server.py

from fastapi import FastAPI, Request
from pydantic import BaseModel

import torch

from fastapi.middleware.cors import CORSMiddleware

from ROBERTAmodel import *
from BERTmodel import *
from DISTILLBERTmodel import *

import os
import zipfile
import shutil


VISUALIZER_CLASSES = {
    "BERT": BERTVisualizer,
    "RoBERTa": RoBERTaVisualizer,
    "DistilBERT": DistilBERTVisualizer,
}

VISUALIZER_CACHE = {}
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],   
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

MODEL_MAP = {
    "BERT": "bert-base-uncased",
    "RoBERTa": "roberta-base",
    "DistilBERT": "distilbert-base-uncased",
}

class LoadModelRequest(BaseModel):
    model: str
    sentence: str
    task:str
    hypothesis:str

class GradAttnModelRequest(BaseModel):
    model: str
    task: str
    sentence: str
    hypothesis:str
    maskID: int | None = None

class PredModelRequest(BaseModel):
    model: str
    sentence: str
    task:str
    hypothesis:str
    maskID: int | None = None

@app.get("/ping")
def ping():
    return {"message": "pong"}



    
def zip_if_needed(src_dir, zip_path):
    if os.path.exists(zip_path):
        return  # already zipped
    print(f"📦 Zipping {src_dir} → {zip_path}")
    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:
        for root, _, files in os.walk(src_dir):
            for file in files:
                full_path = os.path.join(root, file)
                rel_path = os.path.relpath(full_path, src_dir)
                zf.write(full_path, arcname=rel_path)
    print(f"✅ Created zip: {zip_path}")

def extract_zip_if_needed(zip_path, dest_dir):
    if os.path.exists(dest_dir):
        print(f"✅ Already exists: {dest_dir}")
        return
    print(f"🔓 Extracting {zip_path} → {dest_dir}")
    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        zip_ref.extractall(dest_dir)
    print(f"✅ Extracted to: {dest_dir}")

def print_directory_tree(path):
    for root, dirs, files in os.walk(path):
        indent = '  ' * (root.count(os.sep) - path.count(os.sep))
        print(f"{indent}📁 {os.path.basename(root)}/")
        for f in files:
            print(f"{indent}  📄 {f}")
@app.get("/copy_and_extract")
def copy_zip_extract_and_report():
    src_base = "./hf_cache"
    dst_base = "/data/hf_cache"

    for category in ["models", "tokenizers"]:
        src_dir = os.path.join(src_base, category)
        dst_dir = os.path.join(dst_base, category)

        if not os.path.exists(src_dir):
            continue

        os.makedirs(dst_dir, exist_ok=True)

        for name in os.listdir(src_dir):
            full_path = os.path.join(src_dir, name)
            if not os.path.isdir(full_path):
                continue

            zip_name = f"{name}.zip"
            local_zip = os.path.join(src_dir, zip_name)
            dst_zip = os.path.join(dst_dir, zip_name)
            extract_path = os.path.join(dst_dir, name)

            zip_if_needed(full_path, local_zip)

            # Copy zip to /data
            if not os.path.exists(dst_zip):
                shutil.copy(local_zip, dst_zip)
                print(f"📤 Copied zip to: {dst_zip}")

            extract_zip_if_needed(dst_zip, extract_path)

    print("\n📦 Local hf_cache structure:")
    print_directory_tree("./hf_cache")

    print("\n💾 Persistent /data/hf_cache structure:")
    print_directory_tree("/data/hf_cache")





@app.post("/load_model")
def load_model(req: LoadModelRequest):
    print(f"\n--- /load_model request received ---")
    print(f"Model: {req.model}")
    print(f"Sentence: {req.sentence}")
    print(f"Task: {req.task}")
    print(f"hypothesis: {req.hypothesis}")


    if req.model in VISUALIZER_CACHE:
        del VISUALIZER_CACHE[req.model]
    torch.cuda.empty_cache()

    vis_class = VISUALIZER_CLASSES.get(req.model)
    if vis_class is None:
        return {"error": f"Unknown model: {req.model}"}

    print("instantiating visualizer")
    try:
        vis = vis_class(task=req.task.lower())
        print(vis)
        VISUALIZER_CACHE[req.model] = vis
        print("Visualizer instantiated")
    except Exception as e:
        print("Visualizer init failed:", e)
        return {"error": f"Instantiation failed: {str(e)}"}

    print('tokenizing')
    try:
        if req.task.lower() == 'mnli':
            token_output = vis.tokenize(req.sentence, hypothesis=req.hypothesis)
        else:
            token_output = vis.tokenize(req.sentence)
        print("0 Tokenization successful:", token_output["tokens"])
    except Exception as e:
        print("Tokenization failed:", e)
        return {"error": f"Tokenization failed: {str(e)}"}

    print('response ready')    
    response = {
        "model": req.model,
        "tokens": token_output['tokens'],
        "num_layers": vis.num_attention_layers,
    }
    print("load model successful")
    print(response)
    return response





@app.post("/predict_model")
def predict_model(req: PredModelRequest):
    
    print(f"\n--- /predict_model request received ---")
    print(f"predict: Model: {req.model}")
    print(f"predict: Task: {req.task}")
    print(f"predict: sentence: {req.sentence}")
    print(f"predict: hypothesis: {req.hypothesis}")
    print(f"predict: maskID: {req.maskID}")



    print('predict: instantiating')
    try:
        vis_class = VISUALIZER_CLASSES.get(req.model)
        if vis_class is None:
            return {"error": f"Unknown model: {req.model}"}
        #if any(p.device.type == 'meta' for p in vis.model.parameters()):
        #    vis.model = torch.nn.Module.to_empty(vis.model, device=torch.device("cpu"))
    
        vis = vis_class(task=req.task.lower())
        VISUALIZER_CACHE[req.model] = vis
        print("Model reloaded and cached.")
    except Exception as e:
        return {"error": f"Failed to reload model: {str(e)}"}
    
    print('predict: meta stuff')
    

    
    print('predict: Run prediction')
    try:
        if req.task.lower() == 'mnli':
            decoded, top_probs = vis.predict(req.task.lower(), req.sentence, hypothesis=req.hypothesis)
        elif req.task.lower() == 'mlm':
            decoded, top_probs = vis.predict(req.task.lower(), req.sentence, maskID=req.maskID)
        
        else:
            decoded, top_probs = vis.predict(req.task.lower(), req.sentence)
    except Exception as e:
        decoded, top_probs = "error", e
        print(e)

    print('predict: response ready')
    response = {
        "decoded": decoded,
        "top_probs": top_probs.tolist(),
    }
    print("predict: predict model successful")
    if len(decoded) > 5:
        print([(k,v[:5]) for k,v in response.items()])
    else:
        print(response)
    return response



@app.post("/get_grad_attn_matrix")
def get_grad_attn_matrix(req: GradAttnModelRequest):
    
    try:
        print(f"\n--- /get_grad_matrix request received ---")
        print(f"grad:Model: {req.model}")
        print(f"grad:Task: {req.task}")
        print(f"grad:sentence: {req.sentence}")
        print(f"grad: hypothesis: {req.hypothesis}")
        print(f"predict: maskID: {req.maskID}")
        
        
        
        try:
            vis_class = VISUALIZER_CLASSES.get(req.model)
            if vis_class is None:
                return {"error": f"Unknown model: {req.model}"}
            #if any(p.device.type == 'meta' for p in vis.model.parameters()):
            #    vis.model = torch.nn.Module.to_empty(vis.model, device=torch.device("cpu"))
            vis = vis_class(task=req.task.lower())
            VISUALIZER_CACHE[req.model] = vis
            print("Model reloaded and cached.")
        except Exception as e:
            return {"error": f"Failed to reload model: {str(e)}"} 


        
        print("run function")
        try:
            if req.task.lower()=='mnli':
                grad_matrix, attn_matrix = vis.get_all_grad_attn_matrix(req.task.lower(), req.sentence,hypothesis=req.hypothesis)
            elif req.task.lower()=='mlm':
                grad_matrix, attn_matrix = vis.get_all_grad_attn_matrix(req.task.lower(), req.sentence,maskID=req.maskID)
            else:
                grad_matrix, attn_matrix = vis.get_all_grad_attn_matrix(req.task.lower(), req.sentence)
        except Exception as e:
            print("Exception during grad/attn computation:", e)
            grad_matrix, attn_matrix = e,e

    
        response = {
            "grad_matrix": grad_matrix,
            "attn_matrix": attn_matrix,
        }
        print('grad attn successful')
        return response
    except Exception as e:
        print("SERVER EXCEPTION:", e)
        return {"error": str(e)}