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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+vecteezy_loading-bar-animation_26651030.mp4 filter=lfs diff=lfs merge=lfs -text

Bouncer_v2.py

@@ -0,0 +1,160 @@
+import streamlit as st
+import requests
+import json
+import time
+import base64
+from PIL import Image
+from io import BytesIO
+from transformers import pipeline
+from concurrent.futures import ThreadPoolExecutor
+
+# --- 1. CONFIGURATION & SECRETS ---
+# Add your key to Streamlit/HuggingFace Secrets as 'SERPER_API_KEY' for safety
+try:
+    SERPER_API_KEY = st.secrets["SERPER_API_KEY"]
+except:
+    # Fallback for local testing
+    SERPER_API_KEY = "827d931e4257327f4bcc9a35b2995001a68635d0"
+
+SERPER_URL = "https://google.serper.dev/images"
+BG_IMAGE = "vecteezy_abstract-background-design-background-texture-design-with_18752866-1.jpg"
+LOADING_VIDEO = "vecteezy_loading-bar-animation_26651030.mp4"
+
+# --- Page Config & Browser Icon ---
+st.set_page_config(
+    page_title="HumanLens | AI Image Filter",
+    page_icon=BG_IMAGE,  # Sets your custom background as the browser tab icon
+    layout="wide"
+)
+
+# --- 2. CUSTOM BACKGROUND STYLING ---
+def add_custom_style(image_file):
+    with open(image_file, "rb") as image:
+        encoded_string = base64.b64encode(image.read()).decode()
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background-image: url("data:image/jpg;base64,{encoded_string}");
+            background-attachment: fixed;
+            background-size: cover;
+        }}
+        /* Sidebar Glassmorphism effect */
+        [data-testid="stSidebar"] {{
+            background-color: rgba(255, 255, 255, 0.05) !important;
+            backdrop-filter: blur(15px);
+            border-right: 1px solid rgba(255, 255, 255, 0.1);
+        }}
+        /* Title shadows for readability */
+        h1 {{ color: white !important; text-shadow: 2px 2px 8px #000000; }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+try:
+    add_custom_style(BG_IMAGE)
+except:
+    st.warning("Background image not found. Ensure the filename is correct.")
+
+# --- 3. AI MODELS ---
+@st.cache_resource
+def load_models():
+    style_pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-224", device=-1)
+    anime_ai_pipe = pipeline("image-classification", model="legekka/AI-Anime-Image-Detector-ViT", device=-1)
+    photo_ai_pipe = pipeline("image-classification", model="Ateeqq/ai-vs-human-image-detector", device=-1)
+    return style_pipe, anime_ai_pipe, photo_ai_pipe
+
+style_classifier, anime_ai_detector, photo_ai_detector = load_models()
+
+# --- 4. PROCESSING LOGIC ---
+def get_score(preds, target_labels):
+    for p in preds:
+        if p['label'].lower() in target_labels: return p['score']
+    return 0.0
+
+def download_and_process(item, tolerance):
+    url = item.get("imageUrl") 
+    try:
+        response = requests.get(url, timeout=5)
+        # Robust opening to handle transparency
+        raw_img = Image.open(BytesIO(response.content))
+        img = raw_img.convert("RGBA").convert("RGB")
+        img_small = img.resize((224, 224))
+        
+        style_results = style_classifier(img_small, candidate_labels=["anime illustration", "real photo"])
+        top_style = style_results[0]['label']
+        
+        if top_style == "anime illustration":
+            preds = anime_ai_detector(img_small)
+            # Model uses 'natural' for human art
+            human_score = get_score(preds, ['natural', 'human', 'real'])
+        else:
+            preds = photo_ai_detector(img_small)
+            human_score = get_score(preds, ['human', 'real', 'natural'])
+            
+        if human_score >= tolerance:
+            return {"img": img, "score": human_score, "url": url, "style": top_style}
+    except: return None
+
+# --- 5. SIDEBAR CONTROLS ---
+with st.sidebar:
+    st.title("⚙️ Search Controls")
+    query = st.text_input("What are you looking for?", "Concept Art")
+
+    # Target Count with Help
+    c1, c2 = st.columns([4, 1])
+    target_count = c1.slider("Results", 1, 20, 6)
+    if c2.button("❓", key="h_count"):
+        st.info("The number of human-made images you want to see.")
+
+    # Strictness with Help
+    s1, s2 = st.columns([4, 1])
+    tolerance = s1.slider("Strictness", 0.0, 1.0, 0.5)
+    if s2.button("❓", key="h_strict"):
+        st.info("How sure the AI must be. 0.8+ is strict; 0.2 is loose.")
+
+    # Threads with Help
+    t1, t2 = st.columns([4, 1])
+    workers = t1.slider("Threads", 2, 16, 8)
+    if t2.button("❓", key="h_thread"):
+        st.info("Processing speed. Higher is faster but heavier on memory.")
+
+    start_search = st.button("🚀 Start Search", use_container_width=True)
+
+# --- 6. MAIN DISPLAY & LOADING BAR ---
+st.markdown("# 📸 HumanLens")
+st.write("Verifying human creativity on the web.")
+
+if start_search and query:
+    start_time = time.time()
+    
+    # Custom Video Loading Bar
+    loading_placeholder = st.empty()
+    with loading_placeholder.container():
+        st.video(LOADING_VIDEO, autoplay=True, loop=True, muted=True)
+        st.write("### 🤖 AI is analyzing images...")
+
+    # Fetch and process
+    payload = json.dumps({"q": query, "num": target_count * 3})
+    headers = {'X-API-KEY': SERPER_API_KEY, 'Content-Type': 'application/json'}
+    response = requests.post(SERPER_URL, headers=headers, data=payload)
+    raw_results = response.json().get("images", [])
+
+    if raw_results:
+        with ThreadPoolExecutor(max_workers=workers) as executor:
+            results = list(executor.map(lambda r: download_and_process(r, tolerance), raw_results))
+            final_images = [r for r in results if r is not None][:target_count]
+
+        # Remove the loading video once finished
+        loading_placeholder.empty()
+
+        if final_images:
+            st.success(f"Verified {len(final_images)} images in {time.time() - start_time:.1f}s")
+            cols = st.columns(3)
+            for i, item in enumerate(final_images):
+                with cols[i % 3]:
+                    st.image(item["img"], use_container_width=True)
+                    st.caption(f"Human Confidence: {item['score']:.0%}")
+        else:
+            st.warning("No images passed the AI filter. Try lowering 'Strictness'.")

requirments.txt

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+streamlit
+requests
+Pillow
+transformers
+torch

vecteezy_loading-bar-animation_26651030.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9684b3a29a0ce9756e85937f30876220adcd252d64d62f4cbcf00c1331a280a2
+size 2271472