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import pandas as pd
from sentence_transformers import SentenceTransformer
import faiss
def load_objections(file_path):
"""Load objections from a CSV file into a dictionary."""
try:
objections_df = pd.read_csv(file_path)
objections_dict = {}
for index, row in objections_df.iterrows():
objections_dict[row['Customer Objection']] = row['Salesperson Response']
return objections_dict
except Exception as e:
print(f"Error loading objections: {e}")
return {}
def check_objections(text, objections_dict):
"""Check for objections in the given text and return responses."""
responses = []
for objection, response in objections_dict.items():
if objection.lower() in text.lower():
responses.append(response)
return responses
class ObjectionHandler:
def __init__(self, objection_data_path):
self.data = pd.read_csv(objection_data_path)
self.model = SentenceTransformer('all-MiniLM-L6-v2')
self.embeddings = self.model.encode(self.data['Customer Objection'].tolist())
self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
self.index.add(self.embeddings)
def handle_objection(self, query, top_n=1):
"""Handle objections using embeddings."""
query_embedding = self.model.encode([query])
distances, indices = self.index.search(query_embedding, top_n)
responses = []
for i in indices[0]:
responses.append(self.data.iloc[i]['Salesperson Response'])
return responses |