updated dashboard

app.py

@@ -1,46 +1,177 @@
-
-from ultralytics import YOLO
 import gradio as gr
-import numpy as np
-from PIL import Image
-
-# -------------------------
-# Load detection model
-# -------------------------
-model = YOLO("buck_vs_doe_Detection_best.pt")
-
-# -------------------------
-# Inference function
-# -------------------------
-def predict(image):
-    # Run inference (YOLO accepts numpy RGB directly)
-    results = model(image)
-
-    # Take first result (single image)
-    r = results[0]
-
-    # Plot results (BGR numpy array)
-    im_bgr = r.plot()
-
-    # Convert BGR → RGB for Gradio
-    im_rgb = im_bgr[..., ::-1]
-
-    return im_rgb
-
-
-# -------------------------
-# Gradio UI
-# -------------------------
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload Image"),
-    outputs=gr.Image(type="numpy", label="Detection Result"),
-    title="Buck Tracker AI – Deer Detection",
-    description="YOLO-based buck vs doe detection using Ultralytics native plotting."
-)
-
-# -------------------------
-# Launch
-# -------------------------
-if __name__ == "__main__":
-    app.launch()
+from huggingface_hub import list_bucket_tree, download_bucket_files
+from datetime import datetime
+import pandas as pd
+import json
+import os
+from dotenv import load_dotenv
+
+# ---------- LOAD ENV ----------
+load_dotenv()
+BUCKET_NAME = os.getenv("BUCKET_NAME")
+
+PAGE_SIZE = 5
+
+
+# ---------- LOAD DATA FROM BUCKET ----------
+def extract_customer_data():
+    try:
+        tree = list_bucket_tree(BUCKET_NAME, recursive=True)
+
+        json_files = [
+            item.path for item in tree
+            if item.path.endswith("cameras.json")
+        ]
+
+        if not json_files:
+            return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
+
+        files_map = [(f, f"C:/tmp/{f.replace('/', '_')}") for f in json_files]
+
+        download_bucket_files(BUCKET_NAME, files=files_map)
+
+        rows = []
+
+        for remote_path, local_path in files_map:
+            parts = remote_path.split("/")
+            customer_id = parts[1] if len(parts) > 1 else "unknown"
+
+            try:
+                with open(local_path, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+
+                for item in data:
+                    rows.append({
+                        "customer_id": customer_id,
+                        "customer_name": item.get("customer_name"),
+                        "customer_email": item.get("customer_email")
+                    })
+
+            except Exception as e:
+                print(f"File error {remote_path}: {e}")
+
+        df = pd.DataFrame(rows)
+
+        # ---------- REMOVE DUPLICATES ----------
+        if not df.empty:
+            df = df.drop_duplicates(
+                subset=["customer_id", "customer_name", "customer_email"]
+            )
+
+        return df
+
+    except Exception as e:
+        print("ERROR:", e)
+        return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
+
+
+# ---------- FILTER ----------
+def apply_search(df, email_query):
+    if df is None or not isinstance(df, pd.DataFrame):
+        return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
+
+    if email_query:
+        df = df[df["customer_email"].astype(str).str.contains(email_query, case=False, na=False)]
+
+    return df
+
+
+# ---------- PAGINATION ----------
+def build_view(df, page, email_query):
+    filtered = apply_search(df, email_query)
+
+    total_pages = max(1, (len(filtered) - 1) // PAGE_SIZE + 1)
+    page = max(0, min(page, total_pages - 1))
+
+    start = page * PAGE_SIZE
+    end = start + PAGE_SIZE
+
+    paged = filtered.iloc[start:end]
+
+    status = f"Page {page+1} / {total_pages} | Total Results: {len(filtered)}"
+
+    return paged, page, status
+
+
+# ---------- ACTIONS ----------
+def search(df, email_query):
+    page = 0
+    table, page, status = build_view(df, page, email_query)
+    return table, page, status, df
+
+
+def next_page(df, page, email_query):
+    page = page + 1
+    table, page, status = build_view(df, page, email_query)
+    return table, page, status, df
+
+
+def prev_page(df, page, email_query):
+    page = page - 1
+    table, page, status = build_view(df, page, email_query)
+    return table, page, status, df
+
+
+def refresh_data(email_query):
+    df = extract_customer_data()
+    page = 0
+    table, page, status = build_view(df, page, email_query)
+    return table, page, status, df
+
+
+# ---------- INITIAL DATA ----------
+df_global = extract_customer_data()
+
+
+# ---------- UI ----------
+with gr.Blocks() as app:
+    gr.Markdown("# 🛰 Buck Tracker Customer Dashboard")
+
+    state_df = gr.State(df_global)
+    state_page = gr.State(0)
+
+    search_box = gr.Textbox(
+        label="🔍 Search Customer Email",
+        placeholder="Enter email to search..."
+    )
+
+    table = gr.Dataframe(
+        value=build_view(df_global, 0, "")[0],
+        interactive=False,
+        label="Customer Data"
+    )
+
+    status = gr.Textbox(interactive=False)
+
+    with gr.Row():
+        refresh_btn = gr.Button("🔄 Refresh Bucket Data", variant="primary")
+        prev_btn = gr.Button("⬅️ Previous")
+        next_btn = gr.Button("Next ➡️")
+
+    # ---------- EVENTS ----------
+
+    search_box.change(
+        fn=search,
+        inputs=[state_df, search_box],
+        outputs=[table, state_page, status, state_df]
+    )
+
+    refresh_btn.click(
+        fn=refresh_data,
+        inputs=[search_box],
+        outputs=[table, state_page, status, state_df]
+    )
+
+    next_btn.click(
+        fn=next_page,
+        inputs=[state_df, state_page, search_box],
+        outputs=[table, state_page, status, state_df]
+    )
+
+    prev_btn.click(
+        fn=prev_page,
+        inputs=[state_df, state_page, search_box],
+        outputs=[table, state_page, status, state_df]
+    )
+
+app.launch()

buck_vs_doe_Detection_best.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:59b80c9c56fdc835892ab2604f5b214700ad3257735d6ecfcd768dee1065b595
-size 22527082

requirements.txt

@@ -1,6 +1,3 @@
-ultralytics==8.3.240
 gradio
 Pillow
-numpy==1.26.4
-torch
-onnx==1.20.0
+numpy