Update app.py

Author: aimtyaem

app.py

@@ -1,86 +1,89 @@
-# Azure Web app.py
 from flask import Flask, jsonify, request
 from flask_cors import CORS
 import numpy as np
-import faiss
-import os
 
 app = Flask(__name__)
-CORS(app)  # Enable CORS for cross-origin requests
+CORS(app)
 
-# Agricultural best practices database
-AGRICULTURE_KNOWLEDGE_BASE = {
-    "recommendations": [
-        "Implement precision irrigation scheduling",
-        "Adopt integrated pest management",
-        "Use soil moisture sensors",
-        "Apply crop rotation strategies",
-        "Monitor weather patterns for planting",
-        "Utilize organic fertilizers",
-        "Practice conservation tillage",
-        "Install windbreaks for soil protection"
-    ],
-    "details": [
-        """Precision Irrigation Best Practices:
-- Use soil moisture sensors for data-driven watering
-- Implement drip irrigation systems
-- Schedule irrigation during cooler hours
-- Monitor evapotranspiration rates
-- Adjust for crop growth stages""",
-        
-        """Integrated Pest Management:
-- Regular field scouting
-- Use biological control agents
-- Implement trap cropping
-- Apply targeted pesticides
-- Maintain pest monitoring records""",
-        
-        # Add other details entries...
-    ]
+# 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"""
 }
 
-# Create embeddings and FAISS index
-embeddings = np.random.randn(len(AGRICULTURE_KNOWLEDGE_BASE["recommendations"]), 128).astype('float32')
-index = faiss.IndexFlatL2(128)
-index.add(embeddings)
+# 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 list of agricultural best practices recommendations"""
-    return jsonify({
-        "source": "FAO Agricultural Best Practices Database",
-        "recommendations": AGRICULTURE_KNOWLEDGE_BASE["recommendations"],
-        "last_updated": "2024-03-15"
-    })
+    return jsonify({"recommendations": recommendations})
 
-@app.route('/details', methods=['POST'])
-def get_details():
-    """Get detailed information for a specific recommendation"""
-    data = request.json
-    try:
-        index = int(data.get('index'))
-        if index < 0 or index >= len(AGRICULTURE_KNOWLEDGE_BASE["recommendations"]):
-            raise ValueError
-    except (ValueError, TypeError):
-        return jsonify({"error": "Invalid recommendation index"}), 400
+@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({
-        "recommendation": AGRICULTURE_KNOWLEDGE_BASE["recommendations"][index],
-        "details": AGRICULTURE_KNOWLEDGE_BASE["details"][index],
-        "related_resources": [
-            "FAO Guidelines",
-            "Local Extension Services",
-            "Climate Smart Agriculture Handbook"
-        ]
-    })
-
-@app.route('/')
-def health_check():
-    return jsonify({
-        "status": "active",
-        "service": "Agricultural Best Practices API",
-        "version": "1.2.0"
+        "query": query,
+        "matched_recommendation": best_match,
+        "details": detail
     })
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 5000)))
+    app.run(host='0.0.0.0', port=5000)