main.py

from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
import io
import torch
import pickle
import os
import uvicorn
# Import from model.py
from model import (
    Vocabulary,
    ResNetEncoder,
    DecoderLSTM,
    ImageCaptioningModel,
    generate_caption,
    transform,
    EMBED_DIM,
    HIDDEN_DIM,
)

app = FastAPI(title="Image Captioning API")

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

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# -------------------------
# Paths (relative to main.py)
# -------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))

VOCAB_PATH = os.path.join(BASE_DIR, "vocab.pkl")
CHECKPOINT_PATH = os.path.join(BASE_DIR, "best_checkpoint.pth")

# -------------------------
# Load Vocabulary
# -------------------------
class CustomUnpickler(pickle.Unpickler):
    def find_class(self, module, name):
        if name == "Vocabulary":
            return Vocabulary
        return super().find_class(module, name)

with open(VOCAB_PATH, "rb") as f:
    vocab = CustomUnpickler(f).load()

vocab_size = len(vocab)

# -------------------------
# Build Model
# -------------------------
encoder = ResNetEncoder(EMBED_DIM)
decoder = DecoderLSTM(EMBED_DIM, HIDDEN_DIM, vocab_size)

model = ImageCaptioningModel(encoder, decoder).to(DEVICE)

# -------------------------
# Load Weights
# -------------------------
checkpoint = torch.load(CHECKPOINT_PATH, map_location=DEVICE)
model.load_state_dict(checkpoint["model_state_dict"])

model.eval()

print("✅ Model Loaded Successfully")

# -------------------------
# Health Check
# -------------------------
@app.get("/")
def root():
    return {"message": "Image Captioning API Running"}

# -------------------------
# Caption Endpoint
# -------------------------
@app.post("/caption")
async def caption_image(file: UploadFile = File(...)):

    contents = await file.read()

    image = Image.open(io.BytesIO(contents)).convert("RGB")

    image = transform(image)

    caption = generate_caption(model, image, vocab)

    return {
        "caption": caption
    }

if __name__ == "__main__":

    uvicorn.run("main:app", host="0.0.0.0", port=7860)