Upload 4 files

Dockerfile

@@ -0,0 +1,22 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# System deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1 libglib2.0-0 && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install Python deps
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app
+COPY app.py .
+
+# HF Spaces expects port 7860
+EXPOSE 7860
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+ENV GRADIO_SERVER_PORT="7860"
+
+CMD ["python", "app.py"]

README.md

@@ -1,64 +1,12 @@
----
-title: AI Image Detector (Fine-Tuned Model)
-emoji: 👁️
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 6.8.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 👁️ AI Image Detector Ensemble
-
-A 6-engine ensemble that detects whether an image is **AI-generated** or **real**.
-
-## Engines
-
-| # | Engine | Model / Method |
-|---|--------|---------------|
-| 1 | FFT | Fast Fourier Transform — frequency-domain artifact detection |
-| 2 | ELA | Error Level Analysis — compression-tampering map |
-| 3 | ResNet | `umm-maybe/AI-image-detector` |
-| 4 | SigLIP | `Ateeqq/ai-vs-human-image-detector` |
-| 5 | SDXL   | `Organika/sdxl-detector` |
-| 6 | ViT    | `prithivMLmods/Deep-Fake-Detector-v2-Model` |
-
-The **Final Verdict** averages all four deep-learning scores.
-
-## API Usage (Android / Mobile)
-
-Gradio exposes a REST API automatically.  
-Use the `/api/predict` endpoint (or the auto-generated client) to integrate with any Android app:
-
-```
-POST https://<your-space-url>/api/predict
-Content-Type: application/json
-
-{
-  "data": ["<base64-encoded-image-or-public-URL>"]
-}
-```
-
-The JSON response includes:
-```json
-{
-  "verdict": "FAKE | REAL",
-  "confidence": 87.45,
-  "scores": {
-    "resnet":   92.3,
-    "siglip":   88.1,
-    "sdxl":     85.0,
-    "deepfake": 84.5
-  }
-}
-```
-
-## Files
-
-| File | Purpose |
-|------|---------|
-| `app.py` | Everything — pre-processing, all engines, ensemble, Gradio UI |
-| `requirements.txt` | Python dependencies (CPU PyTorch for fast HF builds) |
-| `README.md` | This file (also configures the HF Space) |
+---
+title: AI Image Detector
+emoji: 🧬
+colorFrom: purple
+colorTo: blue
+sdk: docker
+pinned: false
+---
+
+# 🧬 AI Image Detector — Fine-Tuned Model
+
+Powered by a custom fine-tuned Vision Transformer (99.4% test accuracy).

app.py

@@ -372,7 +372,6 @@ footer { display: none !important; }
 """
 
 HEADER_HTML = f"""
-<style>{CUSTOM_CSS}</style>
 <div class="hero-header">
     <h1>🧬 AI Image Detector</h1>
     <p class="tagline">Powered by a <b>fine-tuned Vision Transformer</b> with 99.4% accuracy</p>
@@ -443,7 +442,11 @@ def analyze_image(pil_image):
     return html, result["_fft_img"], result["_ela_img"], result["_noise_img"], s.get("finetuned",0), s.get("siglip",0), s.get("smogy",0), s.get("noise",0), j
 
 
-with gr.Blocks(title="AI Image Detector — Fine-Tuned") as demo:
+with gr.Blocks(
+    title="AI Image Detector — Fine-Tuned",
+    theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue", neutral_hue="slate"),
+    css=CUSTOM_CSS,
+) as demo:
 
     gr.HTML(HEADER_HTML)
 
@@ -468,7 +471,7 @@ with gr.Blocks(title="AI Image Detector — Fine-Tuned") as demo:
         noise_score_out = gr.Number(label="🔬 Noise (20%)", precision=2)
 
     gr.HTML('<div class="section-title">📦 API Response</div>')
-    json_out = gr.Textbox(label="JSON", lines=8, interactive=False)
+    json_out = gr.Textbox(label="JSON", lines=8, show_copy_button=True, interactive=False)
 
     submit_btn.click(
         fn=analyze_image,

requirements.txt

@@ -1,12 +1,12 @@
 # AI Image Detector — New Approach
-# Designed for Hugging Face Spaces (Python 3.11+)
+# Uses Dockerfile to control exact versions (bypasses HF forced Gradio 6)
 
-# PyTorch CPU (Spaces provides GPU separately)
+# PyTorch CPU
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch==2.6.0+cpu
 
-# Core
-# gradio is auto-installed by HF Spaces (v6.x)
+# Core — pinned Gradio 5.12.0 (stable, tested)
+gradio==5.12.0
 transformers>=4.38.0
 Pillow>=9.0.0
 numpy<2.0