Restore working app

.gradio/certificate.pem

@@ -1,31 +0,0 @@
------BEGIN CERTIFICATE-----
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------END CERTIFICATE-----

app.py

@@ -1,177 +1,46 @@
-import gradio as gr
-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()
+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()

buck_vs_doe_Detection_best.pt

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

requirements.txt

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