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Update app.py
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app.py
CHANGED
@@ -15,15 +15,7 @@ if not os.path.exists(UPLOAD_FOLDER):
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from inference_sdk import InferenceHTTPClient
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import base64
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-
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try:
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with open(filepath, "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
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print(f"Encoded image length: {len(encoded_string)}") # Debug: Check length
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return encoded_string
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except Exception as e:
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print(f"Error encoding image: {e}")
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return None
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def predict_pest(filepath):
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CLIENT = InferenceHTTPClient(
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@@ -32,7 +24,7 @@ def predict_pest(filepath):
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)
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try:
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encoded_image = filepath
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result = CLIENT.infer(encoded_image, model_id="pest-detection-ueoco/1")
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return result['predicted_classes'][0]
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except Exception as e:
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@@ -46,7 +38,7 @@ def predict_disease(filepath):
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)
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try:
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encoded_image = filepath
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result = CLIENT.infer(encoded_image, model_id="plant-disease-detection-iefbi/1")
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return result['predicted_classes'][0]
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except Exception as e:
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@@ -60,7 +52,6 @@ client = Client(account_sid, auth_token)
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# WhatsApp number to send messages from (your Twilio number)
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from_whatsapp_number = 'whatsapp:+14155238886'
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# Placeholder functions for image classification
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def classify_pest(image_path):
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# Implement pest classification model here
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return f"Detected Pest: [Pest Name] for image at {image_path}"
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@@ -83,8 +74,9 @@ def whatsapp_webhook():
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if content_type.startswith('image/'):
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r = requests.get(media_url)
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r.raise_for_status()
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# Generate a unique filename
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filename = f"{uuid.uuid4()}.jpg"
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filepath = os.path.join(UPLOAD_FOLDER, filename)
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@@ -101,7 +93,7 @@ def whatsapp_webhook():
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elif 'disease' in incoming_msg:
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response_text = predict_disease(filepath)
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else:
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response_text = "Please specify if you want to detect a pest or a disease."
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else:
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response_text = "The attached file is not an image. Please send an image for classification."
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from inference_sdk import InferenceHTTPClient
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import base64
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+
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def predict_pest(filepath):
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CLIENT = InferenceHTTPClient(
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)
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try:
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encoded_image = encode_image_to_base64(filepath)
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result = CLIENT.infer(encoded_image, model_id="pest-detection-ueoco/1")
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return result['predicted_classes'][0]
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except Exception as e:
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)
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try:
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encoded_image = encode_image_to_base64(filepath)
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result = CLIENT.infer(encoded_image, model_id="plant-disease-detection-iefbi/1")
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return result['predicted_classes'][0]
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except Exception as e:
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# WhatsApp number to send messages from (your Twilio number)
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from_whatsapp_number = 'whatsapp:+14155238886'
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def classify_pest(image_path):
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# Implement pest classification model here
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return f"Detected Pest: [Pest Name] for image at {image_path}"
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if content_type.startswith('image/'):
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r = requests.get(media_url)
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r.raise_for_status()
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response_text=media_url
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'''# Generate a unique filename
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filename = f"{uuid.uuid4()}.jpg"
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filepath = os.path.join(UPLOAD_FOLDER, filename)
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elif 'disease' in incoming_msg:
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response_text = predict_disease(filepath)
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else:
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response_text = "Please specify if you want to detect a pest or a disease."'''
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else:
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response_text = "The attached file is not an image. Please send an image for classification."
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