ShellyMimo/agrigrow

1import pandas as pd
2from sentence_transformers import SentenceTransformer
3import faiss
4import os
5
6class AgricultureBot:
7    def __init__(self):
8        # Load agricultural data from CSV
9        if not os.path.exists('agriculture_data.csv'):
10            raise FileNotFoundError("The file 'agriculture_data.csv' was not found. Please make sure it exists in the directory.")
11        
12        self.agriculture_data = pd.read_csv('agriculture_data.csv')
13
14        # Initialize SentenceTransformer model for encoding advice texts
15        self.model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
16        self.index = self.create_faiss_index()
17
18    def create_faiss_index(self):
19        # Convert advice texts to vectors
20        advice_vectors = self.model.encode(self.agriculture_data['advice_text'].tolist())
21        # Create a FAISS index
22        index = faiss.IndexFlatL2(advice_vectors.shape[1])
23        index.add(advice_vectors)
24        return index  # Return the newly created index
25
26    def get_agricultural_advice(self, user_input):
27        # Convert user input to a vector
28        user_vector = self.model.encode([user_input])
29        # Search the FAISS index
30        _, advice_ids = self.index.search(user_vector, k=1)
31        # Retrieve the most relevant advice
32        most_relevant_advice = self.agriculture_data.iloc[advice_ids[0][0]]['advice_text']
33
34        # Additional processing to make the response more precise
35        precise_advice = self.process_advice(most_relevant_advice)
36
37        return precise_advice
38
39    def process_advice(self, advice):
40        # Example of processing to make the advice more precise
41        if "Rotate crops to prevent soil depletion and improve yield." in advice:
42            advice += "\nConsider integrating cover crops like legumes to enhance soil fertility."
43
44        return advice
45