feat: add Bit Plane extraction to forensic analysis and enhance Gradio interface with new processing tools

app_mcp.py

@@ -362,6 +362,8 @@ def predict_image_with_json(img, confidence_threshold, augment_methods, rotate_d
     # 6. Perform forensic processing
     gradient_image = gradient_processing(img_np_og)  # Added gradient processing
     minmax_image = minmax_preprocess(img_np_og)  # Added MinMax processing
+    bitplane_image = bit_plane_extractor(img_np_og)
+    
 
     # First pass - standard analysis
     ela1 = ELA(img_np_og, quality=75, scale=50, contrast=20, linear=False, grayscale=True)
@@ -370,7 +372,7 @@ def predict_image_with_json(img, confidence_threshold, augment_methods, rotate_d
     ela2 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
     ela3 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
     
-    forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, minmax_image]
+    forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, minmax_image, bitplane_image]
 
     # 7. Generate boilerplate descriptions for forensic outputs for anomaly agent
     forensic_output_descriptions = [
@@ -379,7 +381,8 @@ def predict_image_with_json(img, confidence_threshold, augment_methods, rotate_d
         "ELA analysis (Pass 2): Grayscale error map, quality 75, enhanced contrast.",
         "ELA analysis (Pass 3): Color error map, quality 75, enhanced contrast.",
         "Gradient processing: Highlights edges and transitions.",
-        "MinMax processing: Deviations in local pixel values."
+        "MinMax processing: Deviations in local pixel values.",
+        "Bit Plane extractor: Visualization of individual bit planes from different color channels."
     ]
     # You could also add descriptions for Wavelet and Bit Plane if they were dynamic outputs
     # For instance, if wavelet_blocking_noise_estimation had parameters that changed and you wanted to describe them.
@@ -581,14 +584,50 @@ with gr.Blocks(css="#post-gallery { overflow: hidden !important;} .grid-wrap{ ov
             description="Extracts and visualizes individual bit planes from different color channels. This forensic tool helps identify hidden patterns and artifacts in image data that may indicate manipulation. Different bit planes can reveal inconsistencies in image processing or editing.",
             api_name="tool_bitplane"
         )
-    # with gr.Tab("EXIF Full Dump"):
-    #     gr.Interface(
-    #         fn=exif_full_dump,
-    #         inputs=gr.Image(type="pil"),
-    #         outputs=gr.JSON(),
-    #         description="Extract all EXIF metadata from the uploaded image."
-    #     )
-
+    with gr.Tab("Error Level Analysis (ELA)", visible=False):
+        gr.Interface(
+            fn=ELA,
+            inputs=[
+                gr.Image(type="pil", label="Input Image"),
+                gr.Slider(1, 100, value=75, step=1, label="JPEG Quality"),
+                gr.Slider(1, 100, value=50, step=1, label="Output Scale (Multiplicative Gain)"),
+                gr.Slider(0, 100, value=20, step=1, label="Output Contrast (Tonality Compression)"),
+                gr.Checkbox(value=False, label="Use Linear Difference"),
+                gr.Checkbox(value=False, label="Grayscale Output")
+            ],
+            outputs=gr.Image(type="pil"),
+            title="Error Level Analysis (ELA)",
+            description="Performs Error Level Analysis to detect re-saved JPEG images, which can indicate tampering. ELA highlights areas of an image that have different compression levels.",
+            api_name="tool_ela"
+        )
+    with gr.Tab("Gradient Processing", visible=False):
+        gr.Interface(
+            fn=gradient_processing,
+            inputs=[
+                gr.Image(type="pil", label="Input Image"),
+                gr.Slider(0, 100, value=90, step=1, label="Intensity"),
+                gr.Dropdown(["Abs", "None", "Flat", "Norm"], label="Blue Mode", value="Abs"),
+                gr.Checkbox(value=False, label="Invert Gradients"),
+                gr.Checkbox(value=False, label="Equalize Histogram")
+            ],
+            outputs=gr.Image(type="pil"),
+            title="Gradient Processing",
+            description="Applies gradient filters to an image to enhance edges and transitions, which can reveal inconsistencies due to manipulation.",
+            api_name="tool_gradient_processing"
+        )
+    with gr.Tab("MinMax Processing", visible=False):
+        gr.Interface(
+            fn=minmax_preprocess,
+            inputs=[
+                gr.Image(type="pil", label="Input Image"),
+                gr.Radio([0, 1, 2, 3, 4], label="Channel (0:Grayscale, 1:Blue, 2:Green, 3:Red, 4:RGB Norm)", value=4),
+                gr.Slider(0, 10, value=2, step=1, label="Radius")
+            ],
+            outputs=gr.Image(type="pil"),
+            title="MinMax Processing",
+            description="Analyzes local pixel value deviations to detect subtle changes in image data, often indicative of digital forgeries.",
+            api_name="tool_minmax_processing"
+        )
 # --- MCP-Ready Launch ---
 if __name__ == "__main__":
     # Initialize CommitScheduler