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7cc0170 af4689d 7cc0170 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | import psycopg2
from dotenv import load_dotenv
import os
from functools import lru_cache
from transformers import pipeline
load_dotenv()
class Database:
def __init__(self):
try:
self.conn = psycopg2.connect(os.getenv("POSTGRES_URL"))
self.cursor = self.conn.cursor()
# Verify tables exist on startup
self.cursor.execute("""
CREATE TABLE IF NOT EXISTS messages (
id SERIAL PRIMARY KEY,
sender VARCHAR(50),
body TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""")
self.conn.commit()
self.cursor.execute("""
SELECT EXISTS (
SELECT FROM information_schema.tables
WHERE table_name = 'users'
)
""")
if not self.cursor.fetchone()[0]:
raise RuntimeError("Database tables not initialized")
except Exception as e:
print(f"Database connection error: {str(e)}")
raise
def find_technician(self, service_type: str, longitude: float, latitude: float):
query = """
SELECT id, name, contact, rating
FROM technicians
WHERE %s = ANY(qualifications)
AND availability = 'available'
ORDER BY location <-> ST_SetSRID(ST_MakePoint(%s, %s), 4326)
LIMIT 1
"""
self.cursor.execute(query, (service_type, longitude, latitude))
result = self.cursor.fetchone()
if result:
return {
"id": result[0],
"name": result[1],
"contact": result[2],
"rating": result[3]
}
return None
def get_user_state(self, user_number: str):
query = "SELECT state, last_message FROM users WHERE number = %s"
self.cursor.execute(query, (user_number,))
result = self.cursor.fetchone()
return {"state": result[0], "last_message": result[1]} if result else None
def update_user_state(self, user_number: str, state: str, last_message: str):
query = """
INSERT INTO users (number, state, last_message)
VALUES (%s, %s, %s)
ON CONFLICT (number) DO UPDATE
SET state = %s, last_message = %s, updated_at = CURRENT_TIMESTAMP
"""
self.cursor.execute(query, (user_number, state, last_message, state, last_message))
self.conn.commit()
def save_request(self, user_number: str, technician_id: int, service_type: str):
query = """
INSERT INTO requests (user_number, technician_id, service_type, status)
VALUES (%s, %s, %s, %s)
RETURNING id
"""
self.cursor.execute(query, (user_number, technician_id, service_type, "pending"))
self.conn.commit()
return self.cursor.fetchone()[0]
def close(self):
self.cursor.close()
self.conn.close()
class NLPProcessor:
def __init__(self):
# Initialize all required attributes
self.api_url = "https://api-inference.huggingface.co/models/Christy123/service-classifier"
self.api_token = os.getenv("HF_API_TOKEN") # Make sure this is in your .env
self.ner_url = "https://api-inference.huggingface.co/models/dbmdz/bert-large-cased-finetuned-conll03-english"
self.service_mappings = {
"ac": "hvac",
"air conditioner": "hvac",
"plumb": "plumbing",
"pipe": "plumbing",
"electr": "electrical",
"wiring": "electrical"
}
def extract_service(self, text: str) -> str:
"""Enhanced service classification with fallback"""
try:
# Try Hugging Face API first
headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
response = requests.post(
"https://api-inference.huggingface.co/models/Christy123/service-classifier",
headers=headers,
json={"inputs": text},
timeout=5
)
if response.status_code == 200:
result = response.json()[0]
if result["score"] > 0.7: # Only accept confident predictions
return result["label"]
# Fallback to keyword matching
text_lower = text.lower()
for keyword, service in self.service_mappings.items():
if keyword in text_lower:
return service
return "unknown"
except Exception:
return "unknown"
def extract_location(self, text: str) -> str:
"""Enhanced location detection with fallback methods"""
headers = {"Authorization": f"Bearer {self.api_token}"}
# Try Hugging Face NER first
try:
response = requests.post(
"https://api-inference.huggingface.co/models/dbmdz/bert-large-cased-finetuned-conll03-english",
headers=headers,
json={"inputs": text},
timeout=5
)
if response.status_code == 200:
entities = response.json()
locations = [e["word"] for e in entities if e["entity_group"] == "LOC"]
if locations:
return locations[0]
except Exception:
pass
# Fallback 1: Simple keyword matching
kenyan_towns = ["Nairobi", "Mombasa", "Kisumu", "Nakuru", "Eldoret",
"Westlands", "Karen", "Runda", "Thika", "Naivasha"]
for town in kenyan_towns:
if town.lower() in text.lower():
return town
# Fallback 2: Look for "in <location>" pattern
import re
match = re.search(r"\bin\s+([A-Za-z]+)", text, re.IGNORECASE)
if match:
return match.group(1)
return None |