Update app.py

app.py

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+# app.py
+import io
+import os
+import numpy as np
+from PIL import Image
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Load API token from HF secrets
+HF_API_TOKEN = os.environ.get("HF_API_TOKEN", None)
+client = InferenceClient(token=HF_API_TOKEN)
+
+# Models
+CLASSIFIER_MODEL = "prithivMLmods/deepfake-detector-model-v1"
+FORGERY_MODEL = "zhipeixu/fakeshield-v1-22b"
+
+def run_classification(img):
+    """ Deepfake / AI image detection model """
+    try:
+        buf = io.BytesIO()
+        img.save(buf, format="PNG")
+        buf.seek(0)
+
+        out = client.image_classification(
+            model=CLASSIFIER_MODEL,
+            inputs=buf
+        )
+
+        if isinstance(out, list) and len(out) > 0:
+            top = out[0]
+            label = top.get("label", "")
+            score = float(top.get("score", 0.0))
+
+            if "fake" in label.lower() or "ai" in label.lower():
+                return "AI-Generated", round(score * 100, 2)
+            else:
+                return "Real Image", round(score * 100, 2)
+
+        return "Unknown", 0.0
+
+    except Exception as e:
+        return f"Error: {e}", 0.0
+
+def run_forgery_model(img):
+    """ Forgery & manipulation detection model """
+    try:
+        buf = io.BytesIO()
+        img.save(buf, format="PNG")
+        buf.seek(0)
+
+        out = client(
+            model=FORGERY_MODEL,
+            inputs=buf
+        )
+
+        result = {
+            "explanation": None,
+            "mask": None,
+            "raw": out
+        }
+
+        # Modern HF models return dict
+        if isinstance(out, dict):
+            result["explanation"] = out.get("explanation") or out.get("text")
+            result["mask"] = out.get("mask")
+            return result
+
+        # If output is a list of tokens
+        if isinstance(out, list):
+            explanation = []
+            for item in out:
+                if isinstance(item, dict):
+                    explanation.append(item.get("text") or item.get("label", ""))
+                elif isinstance(item, str):
+                    explanation.append(item)
+            result["explanation"] = " ".join(explanation)
+            return result
+
+        return result
+
+    except Exception as e:
+        return {"explanation": f"Error: {e}", "mask": None}
+
+def overlay_mask(img, mask_data):
+    """ Creates a red overlay on manipulated regions """
+    if mask_data is None:
+        return None
+
+    try:
+        arr = np.array(mask_data)
+        if arr.max() <= 1:
+            arr = (arr * 255).astype("uint8")
+
+        mask = Image.fromarray(arr).resize(img.size).convert("L")
+        red = Image.new("RGBA", img.size, (255, 0, 0, 120))
+        overlay = Image.composite(red, Image.new("RGBA", img.size), mask)
+        final = Image.alpha_composite(img.convert("RGBA"), overlay)
+        return final
+
+    except:
+        return None
+
+def analyze(image):
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    label, percent = run_classification(image)
+    forg = run_forgery_model(image)
+
+    explanation = forg.get("explanation") or "No clear manipulation detected."
+    mask = forg.get("mask")
+    overlay_image = overlay_mask(image, mask)
+
+    return image, f"{label} ({percent}%)", explanation, overlay_image
+
+
+# ---------------- UI --------------------
+title = "AI DeepFake & Manipulation Detector"
+description = """
+Upload an image to detect if it's AI-generated or manipulated.
+Two AI models are used:
+- Deepfake classifier (Real vs AI)
+- Forgery detector (Manipulated region + explanation)
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}\n{description}")
+
+    with gr.Row():
+        inp = gr.Image(type="pil", label="Upload Image")
+        with gr.Column():
+            original_out = gr.Image(label="Original Image")
+            overlay_out = gr.Image(label="Manipulation Overlay")
+
+    label_out = gr.Textbox(label="Classification", interactive=False)
+    explanation_out = gr.Textbox(label="Manipulation Explanation", interactive=False)
+
+    btn = gr.Button("Analyze")
+    btn.click(
+        fn=analyze,
+        inputs=[inp],
+        outputs=[original_out, label_out, explanation_out, overlay_out]
+    )
+
+demo.launch()