Spaces:
Runtime error
Runtime error
File size: 4,149 Bytes
0424b36 8907dd8 0424b36 8907dd8 0424b36 8907dd8 0424b36 8907dd8 |
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 |
import redis
import psycopg2
from gradio_client import Client
import json
redis_url = 'redis://default:[email protected]:7369'
r = redis.from_url(redis_url)
# run josh's algorithm
client = Client("https://diverseco-metaformer.hf.space/")
# Postgres database get the observations
def connect():
conn = None
try:
# Connecting to your PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect('postgresql://postgres:[email protected]:7297/railway')
# conn = psycopg2.connect('postgresql://postgres:[email protected]:6771/railway') #staging
# conn = psycopg2.connect('postgresql://postgres:[email protected]:5772/railway') #development
except (Exception, psycopg2.DatabaseError) as error:
print(error)
print("Connection successful")
return conn
def get_all_images():
query = """SELECT "id", "awsCID" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Camera Traps'"""
conn = connect()
cur = conn.cursor()
try:
# Execute a simple SQL command
cur.execute(query)
# Fetch all the data returned by the database
rows = cur.fetchall()
for row in rows:
image_cnt = generate_id('cnt:image')
# Set multiple field-value pairs using HMSET
fields_values = {
'uuid': row[0],
'awsCID': row[1],
'sensor': 'Rover/RGB',
'label': '❓',
'author': '❓',
'timestamp': '❓'
}
r.hmset(f'image:{image_cnt}', fields_values)
image_url = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{row[1]}'
print(f'predicting {image_url}')
result = client.predict(
image_url,
api_name="/predict"
)
with open(result, 'r') as file:
json_data = json.load(file)
# Extract labels and confidences from JSON data
labels = [data['label'] for data in json_data['confidences']]
confidences = [data['confidence'] for data in json_data['confidences']]
# Store labels and confidences in Redis using HMSET
for label, confidence in zip(labels, confidences):
pred_cnt = generate_id(f'cnt:prediction:{image_cnt}')
fields_values = {
'label': label,
'confidence': confidence,
}
r.hmset(f'prediction:{image_cnt}:{pred_cnt}', fields_values)
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
# Close the cursor and connection
cur.close()
conn.close()
r.close()
def get_all_sounds():
query = """SELECT "id", "awsCID" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Bioacoustics'"""
conn = connect()
cur = conn.cursor()
try:
# Execute a simple SQL command
cur.execute(query)
# Fetch all the data returned by the database
rows = cur.fetchall()
for row in rows:
sound_cnt = generate_id('cnt:sound')
# Set multiple field-value pairs using HMSET
fields_values = {
'uuid': row[0],
'awsCID': row[1],
'sensor': 'Speedybee/Acoustic',
'label': '❓',
'author': '❓',
'label_at': '❓',
'timestamp': '❓'
}
r.hmset(f'sound:{sound_cnt}', fields_values)
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
# Close the cursor and connection
cur.close()
conn.close()
r.close()
# Function to generate auto-incremented IDs
def generate_id(key):
return r.incr(key)
r.flushdb()
get_all_images()
get_all_sounds() |