Create app.py

app.py

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+import gradio as gr
+from inference_sdk import InferenceHTTPClient
+from PIL import Image, ImageDraw, ImageFont
+import os
+from collections import defaultdict
+
+API_KEY = os.getenv("ROBOFLOW_API_KEY") 
+
+CLIENT = InferenceHTTPClient(
+    api_url="https://detect.roboflow.com",
+    api_key=API_KEY  
+)
+
+
+# Model settings
+MODEL_ID = "hvacsym/5"
+CONFIDENCE_THRESHOLD = 20  # Confidence threshold for filtering predictions
+GRID_SIZE = (4, 4)  # 4x4 segmentation
+
+def process_image(image_path):
+    """Processes an uploaded image and returns the final image with bounding boxes & symbol counts."""
+    original_image = Image.open(image_path)
+    width, height = original_image.size
+    seg_w, seg_h = width // GRID_SIZE[1], height // GRID_SIZE[0]
+
+    # Create a copy of the image for bounding boxes
+    final_image = original_image.copy()
+    draw_final = ImageDraw.Draw(final_image)
+
+    # Load font
+    try:
+        font = ImageFont.truetype("arial.ttf", 10)
+    except:
+        font = ImageFont.load_default()
+
+    # Dictionary for total counts
+    total_counts = defaultdict(int)
+
+    # Process each segment
+    for row in range(GRID_SIZE[0]):
+        for col in range(GRID_SIZE[1]):
+            x1, y1 = col * seg_w, row * seg_h
+            x2, y2 = (col + 1) * seg_w, (row + 1) * seg_h
+
+            segment = original_image.crop((x1, y1, x2, y2))
+            segment_path = f"segment_{row}_{col}.png"
+            segment.save(segment_path)
+
+            # Run inference
+            result = CLIENT.infer(segment_path, model_id=MODEL_ID)
+            filtered_predictions = [
+                pred for pred in result["predictions"] if pred["confidence"] * 100 >= CONFIDENCE_THRESHOLD
+            ]
+
+            # Draw bounding boxes and update counts
+            for obj in filtered_predictions:
+                sx, sy, sw, sh = obj["x"], obj["y"], obj["width"], obj["height"]
+                class_name = obj["class"]
+                confidence = obj["confidence"]
+                total_counts[class_name] += 1
+
+                # Adjust coordinates for final image
+                x_min, y_min = x1 + (sx - sw // 2), y1 + (sy - sh // 2)
+                x_max, y_max = x1 + (sx + sw // 2), y1 + (sy + sh // 2)
+
+                # Draw bounding box
+                draw_final.rectangle([x_min, y_min, x_max, y_max], outline="green", width=2)
+
+                # Draw label
+                text = f"{class_name} {confidence:.2f}"
+                draw_final.text((x_min + 2, y_min - 10), text, fill="white", font=font)
+
+    # Save final image with bounding boxes
+    final_image_path = "final_detected_image.png"
+    final_image.save(final_image_path)
+
+    return final_image_path, total_counts
+
+def process_uploaded_image(image):
+    """Gradio wrapper function that calls `process_image` and formats the output."""
+    final_image_path, total_counts = process_image(image)
+
+    # Convert count dictionary to readable text
+    count_text = "\n".join([f"{label}: {count}" for label, count in total_counts.items()])
+
+    return final_image_path, count_text
+
+# Deploy with Gradio
+iface = gr.Interface(
+    fn=process_uploaded_image,
+    inputs=gr.Image(type="filepath"),  # Gradio input expects a file path
+    outputs=[gr.Image(type="filepath"), gr.Text()],
+    title="HVAC Symbol Detector",
+    description="Upload an HVAC blueprint image. The model will detect symbols and return the final image with bounding boxes along with symbol counts."
+)
+
+# Launch the Gradio app
+iface.launch()