AgriChatbot / app.py
Neurolingua's picture
Update app.py
77e49e3 verified
raw
history blame
4.16 kB
from flask import Flask, request
from twilio.twiml.messaging_response import MessagingResponse
from twilio.rest import Client
import os
import requests
from PIL import Image
import io
import uuid
import shutil
app = Flask(__name__)
UPLOAD_FOLDER = '/code/uploads'
if not os.path.exists(UPLOAD_FOLDER):
os.makedirs(UPLOAD_FOLDER)
from inference_sdk import InferenceHTTPClient
def predict_disease(filepath):
CLIENT = InferenceHTTPClient(
api_url="https://classify.roboflow.com",
api_key="oF1aC4b1FBCDtK8CoKx7"
)
result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1")
return result['predicted_classes'][0]
def predict_pest(filepath):
CLIENT = InferenceHTTPClient(
api_url="https://detect.roboflow.com",
api_key="oF1aC4b1FBCDtK8CoKx7")
result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1")
return result['predicted_classes'][0]
# Initialize the Flask app
account_sid = os.environ.get('TWILIO_ACCOUNT_SID')
auth_token = os.environ.get('TWILIO_AUTH_TOKEN')
client = Client(account_sid, auth_token)
# WhatsApp number to send messages from (your Twilio number)
from_whatsapp_number = 'whatsapp:+14155238886'
# Placeholder functions for image classification
def classify_pest(image_path):
# Implement pest classification model here
return f"Detected Pest: [Pest Name] for image at {image_path}"
def classify_disease(image_path):
# Implement disease classification model here
return f"Detected Disease: [Disease Name] for image at {image_path}"
@app.route('/whatsapp', methods=['POST'])
def whatsapp_webhook():
incoming_msg = request.values.get('Body', '').lower()
sender = request.values.get('From')
# Check if an image is attached
num_media = int(request.values.get('NumMedia', 0))
if num_media > 0:
media_url = request.values.get('MediaUrl0')
content_type = request.values.get('MediaContentType0')
if content_type.startswith('image/'):
r = requests.get(media_url)
# Generate a unique filename
filename = f"{uuid.uuid4()}.jpg"
filepath = os.path.join(UPLOAD_FOLDER, filename)
with open(filepath, 'wb') as out_file:
shutil.copyfileobj(r.raw, out_file)
try:
if ('pest' in incoming_msg) or ('Pest' in incoming_msg):
response_text = predict_pest(filepath)
elif ('disease' in incoming_msg) or ('Disease' in incoming_msg):
response_text = predict_disease(filepath)
else:
response_text = "Please specify if you want to detect a pest or a disease."
except Exception as e:
print(f"Error processing image: {e}")
response_text = 'Invalid image'
else:
response_text = "The attached file is not an image. Please send an image for classification."
elif 'bookkeeping' in incoming_msg:
response_text = "Please provide the details you'd like to record."
else:
response_text = get_agricultural_insights(incoming_msg)
send_message(sender, response_text)
return '', 204 # Return an empty response to Twilio
def get_agricultural_insights(query):
# Implement your agricultural insights logic here
return f"Insights related to: {query}"
def send_message(to, body):
try:
message = client.messages.create(
from_=from_whatsapp_number,
body=body,
to=to
)
print(f"Message sent with SID: {message.sid}")
except Exception as e:
print(f"Error sending message: {e}")
# Function to send an initial message
def send_initial_message(to_number):
send_message(
f'whatsapp:{to_number}',
'Welcome to the Agri AI Chatbot! How can I assist you today? You can send an image with "pest" or "disease" to classify it.'
)
if __name__ == '__main__':
send_initial_message('916382792828')
app.run(host='0.0.0.0', port=7860)