Update app.py

app.py

@@ -1,364 +1,364 @@
-from PIL import Image
-import numpy as np
-import base64
-from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import google.generativeai as genai
-from langchain_core.messages import HumanMessage
-from langchain_google_genai import ChatGoogleGenerativeAI
-from reportlab.lib.utils import ImageReader
-from flask import send_file, jsonify, request
-from reportlab.pdfgen import canvas
-from reportlab.lib.pagesizes import A4
-from reportlab.lib.units import inch
-import io, torch, os
-from reportlab.lib import colors
-from datetime import datetime
-
-os.environ['GOOGLE_API_KEY'] = "AIzaSyCv2dNQMCD3-9s3E5Th7bDy4ko0dyucRCc"
-genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
-
-# Setup
-app = Flask(__name__)
-CORS(app)
-
-# Initialize device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load model and processor
-processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
-model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
-# model.load_state_dict(torch.load(r"E:\FYP Work\FYP_code\backend\mask2former-ade-(splicing1_2).pth", map_location=device))
-model.load_state_dict(torch.load(r"mask2former-ade-(splicing1_2).pth", map_location=device))
-model = model.to(device)
-model.eval()
-
-# ========== Flask routes ==========
-
-@app.route('/')
-def home():
-    return "Backend is running!"
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'image' not in request.files:
-        return jsonify({"error": "No image uploaded"}), 400
-    
-    try:
-        file = request.files['image']
-        image = Image.open(io.BytesIO(file.read()))
-        
-        # Convert to RGB if needed
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-            
-        # Encode original image to base64
-        original_image_buffer = io.BytesIO()
-        image.save(original_image_buffer, format="PNG")
-        original_image_base64 = base64.b64encode(original_image_buffer.getvalue()).decode("utf-8")
-            
-        # Process image using Mask2Former processor
-        inputs = processor(images=image, return_tensors="pt").to(device)
-        
-        # Predict
-        with torch.no_grad():
-            outputs = model(**inputs)
-            
-        # Process outputs
-        predicted_segmentation = processor.post_process_semantic_segmentation(
-            outputs, target_sizes=[image.size[::-1]]
-        )[0]
-        
-        # Convert to numpy array for visualization
-        segmentation_mask = predicted_segmentation.cpu().numpy()
-        
-        # ========== Create visualizations ==========
-        # Create side-by-side plot
-        fig, axes = plt.subplots(1, 2, figsize=(10, 5))
-        axes[0].imshow(image)
-        axes[0].set_title("Input Image")
-        axes[1].imshow(segmentation_mask)
-        axes[1].set_title("Prediction")
-        
-        for ax in axes:
-            ax.axis("off")
-        plt.tight_layout()
-
-        # Save visualization to buffer
-        buf = io.BytesIO()
-        plt.savefig(buf, format="png", bbox_inches='tight', pad_inches=0)
-        buf.seek(0)
-        visualization_base64 = base64.b64encode(buf.read()).decode('utf-8')
-        plt.close()
-
-        # ========== Encode mask separately ==========
-        # Normalize mask to 0-255 range
-        mask_normalized = (segmentation_mask - segmentation_mask.min()) * (255.0 / (segmentation_mask.max() - segmentation_mask.min()))
-        mask_image = Image.fromarray(mask_normalized.astype(np.uint8))
-        
-        mask_buffer = io.BytesIO()
-        mask_image.save(mask_buffer, format="PNG")
-        mask_base64 = base64.b64encode(mask_buffer.getvalue()).decode("utf-8")
-
-
-        #VLM code
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
-
-        # Create multimodal message
-        message = HumanMessage(
-            content=[
-                {
-                    "type": "text",
-                    #"text": "Please explain briefly where the manipulation has been occured, don't use mask"
-                     "text": " This is an image and its predicted binary mask showing manipulated regions in white. "
-                     "Please explain briefly in 2-3 lines where the manipulation occurred and what might have been altered."
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{original_image_base64}"
-                    },
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/png;base64,{mask_base64}"
-                    },
-                },
-            ]
-        )
-
-        # Get response
-        response = llm.invoke([message])
-        print(response.content)
-
-        return jsonify({
-            "original_image": original_image_base64,
-            "mask": mask_base64,
-            "visualization": visualization_base64,
-            "message": response.content
-        })
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-import json
-from threading import Lock
-
-counter_file = "counter.json"
-counter_lock = Lock()
-
-def get_case_id():
-    today = datetime.now().strftime('%Y%m%d')
-
-    with counter_lock:
-        if os.path.exists(counter_file):
-            with open(counter_file, "r") as f:
-                data = json.load(f)
-        else:
-            data = {}
-
-        count = data.get(today, 0) + 1
-        data[today] = count
-
-        with open(counter_file, "w") as f:
-            json.dump(data, f)
-
-    return f"DFD-{today}-{count:03d}"
-
-
-@app.route('/download-report', methods=['POST'])
-def download_report():
-    try:
-        file = request.files['image']
-        image = Image.open(io.BytesIO(file.read())).convert("RGB")
-
-        # === Process Image ===
-        inputs = processor(images=image, return_tensors="pt").to(device)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        predicted_segmentation = processor.post_process_semantic_segmentation(
-            outputs, target_sizes=[image.size[::-1]]
-        )[0]
-        segmentation_mask = predicted_segmentation.cpu().numpy()
-
-        # === Create Mask Image ===
-        mask_normalized = (segmentation_mask - segmentation_mask.min()) * (255.0 / (segmentation_mask.max() - segmentation_mask.min()))
-        mask_image = Image.fromarray(mask_normalized.astype(np.uint8)).convert("L")
-
-        # === Prepare Images ===
-        image.save("temp_input.png")
-        mask_image.save("temp_mask.png")
-
-        # === Get LLM Analysis ===
-        # Encode images for LLM
-        original_buffer = io.BytesIO()
-        image.save(original_buffer, format="PNG")
-        original_base64 = base64.b64encode(original_buffer.getvalue()).decode("utf-8")
-        
-        mask_buffer = io.BytesIO()
-        mask_image.save(mask_buffer, format="PNG")
-        mask_base64 = base64.b64encode(mask_buffer.getvalue()).decode("utf-8")
-
-        # Get professional analysis from Gemini
-        llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
-        message = HumanMessage(
-            content=[
-                {
-                    "type": "text",
-                     "text": " This is an image and its predicted binary mask showing manipulated regions in white. "
-                     "Please explain briefly where the manipulation occurred and what might have been altered."
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {"url": f"data:image/jpeg;base64,{original_base64}"},
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {"url": f"data:image/png;base64,{mask_base64}"},
-                },
-            ]
-        )
-        llm_response = llm.invoke([message]).content
-
-        # === Generate PDF Report ===
-        buffer = io.BytesIO()
-        c = canvas.Canvas(buffer, pagesize=A4)
-        width, height = A4
-
-        # === Professional Report Design ===
-        # Light blue background
-        c.setFillColorRGB(0.96, 0.96, 1)
-        c.rect(0, 0, width, height, fill=1, stroke=0)
-
-        # Dark blue header
-        c.setFillColorRGB(0, 0.2, 0.4)
-        c.rect(0, height-80, width, 80, fill=1, stroke=0)
-        
-        # Title
-        c.setFillColorRGB(1, 1, 1)
-        c.setFont("Helvetica-Bold", 18)
-        c.drawCentredString(width/2, height-50, "DIGITAL IMAGE AUTHENTICITY REPORT")
-        c.setFont("Helvetica", 10)
-        c.drawCentredString(width/2, height-70, "Forensic Analysis Report")
-
-        # Metadata
-        c.setFillColorRGB(0, 0, 0)
-        c.setFont("Helvetica", 9)
-        c.drawString(40, height-100, f"Report Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-        case_id = get_case_id()
-        c.drawString(width-200, height-100, f"Case ID: {case_id}")
-
-        # Divider
-        c.setStrokeColorRGB(0, 0.4, 0.6)
-        c.setLineWidth(1)
-        c.line(40, height-110, width-40, height-110)
-
-        # === Analysis Summary ===
-        c.setFillColorRGB(0, 0.3, 0.6)
-        c.setFont("Helvetica-Bold", 12)
-        c.drawString(40, height-140, "EXECUTIVE SUMMARY")
-        
-        c.setFillColorRGB(0, 0, 0)
-        c.setFont("Helvetica", 10)
-        summary_text = [
-            "This report presents forensic analysis of potential digital manipulations",
-            "using state-of-the-art AI detection models. Key findings are summarized below."
-        ]
-        text_object = c.beginText(40, height-160)
-        text_object.setFont("Helvetica", 10)
-        text_object.setLeading(14)
-        for line in summary_text:
-            text_object.textLine(line)
-        c.drawText(text_object)
-
-        # === Image Evidence ===
-        img_y = height-420
-        img_width = 220
-        img_height = 220
-        
-        # Original Image
-        c.drawImage("temp_input.png", 40, img_y, width=img_width, height=img_height)
-        c.setFillColorRGB(0, 0.3, 0.6)
-        c.setFont("Helvetica-Bold", 10)
-        c.drawString(40, img_y-20, "ORIGINAL IMAGE")
-        
-        # Detection Result
-        c.drawImage("temp_mask.png", width-260, img_y, width=img_width, height=img_height)
-        c.drawString(width-260, img_y-20, "DETECTION HEATMAP")
-
-        # === AI Analysis Section ===
-        c.setFillColorRGB(0, 0.3, 0.6)
-        c.setFont("Helvetica-Bold", 12)
-        c.drawString(40, img_y-50, "AI FORENSIC ANALYSIS")
-        
-        # Format LLM response with proper line breaks
-        from textwrap import wrap
-        analysis_lines = []
-        for paragraph in llm_response.split('\n'):
-            analysis_lines.extend(wrap(paragraph, width=90))
-        
-        text_object = c.beginText(40, img_y-70)
-        text_object.setFont("Helvetica", 10)
-        text_object.setLeading(14)
-        
-        # Show first 10 lines (adjust based on space)
-        for line in analysis_lines[:10]:
-            text_object.textLine(line)
-        
-        if len(analysis_lines) > 10:
-            text_object.textLine("\n[Full analysis available in digital report]")
-        
-        c.drawText(text_object)
-
-        # === Technical Details ===
-        c.setFillColorRGB(0, 0.3, 0.6)
-        c.setFont("Helvetica-Bold", 12)
-        c.drawString(40, img_y-180, "TECHNICAL SPECIFICATIONS")
-        
-        c.setFillColorRGB(0, 0, 0)
-        c.setFont("Helvetica", 10)
-        tech_details = [
-            f"Analysis Model: Mask2Former-Swin (ADE20K Fine-tuned)",
-            #f"Detection Threshold: {segmentation_mask.max():.2f} confidence",
-            f"Processing Date: {datetime.now().strftime('%Y-%m-%d')}",
-            "Report Version: 1.1"
-        ]
-        text_object = c.beginText(40, img_y-200)
-        text_object.setFont("Helvetica", 10)
-        text_object.setLeading(14)
-        for line in tech_details:
-            text_object.textLine(line)
-        c.drawText(text_object)
-
-        # === Footer ===
-        c.setFillColorRGB(0, 0.2, 0.4)
-        c.rect(0, 40, width, 40, fill=1, stroke=0)
-        c.setFillColorRGB(1, 1, 1)
-        c.setFont("Helvetica", 8)
-        c.drawCentredString(width/2, 65, "This report was generated by AI forensic tools and should be verified by human experts")
-        c.drawCentredString(width/2, 55, "Sukkur IBA University | Digital Forensics Lab | © 2024 Deepfake Research Project")
-
-        c.save()
-        buffer.seek(0)
-
-        # Cleanup
-        os.remove("temp_input.png")
-        os.remove("temp_mask.png")
-
-        return send_file(
-            buffer,
-            mimetype='application/pdf',
-            as_attachment=True,
-            download_name=f"forensic_report_{datetime.now().strftime('%Y%m%d_%H%M')}.pdf"
-        )
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=False)
+from PIL import Image
+import numpy as np
+import base64
+from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import google.generativeai as genai
+from langchain_core.messages import HumanMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from reportlab.lib.utils import ImageReader
+from flask import send_file, jsonify, request
+from reportlab.pdfgen import canvas
+from reportlab.lib.pagesizes import A4
+from reportlab.lib.units import inch
+import io, torch, os
+from reportlab.lib import colors
+from datetime import datetime
+
+os.environ['GOOGLE_API_KEY'] = "AIzaSyCv2dNQMCD3-9s3E5Th7bDy4ko0dyucRCc"
+genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+
+# Setup
+app = Flask(__name__)
+CORS(app)
+
+# Initialize device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model and processor
+processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
+model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic")
+# model.load_state_dict(torch.load(r"E:\FYP Work\FYP_code\backend\mask2former-ade-(splicing1_2).pth", map_location=device))
+model.load_state_dict(torch.load(r"mask2former-ade-(splicing1_2).pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+# ========== Flask routes ==========
+
+@app.route('/')
+def home():
+    return "Backend is running!"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image uploaded"}), 400
+    
+    try:
+        file = request.files['image']
+        image = Image.open(io.BytesIO(file.read()))
+        
+        # Convert to RGB if needed
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+            
+        # Encode original image to base64
+        original_image_buffer = io.BytesIO()
+        image.save(original_image_buffer, format="PNG")
+        original_image_base64 = base64.b64encode(original_image_buffer.getvalue()).decode("utf-8")
+            
+        # Process image using Mask2Former processor
+        inputs = processor(images=image, return_tensors="pt").to(device)
+        
+        # Predict
+        with torch.no_grad():
+            outputs = model(**inputs)
+            
+        # Process outputs
+        predicted_segmentation = processor.post_process_semantic_segmentation(
+            outputs, target_sizes=[image.size[::-1]]
+        )[0]
+        
+        # Convert to numpy array for visualization
+        segmentation_mask = predicted_segmentation.cpu().numpy()
+        
+        # ========== Create visualizations ==========
+        # Create side-by-side plot
+        fig, axes = plt.subplots(1, 2, figsize=(10, 5))
+        axes[0].imshow(image)
+        axes[0].set_title("Input Image")
+        axes[1].imshow(segmentation_mask)
+        axes[1].set_title("Prediction")
+        
+        for ax in axes:
+            ax.axis("off")
+        plt.tight_layout()
+
+        # Save visualization to buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format="png", bbox_inches='tight', pad_inches=0)
+        buf.seek(0)
+        visualization_base64 = base64.b64encode(buf.read()).decode('utf-8')
+        plt.close()
+
+        # ========== Encode mask separately ==========
+        # Normalize mask to 0-255 range
+        mask_normalized = (segmentation_mask - segmentation_mask.min()) * (255.0 / (segmentation_mask.max() - segmentation_mask.min()))
+        mask_image = Image.fromarray(mask_normalized.astype(np.uint8))
+        
+        mask_buffer = io.BytesIO()
+        mask_image.save(mask_buffer, format="PNG")
+        mask_base64 = base64.b64encode(mask_buffer.getvalue()).decode("utf-8")
+
+
+        #VLM code
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
+
+        # Create multimodal message
+        message = HumanMessage(
+            content=[
+                {
+                    "type": "text",
+                    #"text": "Please explain briefly where the manipulation has been occured, don't use mask"
+                     "text": " This is an image and its predicted binary mask showing manipulated regions in white. "
+                     "Please explain briefly in 2-3 lines where the manipulation occurred and what might have been altered."
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{original_image_base64}"
+                    },
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/png;base64,{mask_base64}"
+                    },
+                },
+            ]
+        )
+
+        # Get response
+        response = llm.invoke([message])
+        print(response.content)
+
+        return jsonify({
+            "original_image": original_image_base64,
+            "mask": mask_base64,
+            "visualization": visualization_base64,
+            "message": response.content
+        })
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+import json
+from threading import Lock
+
+counter_file = "counter.json"
+counter_lock = Lock()
+
+def get_case_id():
+    today = datetime.now().strftime('%Y%m%d')
+
+    with counter_lock:
+        if os.path.exists(counter_file):
+            with open(counter_file, "r") as f:
+                data = json.load(f)
+        else:
+            data = {}
+
+        count = data.get(today, 0) + 1
+        data[today] = count
+
+        with open(counter_file, "w") as f:
+            json.dump(data, f)
+
+    return f"DFD-{today}-{count:03d}"
+
+
+@app.route('/download-report', methods=['POST'])
+def download_report():
+    try:
+        file = request.files['image']
+        image = Image.open(io.BytesIO(file.read())).convert("RGB")
+
+        # === Process Image ===
+        inputs = processor(images=image, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        predicted_segmentation = processor.post_process_semantic_segmentation(
+            outputs, target_sizes=[image.size[::-1]]
+        )[0]
+        segmentation_mask = predicted_segmentation.cpu().numpy()
+
+        # === Create Mask Image ===
+        mask_normalized = (segmentation_mask - segmentation_mask.min()) * (255.0 / (segmentation_mask.max() - segmentation_mask.min()))
+        mask_image = Image.fromarray(mask_normalized.astype(np.uint8)).convert("L")
+
+        # === Prepare Images ===
+        image.save("temp_input.png")
+        mask_image.save("temp_mask.png")
+
+        # === Get LLM Analysis ===
+        # Encode images for LLM
+        original_buffer = io.BytesIO()
+        image.save(original_buffer, format="PNG")
+        original_base64 = base64.b64encode(original_buffer.getvalue()).decode("utf-8")
+        
+        mask_buffer = io.BytesIO()
+        mask_image.save(mask_buffer, format="PNG")
+        mask_base64 = base64.b64encode(mask_buffer.getvalue()).decode("utf-8")
+
+        # Get professional analysis from Gemini
+        llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+        message = HumanMessage(
+            content=[
+                {
+                    "type": "text",
+                     "text": " This is an image and its predicted binary mask showing manipulated regions in white. "
+                     "Please explain briefly where the manipulation occurred and what might have been altered."
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{original_base64}"},
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/png;base64,{mask_base64}"},
+                },
+            ]
+        )
+        llm_response = llm.invoke([message]).content
+
+        # === Generate PDF Report ===
+        buffer = io.BytesIO()
+        c = canvas.Canvas(buffer, pagesize=A4)
+        width, height = A4
+
+        # === Professional Report Design ===
+        # Light blue background
+        c.setFillColorRGB(0.96, 0.96, 1)
+        c.rect(0, 0, width, height, fill=1, stroke=0)
+
+        # Dark blue header
+        c.setFillColorRGB(0, 0.2, 0.4)
+        c.rect(0, height-80, width, 80, fill=1, stroke=0)
+        
+        # Title
+        c.setFillColorRGB(1, 1, 1)
+        c.setFont("Helvetica-Bold", 18)
+        c.drawCentredString(width/2, height-50, "DIGITAL IMAGE AUTHENTICITY REPORT")
+        c.setFont("Helvetica", 10)
+        c.drawCentredString(width/2, height-70, "Forensic Analysis Report")
+
+        # Metadata
+        c.setFillColorRGB(0, 0, 0)
+        c.setFont("Helvetica", 9)
+        c.drawString(40, height-100, f"Report Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        case_id = get_case_id()
+        c.drawString(width-200, height-100, f"Case ID: {case_id}")
+
+        # Divider
+        c.setStrokeColorRGB(0, 0.4, 0.6)
+        c.setLineWidth(1)
+        c.line(40, height-110, width-40, height-110)
+
+        # === Analysis Summary ===
+        c.setFillColorRGB(0, 0.3, 0.6)
+        c.setFont("Helvetica-Bold", 12)
+        c.drawString(40, height-140, "EXECUTIVE SUMMARY")
+        
+        c.setFillColorRGB(0, 0, 0)
+        c.setFont("Helvetica", 10)
+        summary_text = [
+            "This report presents forensic analysis of potential digital manipulations",
+            "using state-of-the-art AI detection models. Key findings are summarized below."
+        ]
+        text_object = c.beginText(40, height-160)
+        text_object.setFont("Helvetica", 10)
+        text_object.setLeading(14)
+        for line in summary_text:
+            text_object.textLine(line)
+        c.drawText(text_object)
+
+        # === Image Evidence ===
+        img_y = height-420
+        img_width = 220
+        img_height = 220
+        
+        # Original Image
+        c.drawImage("temp_input.png", 40, img_y, width=img_width, height=img_height)
+        c.setFillColorRGB(0, 0.3, 0.6)
+        c.setFont("Helvetica-Bold", 10)
+        c.drawString(40, img_y-20, "ORIGINAL IMAGE")
+        
+        # Detection Result
+        c.drawImage("temp_mask.png", width-260, img_y, width=img_width, height=img_height)
+        c.drawString(width-260, img_y-20, "DETECTION HEATMAP")
+
+        # === AI Analysis Section ===
+        c.setFillColorRGB(0, 0.3, 0.6)
+        c.setFont("Helvetica-Bold", 12)
+        c.drawString(40, img_y-50, "AI FORENSIC ANALYSIS")
+        
+        # Format LLM response with proper line breaks
+        from textwrap import wrap
+        analysis_lines = []
+        for paragraph in llm_response.split('\n'):
+            analysis_lines.extend(wrap(paragraph, width=90))
+        
+        text_object = c.beginText(40, img_y-70)
+        text_object.setFont("Helvetica", 10)
+        text_object.setLeading(14)
+        
+        # Show first 10 lines (adjust based on space)
+        for line in analysis_lines[:10]:
+            text_object.textLine(line)
+        
+        if len(analysis_lines) > 10:
+            text_object.textLine("\n[Full analysis available in digital report]")
+        
+        c.drawText(text_object)
+
+        # === Technical Details ===
+        c.setFillColorRGB(0, 0.3, 0.6)
+        c.setFont("Helvetica-Bold", 12)
+        c.drawString(40, img_y-180, "TECHNICAL SPECIFICATIONS")
+        
+        c.setFillColorRGB(0, 0, 0)
+        c.setFont("Helvetica", 10)
+        tech_details = [
+            f"Analysis Model: Mask2Former-Swin (ADE20K Fine-tuned)",
+            #f"Detection Threshold: {segmentation_mask.max():.2f} confidence",
+            f"Processing Date: {datetime.now().strftime('%Y-%m-%d')}",
+            "Report Version: 1.1"
+        ]
+        text_object = c.beginText(40, img_y-200)
+        text_object.setFont("Helvetica", 10)
+        text_object.setLeading(14)
+        for line in tech_details:
+            text_object.textLine(line)
+        c.drawText(text_object)
+
+        # === Footer ===
+        c.setFillColorRGB(0, 0.2, 0.4)
+        c.rect(0, 40, width, 40, fill=1, stroke=0)
+        c.setFillColorRGB(1, 1, 1)
+        c.setFont("Helvetica", 8)
+        c.drawCentredString(width/2, 65, "This report was generated by AI forensic tools and should be verified by human experts")
+        c.drawCentredString(width/2, 55, "Sukkur IBA University | Digital Forensics Lab | © 2024 Deepfake Research Project")
+
+        c.save()
+        buffer.seek(0)
+
+        # Cleanup
+        os.remove("temp_input.png")
+        os.remove("temp_mask.png")
+
+        return send_file(
+            buffer,
+            mimetype='application/pdf',
+            as_attachment=True,
+            download_name=f"forensic_report_{datetime.now().strftime('%Y%m%d_%H%M')}.pdf"
+        )
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860, debug=False)