report generated

app.py

@@ -2,52 +2,68 @@ import gradio as gr
 from detector.face import verify_faces, analyze_face
 from detector.voice import verify_voices
 from detector.video import verify_faces_in_video
-from report.report import generate_pdf_report
+from reports.pdf_report import generate_pdf_report
 
 # Holds latest results
 last_face_result = None
 last_voice_result = None
-last_video_results = []
+last_video_results = None
 
 
 def start_scan(image, audio):
     return "Scanning in progress...", None
 
 def compare_faces(img1_path, img2_path):
+    global last_face_result
     result = verify_faces(img1_path, img2_path)
+    result_text = ""
 
     if "error" in result:
         return f"❌ Error: {result['error']}"
 
     if result["verified"]:
-        return f"✅ Match! Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+        result_text = f"✅ Match! Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+        last_face_result = result_text
+        return result_text
+    
     else:
-        return f"❌ No Match. Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+        result_text = f"❌ No Match. Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+        last_face_result = result_text
+        return result_text
 
 
 def compare_voices(audio1, audio2):
+    global last_voice_result
     result = verify_voices(audio1, audio2)
+    result_text = ""
 
     if "error" in result:
         return f"❌ Error: {result['error']}"
 
     if result["match"]:
-        return f"✅ Same speaker detected. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+        result_text = f"✅ Same speaker detected. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+        last_voice_result = result_text
+        return result_text
     else:
-        return f"❌ Different speakers. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+        result_text = f"❌ Different speakers. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+        last_voice_result = result_text
+        return result_text
 
 
 def scan_video(video_path, ref_img):
+    global last_video_results
     results = verify_faces_in_video(video_path, ref_img)
     report = ""
-
+    last_video_results = results 
     for r in results:
         if "error" in r:
             report += f"\n⚠️ Frame {r['frame']}: {r['error']}"
+            # last_video_results.append(report)
         else:
             status = "✅ Match" if r["verified"] else "❌ Mismatch"
             report += f"\n🖼 Frame {r['frame']}: {status} (Distance: {r['distance']})"
 
+            # last_video_results.append(report)
     return report
 
 def generate_report():
@@ -61,16 +77,19 @@ with gr.Blocks(title="Deepfake Watchdog") as demo:
     # Face Verification
     gr.Markdown("### 📷 Face Verification")
     with gr.Tab("Face Verification"):
-        image = gr.Image(label="Upload your face", type="filepath")
-        audio = gr.Audio(label="Upload your voice (optional)", type="filepath")
+        image1 = gr.Image(label="Upload your face", type="filepath")
+        # audio = gr.Audio(label="Upload your voice (optional)", type="filepath")
+        image2 = gr.Image(label="Upload another face", type="filepath")
+
+        # face_btn = gr.Button("Compare Faces")
 
-    run_button = gr.Button("Start Scan")
-    output_text = gr.Textbox(label="Status")
-    output_gallery = gr.Gallery(label="Matched Results")
+        run_button = gr.Button("Compare Faces")
+        output_text = gr.Textbox(label="Result")
+        # output_gallery = gr.Gallery(label="Matched Results")
 
-    run_button.click(
-        start_scan, inputs=[image, audio], outputs=[output_text, output_gallery]
-    )
+        run_button.click(
+            compare_faces, inputs=[image1, image2], outputs=[output_text]
+        )
 
     # Voice Verification
     gr.Markdown("### 🎤 Voice Verification")

pyproject.toml

@@ -6,6 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "deepface>=0.0.93",
+    "fpdf>=1.7.2",
     "gradio[mcp]>=5.32.1",
     "opencv-python-headless>=4.11.0.86",
     "pydub>=0.25.1",

report/report.py

@@ -1,57 +0,0 @@
-from fpdf import FPDF
-import os
-import datetime
-
-class ReportGenerator(FPDF):
-    def header(self):
-        self.set_font("Arial", "B", 16)
-        self.cell(0, 10, "Deepfake Watchdog Report", ln=1, align="C")
-        self.ln(5)
-        
-    def add_section_title(self, title):
-        self.set_font("Arial", "B", 12)
-        self.cell(0, 10, title, ln=1, align="L")
-        self.ln(2)
-    
-    def add_text(self, text):
-        self.set_font("Arial", "", 12)
-        self.multi_cell(0, 8, text)
-        self.ln(2)
-        
-    def add_image(self, image_path, w=60):
-        if os.path.exists(image_path):
-            self.image(image_path, w=w)
-            self.ln(5)
-        else:
-            print(f"Image not found: {image_path}")
-            
-# Generate PDF Report            
-def generate_pdf_report(face_result, voice_result, video_results, output_path=f"report{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"):
-    pdf = ReportGenerator()
-    pdf.add_page()
-
-    # Section 1: Face Verification
-    pdf.add_section_title("Face Verification Results")
-    pdf.add_text(face_result)
-    
-    # Section 2: Voice Verification
-    pdf.add_section_title("Voice Verification Results")
-    pdf.add_text(voice_result)
-    
-    # Section 3: Video Verification
-    pdf.add_section_title("Video Verification Results")
-    
-    for frame in video_results:
-        if "error" in frame:
-            pdf.add_text(f"{frame['frame']}: ERROR - {frame['error']}")
-        else:
-            status = "✅ Match" if frame["verified"] else "❌ Mismatch"
-            pdf.add_text(f"{frame['frame']} - {status} (Distance: {frame['distance']})")
-            
-            if not frame["verified"]:
-                pdf.add_image(frame["frame"], w=80)
-                
-    pdf.output(output_path)
-    print(f"PDF report generated at: {output_path}")
-    return output_path
-

uv.lock

@@ -274,6 +274,7 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "deepface" },
+    { name = "fpdf" },
     { name = "gradio", extra = ["mcp"] },
     { name = "opencv-python-headless" },
     { name = "pydub" },
@@ -284,6 +285,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "deepface", specifier = ">=0.0.93" },
+    { name = "fpdf", specifier = ">=1.7.2" },
     { name = "gradio", extras = ["mcp"], specifier = ">=5.32.1" },
     { name = "opencv-python-headless", specifier = ">=4.11.0.86" },
     { name = "pydub", specifier = ">=0.25.1" },
@@ -371,6 +373,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b8/25/155f9f080d5e4bc0082edfda032ea2bc2b8fab3f4d25d46c1e9dd22a1a89/flatbuffers-25.2.10-py2.py3-none-any.whl", hash = "sha256:ebba5f4d5ea615af3f7fd70fc310636fbb2bbd1f566ac0a23d98dd412de50051", size = 30953, upload-time = "2025-02-11T04:26:44.484Z" },
 ]
 
+[[package]]
+name = "fpdf"
+version = "1.7.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/37/c6/608a9e6c172bf9124aa687ec8b9f0e8e5d697d59a5f4fad0e2d5ec2a7556/fpdf-1.7.2.tar.gz", hash = "sha256:125840783289e7d12552b1e86ab692c37322e7a65b96a99e0ea86cca041b6779", size = 39504, upload-time = "2015-01-21T00:07:47.493Z" }
+
 [[package]]
 name = "fsspec"
 version = "2025.5.1"