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import redis
import psycopg2
from gradio_client import Client
import json
from datetime import datetime
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 predict_images():
query = """SELECT "id", "awsCID", "name" 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:predict:image')
# Set multiple field-value pairs using HMSET
# Convert the date string to a datetime object
# date_obj = datetime.strptime(row[2].split('_')[1], "%Y%m%d%H%M%S")
# # Format the datetime object to the desired format
# formatted_date = date_obj.strftime("%d/%m/%Y %H:%M")
# fields_values = {
# 'uuid': row[0],
# 'awsCID': row[1],
# 'name': row[2],
# 'sensor': 'M300/RGB',
# 'label': '❓',
# 'author': '❓',
# 'timestamp': formatted_date
# }
# 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()
# Function to generate auto-incremented IDs
def generate_id(key):
return r.incr(key)
predict_images()
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