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app.py

@@ -1,7 +1,171 @@
 import gradio as gr
+from roboflow import Roboflow
+import os
+import tempfile
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+import numpy as np
+
+# Initialize Roboflow
+rf = Roboflow(api_key="E5qhgf3ZimDoTx5OfgZ8")
+project = rf.workspace().project("newhassae")
+
+def get_model(version):
+    return project.version(version).model
+
+def preprocess_image(img, version):
+    # Initial crop for all images
+    img = img.crop((682, 345, 682+2703, 345+1403))
+    
+    # Model specific processing
+    if version == 1:
+        return img.resize((640, 640))
+    elif version == 2:
+        return img
+    elif version == 3:
+        width, height = img.size
+        left = (width - 640) // 2
+        top = (height - 640) // 2
+        right = left + 640
+        bottom = top + 640
+        return img.crop((left, top, right, bottom))
+    return img
+
+def process_images(image_files, version):
+    model = get_model(version)
+    results = []
+    if not isinstance(image_files, list):
+        image_files = [image_files]
+        
+    for image_file in image_files:
+        try:
+            with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
+                temp_file.write(image_file)
+                temp_path = temp_file.name
+            
+            img = Image.open(temp_path)
+            processed_img = preprocess_image(img, version)
+            
+            processed_temp = tempfile.NamedTemporaryFile(suffix='.jpg', delete=False)
+            processed_img.save(processed_temp.name)
+            
+            try:
+                prediction = model.predict(processed_temp.name).json()
+                predicted_class = prediction["predictions"][0]["predictions"][0]["class"]
+                confidence = f"{float(prediction['predictions'][0]['predictions'][0]['confidence']) * 100:.1f}%"
+            except Exception as e:
+                predicted_class = "Error"
+                confidence = "N/A"
+            
+            if processed_img.mode != 'RGB':
+                processed_img = processed_img.convert('RGB')
+                
+            labeled_img = add_label_to_image(processed_img, predicted_class, confidence)
+            
+            top_result = {
+                "predicted_class": predicted_class,
+                "confidence": confidence
+            }
+            
+            results.append((labeled_img, top_result))
+            
+        except Exception as e:
+            gr.Warning(f"Error processing image: {str(e)}")
+            continue
+        finally:
+            if 'temp_path' in locals():
+                os.unlink(temp_path)
+            if 'processed_temp' in locals():
+                os.unlink(processed_temp.name)
+    
+    return results if results else [(Image.new('RGB', (400, 400), 'grey'), {"predicted_class": "Error", "confidence": "N/A"})]
+
+
+
+def add_label_to_image(image, prediction, confidence):
+    # Convert PIL image to OpenCV format
+    img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    
+    # Image dimensions
+    img_height, img_width = img_cv.shape[:2]
+    padding = int(img_width * 0.02)
+    
+    # Rectangle dimensions
+    rect_height = int(img_height * 0.15)
+    rect_width = img_width - (padding * 2)
+    
+    # Draw red rectangle
+    cv2.rectangle(img_cv, 
+                 (padding, padding), 
+                 (padding + rect_width, padding + rect_height), 
+                 (0, 0, 255), 
+                 -1)
+    
+    text = f"{prediction}: {confidence}"
+    
+    # Text settings
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 3.0
+    thickness = 8
+    
+    # Get text size and position
+    (text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, thickness)
+    text_x = padding + (rect_width - text_width) // 2
+    text_y = padding + (rect_height + text_height) // 2
+    
+    # Draw white text
+    cv2.putText(img_cv, text, (text_x, text_y), font, font_scale, (255, 255, 255), thickness)
+    
+    # Convert back to PIL
+    img_pil = Image.fromarray(cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB))
+    return img_pil
+
+def display_results(image_files, version):
+    results = process_images(image_files, version)
+    output_images = [res[0] for res in results]
+    predictions = [res[1] for res in results]
+    
+    return output_images, predictions
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.HTML("""
+        <div style="text-align: center; margin-bottom: 1rem">
+            <img src="https://haeab.se/wp-content/uploads/2023/12/ad.png" alt="Logo" style="height: 100px;">
+        </div>
+    """)
+    gr.Markdown("Hans Andersson Entrepenad")
+    
+    with gr.Row():
+        with gr.Column():
+            model_version = gr.Slider(
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=1,
+                label="Model Version",
+                interactive=True
+            )
+            image_input = gr.File(
+                label="Upload Image(s)",
+                file_count="multiple",
+                type="binary"
+            )
+        
+        with gr.Column():
+            image_output = gr.Gallery(label="Processed Images")
+            text_output = gr.JSON(
+                label="Top Predictions",
+                height=400,  # Increases height
+                container=True,  # Adds a container around the JSON
+                    show_label=True
+                )
+    
+    submit_btn = gr.Button("Analyze Images")
+    submit_btn.click(
+        fn=display_results,
+        inputs=[image_input, model_version],
+        outputs=[image_output, text_output]
+    )
+demo.launch(share=True, debug=True, show_error=True)
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch(share=True)