Create app.py

Created

Author: aimtyaem

app.py

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+from flask import Flask, jsonify, request
+from flask_cors import CORS
+import numpy as np
+
+app = Flask(__name__)
+CORS(app)
+
+# Expert recommendations
+recommendations = [
+    "Optimize irrigation scheduling",
+    "Implement field health monitoring",
+    "Set up pest & disease alerts",
+    "Conduct soil analysis & fertilization advice",
+    "Follow weather-based farming recommendations"
+]
+
+# Detailed knowledge base for each recommendation
+detailed_knowledge = {
+    "Optimize irrigation scheduling": """Irrigation Scheduling Details:
+- Use soil moisture sensors for precision watering
+- Adjust schedules based on crop growth stages
+- Consider evapotranspiration rates
+- Implement drip irrigation for efficiency""",
+
+    "Implement field health monitoring": """Field Health Monitoring Details:
+- Regular satellite imagery analysis
+- Drone-based NDVI scans weekly
+- Soil nutrient level tracking
+- Early stress detection algorithms""",
+
+    "Set up pest & disease alerts": """Pest & Disease Alerts Details:
+- Automated pheromone trap monitoring
+- Weather-based outbreak prediction
+- Image recognition for disease identification
+- Integrated pest management strategies""",
+
+    "Conduct soil analysis & fertilization advice": """Soil Analysis Details:
+- Seasonal nutrient profiling
+- pH balance optimization
+- Organic matter content analysis
+- Custom fertilizer blending recommendations""",
+
+    "Follow weather-based farming recommendations": """Weather-based Farming Details:
+- Microclimate prediction models
+- Frost/heatwave early warning systems
+- Rainfall pattern adaptation
+- Crop variety selection advisor"""
+}
+
+# Convert recommendations into numerical vectors for search
+def text_to_vector(text):
+    return np.array([ord(char) for char in text])
+
+recommendation_vectors = {rec: text_to_vector(rec) for rec in recommendations}
+
+def find_best_match(query):
+    """Find the most relevant recommendation using cosine similarity"""
+    query_vector = text_to_vector(query)
+    similarities = {
+        rec: np.dot(query_vector, rec_vector) / (np.linalg.norm(query_vector) * np.linalg.norm(rec_vector))
+        for rec, rec_vector in recommendation_vectors.items()
+    }
+    return max(similarities, key=similarities.get)
+
+@app.route('/')
+def home():
+    return jsonify({"message": "Welcome to the Agricultural Expert System API!"})
+
+@app.route('/recommendations', methods=['GET'])
+def get_recommendations():
+    return jsonify({"recommendations": recommendations})
+
+@app.route('/recommendation/detail', methods=['GET'])
+def get_recommendation_detail():
+    query = request.args.get('query', '').strip()
+    if not query:
+        return jsonify({"error": "Query parameter is required"}), 400
+
+    best_match = find_best_match(query)
+    detail = detailed_knowledge.get(best_match, "No details available.")
+
+    return jsonify({
+        "query": query,
+        "matched_recommendation": best_match,
+        "details": detail
+    })
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)