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SarowarSaurav
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Update app.py
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app.py
CHANGED
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import os
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import gradio as gr
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import openai
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import
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from torchvision import transforms
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from PIL import Image
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import
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# Set your OpenAI API key here or use environment variables
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openai.api_key = "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"
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# Define image transformations
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preprocess = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=[0.485, 0.456, 0.406], # Standard ImageNet means
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std=[0.229, 0.224, 0.225] # Standard ImageNet stds
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)
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])
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# Load class labels (You should replace this with your specific disease classes)
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# For demonstration, we'll use ImageNet labels
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LABELS_URL = "https://s3.amazonaws.com/outcome-blog/imagenet/labels.json"
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response = requests.get(LABELS_URL)
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labels = {int(key): value for key, value in response.json().items()}
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def detect_disease(image):
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# Preprocess the image
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img = preprocess(image).unsqueeze(0) # Add batch dimension
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# Perform inference
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with torch.no_grad():
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outputs = model(img)
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_, predicted = torch.max(outputs, 1)
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class_id = predicted.item()
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disease = labels.get(class_id, "Unknown Disease")
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if disease == "Unknown Disease":
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return disease, "Sorry, the disease could not be identified. Please consult a local agricultural extension office."
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# Generate remedies using OpenAI's ChatGPT
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prompt = f"The following disease has been detected on a plant leaf: {disease}. Please provide detailed remedies and treatment options for this disease."
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try:
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are a
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{"role": "user", "content":
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],
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temperature=0.7,
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max_tokens=500
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)
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remedies = completion.choices[0].message.content.strip()
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except Exception as e:
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remedies = "An error occurred while fetching remedies. Please try again later."
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# Define the Gradio interface
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iface = gr.Interface(
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fn=
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inputs=gr.
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outputs=[
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gr.
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gr.
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],
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title="Leaf Disease Detector",
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description="Upload an image of a leaf, and the system will detect
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)
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if __name__ == "__main__":
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import os
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import openai
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import gradio as gr
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from PIL import Image
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import io
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import base64
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# Set your OpenAI API key here or use environment variables
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openai.api_key = "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"
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def detect_disease_and_get_remedies(image):
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"""
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Detects leaf disease from the uploaded image and provides remedies.
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Args:
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image (PIL.Image): Uploaded leaf image.
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Returns:
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Tuple[str, str]: Detected disease and corresponding remedies.
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"""
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try:
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# Convert PIL Image to bytes
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buffered = io.BytesIO()
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image.save(buffered, format="JPEG")
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img_bytes = buffered.getvalue()
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# Encode image to base64
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img_base64 = base64.b64encode(img_bytes).decode('utf-8')
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# Create a system prompt to guide the model
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system_prompt = "You are an agricultural expert specializing in plant diseases."
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# Step 1: Detect Disease
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disease_prompt = (
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"Analyze the following image of a plant leaf and identify any diseases present. "
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"Provide only the name of the disease without additional explanation.\n\n"
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"Image (base64 encoded): {}\n\nDisease:".format(img_base64)
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)
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# Send the image to OpenAI's ChatCompletion API for disease detection
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response = openai.ChatCompletion.create(
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model="gpt-4-vision", # Assuming GPT-4 with vision capabilities
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": disease_prompt}
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],
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temperature=0
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)
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disease = response.choices[0].message['content'].strip()
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if not disease or disease.lower() in ["no disease detected", "healthy"]:
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return "No Disease Detected", "The leaf appears to be healthy. Maintain regular plant care to prevent future issues."
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# Step 2: Get Remedies for the Detected Disease
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remedy_prompt = (
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"Provide detailed remedies and treatment options for the following plant leaf disease: {}. "
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"Include both chemical and organic treatment methods if applicable.".format(disease)
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)
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remedy_response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are a knowledgeable agricultural advisor."},
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{"role": "user", "content": remedy_prompt}
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],
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temperature=0.7,
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max_tokens=500
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)
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remedies = remedy_response.choices[0].message['content'].strip()
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return disease, remedies
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except Exception as e:
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return "Error", f"An error occurred: {str(e)}"
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# Define the Gradio interface
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iface = gr.Interface(
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fn=detect_disease_and_get_remedies,
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inputs=gr.Image(type="pil", label="Upload Leaf Image"),
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outputs=[
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gr.Textbox(label="Detected Disease"),
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gr.Textbox(label="Remedies")
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],
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title="Leaf Disease Detector",
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description="Upload an image of a leaf, and the system will detect any diseases present and provide remedies."
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)
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if __name__ == "__main__":
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