upload

Dockerfile

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+# Use Python as the base image
+FROM python:3.8
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the project files
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port 7860
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, render_template, send_file
+from inference_sdk import InferenceHTTPClient
+from PIL import Image, ImageDraw, ImageFont
+import os
+from collections import defaultdict
+
+app = Flask(__name__)
+
+# Securely get API key from Hugging Face Secrets
+API_KEY = os.getenv("ROBOFLOW_API_KEY")  
+
+# Initialize the Roboflow client
+CLIENT = InferenceHTTPClient(
+    api_url="https://detect.roboflow.com",
+    api_key=API_KEY
+)
+
+# Model settings
+MODEL_ID = "hvacsym/5"
+CONFIDENCE_THRESHOLD = 30  # 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", 15)
+    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 = "static/final_detected_image.png"
+    final_image.save(final_image_path)
+
+    return final_image_path, total_counts
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        # Get uploaded image
+        image_file = request.files["image"]
+        if image_file:
+            image_path = "static/uploaded_image.png"
+            image_file.save(image_path)
+
+            # Process image
+            final_image_path, total_counts = process_image(image_path)
+
+            return render_template("index.html", final_image=final_image_path, counts=total_counts)
+
+    return render_template("index.html", final_image=None, counts=None)
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

requirements.txt

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+flask
+pillow
+inference-sdk

static/style.css

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+body {
+    font-family: Arial, sans-serif;
+    text-align: center;
+    background-color: #f8f8f8;
+}
+
+h1 {
+    color: #333;
+}
+
+form {
+    margin: 20px;
+}
+
+button {
+    background-color: #4CAF50;
+    color: white;
+    padding: 10px 20px;
+    border: none;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #45a049;
+}
+
+img {
+    width: 50%;
+    margin-top: 20px;
+    border-radius: 5px;
+}

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>HVAC Symbol Detector</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+    <h1>HVAC Symbol Detector</h1>
+
+    <form action="/" method="POST" enctype="multipart/form-data">
+        <input type="file" name="image" accept="image/*" required>
+        <button type="submit">Upload & Detect</button>
+    </form>
+
+    {% if final_image %}
+        <h2>Processed Image</h2>
+        <img src="{{ final_image }}" alt="Processed Image">
+        
+        <h2>Detected Symbols</h2>
+        <ul>
+            {% for label, count in counts.items() %}
+                <li>{{ label }}: {{ count }}</li>
+            {% endfor %}
+        </ul>
+    {% endif %}
+</body>
+</html>