Update app.py

app.py

@@ -1,57 +1,64 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
 import json
+from datetime import datetime
 from pathlib import Path
+from uuid import uuid4
 
-app = FastAPI()
-
-# Path to store users (persistent across HF Spaces runs)
-USERS_FILE = Path("users.json")
-
-# Ensure the file exists
-if not USERS_FILE.exists():
-    USERS_FILE.write_text("[]")  # empty list
-
-# Pydantic model for input
-class User(BaseModel):
-    username: str
-    num1: float
-    num2: float
-
-# Helper function to load users
-def load_users():
-    with USERS_FILE.open("r") as f:
-        return json.load(f)
-
-# Helper function to save users
-def save_users(users):
-    with USERS_FILE.open("w") as f:
-        json.dump(users, f, indent=2)
-
-# Endpoint to add user and sum numbers
-@app.post("/add-user")
-def add_user(user: User):
-    users = load_users()
-
-    # Check if username already exists
-    if any(u["username"] == user.username for u in users):
-        raise HTTPException(status_code=400, detail="Username already exists")
-
-    # Add user
-    users.append({
-        "username": user.username,
-        "num1": user.num1,
-        "num2": user.num2,
-        "sum": user.num1 + user.num2
-    })
-    save_users(users)
-
-    return {
-        "message": f"User {user.username} added successfully!",
-        "sum": user.num1 + user.num2
-    }
-
-# Endpoint to list all users
-@app.get("/users")
-def get_users():
-    return load_users()
+import gradio as gr
+from PIL import Image
+
+from huggingface_hub import CommitScheduler
+
+# ---------------- Dataset setup ----------------
+IMAGE_DATASET_DIR = Path("image_dataset") / f"train-{uuid4()}"
+IMAGE_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+IMAGE_JSONL_PATH = IMAGE_DATASET_DIR / "metadata.jsonl"
+
+# Scheduler for your HF dataset repo
+scheduler = CommitScheduler(
+    repo_id="codewithRiz/Buck_data_saving",  # your dataset repo
+    repo_type="dataset",
+    folder_path=IMAGE_DATASET_DIR,
+    path_in_repo=IMAGE_DATASET_DIR.name,
+)
+
+# ---------------- Save uploaded image ----------------
+def save_uploaded_image(user_id: str, image: Image.Image) -> str:
+    """
+    Save the uploaded image to local dataset folder and log metadata.
+    """
+    image_path = IMAGE_DATASET_DIR / f"{uuid4()}.png"
+
+    with scheduler.lock:
+        # Save the image
+        image.save(image_path)
+
+        # Save metadata to JSONL
+        with IMAGE_JSONL_PATH.open("a") as f:
+            json.dump({
+                "user_id": user_id,
+                "file_name": image_path.name,
+                "datetime": datetime.now().isoformat()
+            }, f)
+            f.write("\n")
+
+    return f"Image uploaded successfully for user {user_id}!"
+
+# ---------------- Gradio UI ----------------
+def get_demo():
+    with gr.Row():
+        user_id_input = gr.Textbox(label="User ID")
+        image_input = gr.Image(label="Upload Image", type="pil")
+    upload_btn = gr.Button("Upload")
+    upload_status = gr.Textbox(label="Status")
+    upload_btn.click(
+        fn=save_uploaded_image,
+        inputs=[user_id_input, image_input],
+        outputs=upload_status
+    )
+
+# Launch Gradio app
+demo = gr.Blocks()
+with demo:
+    get_demo()
+
+demo.launch()