Initial Commit

Dockerfile

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+FROM ubuntu:latest
+LABEL authors="Carla"
+FROM python:3.13
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# Expose FastAPI on port 7860 (required for Spaces)
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+
+ENTRYPOINT ["top", "-b"]

app/api/v1/captions.py

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+from fastapi import APIRouter
+from starlette.responses import JSONResponse
+from app.services.ai_service import generate_captions
+
+
+router = APIRouter()
+@router.get("/captions")
+async def get_captions(prompt: str):
+    """
+    Example: /api/v1/captions?prompt=My+dog+is+funny
+    """
+
+    try:
+        captions = generate_captions(prompt)
+        return {"captions": captions}
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+# @router.get("/captions")
+# async def get_captions(prompt: str):
+#     try:
+#         captions = generate_captions(prompt)
+#         return {"captions": captions}
+#     except Exception as e:
+#         return JSONResponse(content={"error": str(e)}, status_code=500)

app/api/v1/upload.py

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+from fastapi import APIRouter, UploadFile, File
+from starlette.responses import JSONResponse
+
+from app.utils.file_utils import save_upload_file
+
+router = APIRouter()
+
+@router.post("/upload")
+async def upload_file(file: UploadFile = File(...)):
+    try:
+        file_path = save_upload_file(file)
+        return JSONResponse(content={"message": "File uploaded", "path": file_path})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+

app/main.py

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+from fastapi import FastAPI
+from starlette.middleware.cors import CORSMiddleware
+
+from .api.v1 import upload, captions
+
+app = FastAPI(title="AI Meme Generator")
+
+# Register routes
+
+app.include_router(upload.router, prefix="/api/v1", tags=["upload"])
+app.include_router(captions.router, prefix="/api/v1", tags=["captions"])
+@app.get("/")
+async def read_root():
+    return {"message": "AI Meme Generator is running 🚀"}
+
+
+# ✅ Enable CORS so frontend can talk to backend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=['http://localhost:3000'],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)

app/models/meme.py

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+from pydantic import BaseModel
+
+
+class Meme(BaseModel):
+    caption: str
+    image_url: str
+
+class CaptionRequest(BaseModel):
+    prompt: str
+    max_length: int = 50
+    num_return_sequences: int = 3

app/services/ai_service.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_NAME = "bickett/meme-llama"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+
+# def generate_meme_caption(image_path: str, max_length: int = 40, num_return_sequences: int = 3):
+#     # Step 1: get descriptive caption
+#     image = Image.open(image_path).convert("RGB")
+#     description = image_captioner(image)[0]['generated_text']
+#
+#     # Step 2: twist it into a meme-style funny caption
+#     prompt = f"Turn this into a funny meme caption:\n'{description}'\nMeme caption:"
+#     inputs = tokenizer(prompt, return_tensors="pt")
+#
+#     outputs = model.generate(
+#         **inputs,
+#         max_length=max_length,
+#         num_return_sequences=num_return_sequences,
+#         do_sample=True,
+#         top_p=0.95,
+#         temperature=0.9
+#     )
+#
+#     captions = [tokenizer.decode(out, skip_special_tokens=True) for out in outputs]
+#     return captions
+
+
+# def generate_captions(req: CaptionRequest):
+#     inputs = tokenizer(req.prompt, return_tensors="pt")
+#     outputs = model.generate(
+#         **inputs,
+#         max_length=req.max_length,
+#         num_return_sequences=req.num_return_sequences,
+#         do_sample=True,
+#         top_p=0.95,
+#         temperature=0.8,
+#     )
+#     captions = [tokenizer.decode(out, skip_special_tokens=True) for out in outputs]
+#     return {"captions": captions}
+
+def generate_captions(prompt: str, max_length: int = 50, num_return_sequences: int = 3):
+    """
+    Generate AI meme captions given a prompt using Meme-LLaMA
+    """
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.8
+    )
+
+    captions = [tokenizer.decode(out, skip_special_tokens=True) for out in outputs]
+    return captions
+
+# def generate_captions(prompt: str):
+#     return [
+#         f"{prompt} but make it funny :D",
+#         f"When you realize {prompt} was a mistake...",
+#         f"{prompt} vibes only 🚀",
+#     ]

app/utils/file_utils.py

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+import os
+import shutil
+
+from fastapi import UploadFile
+
+UPLOAD_DIR = "uploads"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+def save_upload_file(file: UploadFile) -> str:
+    file_path = os.path.join(UPLOAD_DIR, file.filename)
+    with open(file_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+    # print(f"File path: {file_path.split(',')[0]}{file_path.split(',')[-1]}")
+    return file_path

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch