app.py

from flask import Flask, request, jsonify
import torch
from transformers import AutoProcessor, AutoModelForImageClassification
from sentence_transformers import SentenceTransformer
import sqlite3

app = Flask(__name__)

# Load the defect detection model (open-source, hugging face model)
DETECTION_MODEL_NAME = "microsoft/beit-base-patch16-224-pt22k-ft22k"
processor = AutoProcessor.from_pretrained(DETECTION_MODEL_NAME)
detection_model = AutoModelForImageClassification.from_pretrained(DETECTION_MODEL_NAME)

defects_to_remedies = {
    "crack": "Fill cracks with epoxy. Structural cracks might need professional inspection.",
    "spalling": "Clean affected area and apply anti-corrosion primer before repairing.",
    "leakage": "Fix water source, seal with water-proofing compounds.",
    "mold": "Clean the mold, improve ventilation, and apply mold-resistant paint."
}

# Initialize a Sentence Transformer for text embeddings
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME)

# SQLite database setup
db_conn = sqlite3.connect('defects.db', check_same_thread=False)
c = db_conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS defects (id INTEGER PRIMARY KEY, defect TEXT, remedy TEXT, embedding BLOB)''')
db_conn.commit()

# Populate defect remedies table if empty
def seed_database():
    for defect, remedy in defects_to_remedies.items():
        c.execute("SELECT * FROM defects WHERE defect=?", (defect,))
        if not c.fetchone():
            embedding = embedding_model.encode(remedy).tolist()
            c.execute("INSERT INTO defects (defect, remedy, embedding) VALUES (?, ?, ?)", (defect, remedy, str(embedding)))
    db_conn.commit()

seed_database()

@app.route('/detect', methods=['POST'])
def detect_defect():
    if 'image' not in request.files:
        return jsonify({"error": "No image uploaded."}), 400

    image = request.files['image'].read()

    # Preprocess and predict the defect
    inputs = processor(images=image, return_tensors="pt")
    outputs = detection_model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
    predicted_class = torch.argmax(probs, dim=1)
    class_name = detection_model.config.id2label[predicted_class.item()]

    # Query remedy
    c.execute("SELECT remedy FROM defects WHERE defect=?", (class_name,))
    row = c.fetchone()
    if row:
        remedy = row[0]
    else:
        remedy = "No specific remedy available for this defect."

    return jsonify({"detected_defect": class_name, "remedy": remedy})

if __name__ == '__main__':
    app.run(debug=True)