSWER:", "END")):
            print("[FIX] response:", response)
            block = response.split("THINKING:")[1].split("END")[0]
            thinking = block.split("ANSWER:")[0].strip()
            answer = block.split("ANSWER:")[1].strip()

        return {"thinking": thinking, "output": answer}


# === Agents ===
summarizer = RoleAgent(
    role_instruction="You are a clinical summarizer trained to extract structured vignettes from doctor–patient dialogues.",
    tokenizer=tokenizer,
    model=model,
)
diagnoser = RoleAgent(
    role_instruction="You are a board-certified diagnostician that diagnoses patients.",
    tokenizer=tokenizer,
    model=model,
)
questioner = RoleAgent(
    role_instruction="You are a physician asking questions to diagnose a patient.",
    tokenizer=tokenizer,
    model=model,
)
treatment_agent = RoleAgent(
    role_instruction="You are a board-certified clinician. Based on the diagnosis and patient vignette provided below, suggest a concise treatment plan that could realistically be initiated by a primary care physician or psychiatrist.",
    tokenizer=tokenizer,
    model=model,
)


# === Inference State ===
conversation_history = []
summary = ""
diagnosis = ""


# === Gradio Inference ===
def simulate_interaction(user_input, iterations=1):
    history = [f"Doctor: What brings you in today?", f"Patient: {user_input}"]
    summary, diagnosis = "", ""

    for i in range(iterations):
        # Summarize
        sum_in = "\n".join(history) + f"\nPrevious Vignette: {summary}"
        sum_out = summarizer.act(sum_in)
        summary = sum_out["output"]

        # Diagnose
        diag_out = diagnoser.act(summary)
        diagnosis = diag_out["output"]

        # Question
        q_in = f"Vignette: {summary}\nCurrent Estimated Diagnosis: {diag_out['thinking']} {diagnosis}"
        q_out = questioner.act(q_in)
        history.append(f"Doctor: {q_out['output']}")
        history.append("Patient: (awaiting response)")

        # Treatment
        treatment_out = treatment_agent.act(
            f"Diagnosis: {diagnosis}\nVignette: {summary}"
        )

        return {
            "summary": sum_out,
            "diagnosis": diag_out,
            "question": q_out,
            "treatment": treatment_out,
            "conversation": "\n".join(history),
        }


# === Gradio UI ===
def ui_fn(user_input):
    res = simulate_interaction(user_input)
    return f"""📋 Vignette Summary:
💭 THINKING: {res['summary']['thinking']}
ANSWER: {res['summary']['output']}

🩺 Diagnosis:
💭 THINKING: {res['diagnosis']['thinking']}
ANSWER: {res['diagnosis']['output']}
T
❓ Follow-up Question:
💭 THINKING: {res['question']['thinking']}
ANSWER: {res['question']['output']}

💊 Treatment Plan:
{res['treatment']['output']}

💬 Conversation:
{res['conversation']}
"""


demo = gr.Interface(
    fn=ui_fn,
    inputs=gr.Textbox(label="Patient Response"),
    outputs=gr.Textbox(label="Doctor Simulation Output"),
    title="🧠 AI Doctor Multi-Agent Reasoning",
)

if __name__ == "__main__":
    demo.launch(share=True)