app.py

import os
import torch
import time
import torch
import time
import gradio as gr
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, LlamaTokenizer, TextIteratorStreamer
import threading
import queue

# Globals
current_model = None
current_tokenizer = None

# Curated models
model_choices = [
    "meta-llama/Llama-3.2-3B-Instruct",
    "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "google/gemma-7b-it",
    "mistralai/Mistral-Nemo-Instruct-FP8-2407"
]

# Example patient database
patient_db = {
    "001 - John Doe": {
        "name": "John Doe",
        "age": "45",
        "id": "001",
        "notes": "History of chest pain and hypertension. No prior surgeries."
    },
    "002 - Maria Sanchez": {
        "name": "Maria Sanchez",
        "age": "62",
        "id": "002",
        "notes": "Suspected pulmonary embolism. Shortness of breath, tachycardia."
    },
    "003 - Ahmed Al-Farsi": {
        "name": "Ahmed Al-Farsi",
        "age": "29",
        "id": "003",
        "notes": "Persistent migraines. MRI scheduled for brain imaging."
    },
    "004 - Lin Wei": {
        "name": "Lin Wei",
        "age": "51",
        "id": "004",
        "notes": "Annual screening. Family history of breast cancer."
    }
}

# Store conversations per patient
patient_conversations = {}


class RichTextStreamer(TextIteratorStreamer):
    def __init__(self, tokenizer, prompt_len=0, **kwargs):
        super().__init__(tokenizer, **kwargs)
        self.token_queue = queue.Queue()
        self.prompt_len = prompt_len
        self.count = 0

    def put(self, value):
        if isinstance(value, torch.Tensor):
            token_ids = value.view(-1).tolist()
        elif isinstance(value, list):
            token_ids = value
        else:
            token_ids = [value]

        for token_id in token_ids:
            self.count += 1
            if self.count <= self.prompt_len:
                continue  # skip prompt tokens
            token_str = self.tokenizer.decode([token_id], **self.decode_kwargs)
            is_special = token_id in self.tokenizer.all_special_ids
            self.token_queue.put({
                "token_id": token_id,
                "token": token_str,
                "is_special": is_special
            })

    def __iter__(self):
        while True:
            try:
                token_info = self.token_queue.get(timeout=self.timeout)
                yield token_info
            except queue.Empty:
                if self.end_of_generation.is_set():
                    break


@spaces.GPU
def chat_with_model(messages):
    global current_model, current_tokenizer
    if current_model is None or current_tokenizer is None:
        yield messages + [{"role": "assistant", "content": "⚠️ No model loaded."}]
        return

    pad_id = current_tokenizer.pad_token_id
    eos_id = current_tokenizer.eos_token_id
    if pad_id is None:
        pad_id = current_tokenizer.unk_token_id or 0

    output_text = ""
    in_think = False
    max_new_tokens = 1024
    generated_tokens = 0

    # ⮕ PREPARE SYSTEM + INITIAL MESSAGES
    system_messages = [
        {
            "role": "system",
            "content": (
                "You are a radiologist's companion, here to answer questions about the patient and assist in the diagnosis if asked to do so. "
                "You are able to call specialized tools. "
                "At the moment, you have one tool available: an organ segmentation algorithm for abdominal CTs.\n\n"
                "If the user requests an organ segmentation, output a JSON object in this structure:\n"
                "{\n"
                "  \"function\": \"segment_organ\",\n"
                "  \"arguments\": {\n"
                "    \"scan_path\": \"<path_to_ct_scan>\",\n"
                "    \"organ\": \"<organ_name>\"\n"
                "  }\n"
                "}\n\n"
                "Once you call the function, the app will execute it and return the result."
            )
        },
        {
            "role": "system",
            "content": f"Patient Information:\nName: {patient_name.value}\nAge: {patient_age.value}\nID: {patient_id.value}\nNotes: {patient_notes.value}"
        }
    ]

    # Optional: if you later add available_images, you could append another system message.

    welcome_message = (
        "**Welcome to the Radiologist's Companion!**\n\n"
        "You can ask me about the patient's medical history or available imaging data.\n"
        "- I can summarize key details from the EHR.\n"
        "- I can tell you which medical images are available.\n"
        "- If you'd like an organ segmentation (e.g. spleen, liver, kidney_left, colon, femur_right) on an abdominal CT scan, just ask!\n\n"
        "**Example Requests:**\n"
        "- \"What do we know about this patient?\"\n"
        "- \"Which images are available for this patient?\"\n"
        "- \"Can you segment the spleen from the CT scan?\"\n"
    )

    # If it's the first user message (i.e., no assistant yet), prepend welcome
    if len(messages) == 1 and messages[0]['role'] == 'user':
        messages = [{"role": "assistant", "content": welcome_message}] + messages

    # Merge full conversation
    full_messages = system_messages + messages

    prompt = format_prompt(full_messages)

    device = torch.device("cuda")
    current_model.to(device).half()

    inputs = current_tokenizer(prompt, return_tensors="pt").to(device)
    prompt_len = inputs["input_ids"].shape[-1]

    streamer = RichTextStreamer(
        tokenizer=current_tokenizer,
        prompt_len=prompt_len,
        skip_special_tokens=False
    )

    generation_kwargs = dict(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        streamer=streamer,
        eos_token_id=eos_id,
        pad_token_id=pad_id
    )

    thread = threading.Thread(target=current_model.generate, kwargs=generation_kwargs)
    thread.start()

    messages = messages.copy()
    messages.append({"role": "assistant", "content": ""})

    for token_info in streamer:
        token_str = token_info["token"]
        token_id = token_info["token_id"]
        is_special = token_info["is_special"]

        if token_id == eos_id:
            break

        if "<think>" in token_str:
            in_think = True
            token_str = token_str.replace("<think>", "")
            output_text += "*"

        if "</think>" in token_str:
            in_think = False
            token_str = token_str.replace("</think>", "")
            output_text += token_str + "*"
        else:
            output_text += token_str

        if "\nUser" in output_text:
            output_text = output_text.split("\nUser")[0].rstrip()
            messages[-1]["content"] = output_text
            break

        generated_tokens += 1
        if generated_tokens >= max_new_tokens:
            break

        messages[-1]["content"] = output_text

        current_id = patient_id.value
        if current_id:
            patient_conversations[current_id] = messages

        yield messages

    if in_think:
        output_text += "*"
        messages[-1]["content"] = output_text

    torch.cuda.empty_cache()
    messages[-1]["content"] = output_text
    return messages






def load_model_on_selection(model_name, progress=gr.Progress(track_tqdm=False)):
    global current_model, current_tokenizer
    token = os.getenv("HF_TOKEN")

    progress(0, desc="Loading config...")
    config = AutoConfig.from_pretrained(model_name, use_auth_token=token)

    progress(0.2, desc="Loading tokenizer...")

    # Default
    current_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code= True, use_auth_token=token)

    progress(0.5, desc="Loading model...")
    current_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="cpu",  # loaded to CPU initially
        use_auth_token=token
    )

    progress(1, desc="Model ready.")
    return f"{model_name} loaded and ready!"


# Format conversation as plain text
def format_prompt(messages):
    prompt = ""
    for msg in messages:
        role = msg["role"]
        if role == "user":
            prompt += f"User: {msg['content'].strip()}\n"
        elif role == "assistant":
            prompt += f"Assistant: {msg['content'].strip()}\n"
    prompt += "Assistant:"
    return prompt

def add_user_message(user_input, history):
    current_id = patient_id.value
    if current_id:
        conversation = patient_conversations.get(current_id, [])
        conversation.append({"role": "user", "content": user_input})
        patient_conversations[current_id] = conversation
    return "", patient_conversations[current_id]



def autofill_patient(patient_key):
    if patient_key in patient_db:
        info = patient_db[patient_key]

        # Init conversation if not existing
        if info["id"] not in patient_conversations:
            welcome_message = (
                "**Welcome to the Radiologist's Companion!**\n\n"
                "You can ask me about the patient's medical history or available imaging data.\n"
                "- I can summarize key details from the EHR.\n"
                "- I can tell you which medical images are available.\n"
                "- If you'd like an organ segmentation (e.g. spleen, liver, kidney_left, colon, femur_right) on an abdominal CT scan, just ask!\n\n"
                "**Example Requests:**\n"
                "- \"What do we know about this patient?\"\n"
                "- \"Which images are available for this patient?\"\n"
                "- \"Can you segment the spleen from the CT scan?\"\n"
            )

            patient_conversations[info["id"]] = [{"role": "assistant", "content": welcome_message}]

        return info["name"], info["age"], info["id"], info["notes"]
    return "", "", "", ""


with gr.Blocks(css=".gradio-container {height: 100vh; overflow: hidden;}") as demo:
    gr.Markdown("<h2 style='text-align: center;'>Radiologist's Companion</h2>")

    default_model = gr.State(model_choices[0])

    with gr.Row(equal_height=True):  # <-- make columns same height
        with gr.Column(scale=1):
            gr.Markdown("### Patient Information")
            patient_selector = gr.Dropdown(
                choices=list(patient_db.keys()), label="Select Patient", allow_custom_value=False
            )
            patient_name = gr.Textbox(label="Name", placeholder="e.g., John Doe")
            patient_age = gr.Textbox(label="Age", placeholder="e.g., 45")
            patient_id = gr.Textbox(label="Patient ID", placeholder="e.g., 123456")
            patient_notes = gr.Textbox(label="Clinical Notes", lines=10, placeholder="e.g., History of chest pain...")

        with gr.Column(scale=2):
            gr.Markdown("### Chat")
            chatbot = gr.Chatbot(label="Chat", type="messages", height=500)  # <-- fixed height
            msg = gr.Textbox(label="Your message", placeholder="Enter your chat message...", show_label=False)
            with gr.Row():
                submit_btn = gr.Button("Submit", variant="primary")
                clear_btn = gr.Button("Clear", variant="secondary")

        with gr.Column(scale=1):
            gr.Markdown("### Model Settings")
            mode = gr.Radio(["Choose from list", "Enter custom model"], value="Choose from list", label="Model Input Mode")
            model_selector = gr.Dropdown(choices=model_choices, label="Select Predefined Model")
            model_textbox = gr.Textbox(label="Or Enter HF Model Name")
            model_status = gr.Textbox(label="Model Status", interactive=False)

    # Functions for resolving model choice
    def resolve_model_choice(mode, dropdown_value, textbox_value):
        return textbox_value.strip() if mode == "Enter custom model" else dropdown_value

    # Link patient selector
    patient_selector.change(
        autofill_patient,
        inputs=[patient_selector],
        outputs=[patient_name, patient_age, patient_id, patient_notes]
    )

    # After patient selected, load their conversation into chatbot
    def load_patient_conversation(patient_key):
        if patient_key in patient_db:
            patient_id = patient_db[patient_key]["id"]
            history = patient_conversations.get(patient_id, [])
            return history
        return []

    patient_selector.change(
        autofill_patient,
        inputs=[patient_selector],
        outputs=[patient_name, patient_age, patient_id, patient_notes]
    ).then(
        load_patient_conversation,
        inputs=[patient_selector],
        outputs=[chatbot]
    )


    # Load on launch
    demo.load(fn=load_model_on_selection, inputs=default_model, outputs=model_status)

    # Model selection logic
    mode.select(fn=resolve_model_choice, inputs=[mode, model_selector, model_textbox], outputs=default_model).then(
        load_model_on_selection, inputs=default_model, outputs=model_status
    )
    model_selector.change(fn=resolve_model_choice, inputs=[mode, model_selector, model_textbox], outputs=default_model).then(
        load_model_on_selection, inputs=default_model, outputs=model_status
    )
    model_textbox.submit(fn=resolve_model_choice, inputs=[mode, model_selector, model_textbox], outputs=default_model).then(
        load_model_on_selection, inputs=default_model, outputs=model_status
    )

    # Submit via enter key or button
    msg.submit(add_user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        chat_with_model, chatbot, chatbot
    )
    submit_btn.click(add_user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        chat_with_model, chatbot, chatbot
    )

    clear_btn.click(lambda: [], None, chatbot, queue=False)

demo.launch()