Create app.py

app.py

@@ -0,0 +1,82 @@
+import os
+import gradio as gr
+import openai
+import torch
+from torchvision import transforms
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Set your OpenAI API key here or use environment variables
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load a pre-trained model for leaf disease detection
+# For demonstration, we'll use a generic ResNet model fine-tuned for classification
+# Replace the model path with your specific model trained for leaf diseases
+model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
+model.eval()
+
+# Define image transformations
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],  # Standard ImageNet means
+        std=[0.229, 0.224, 0.225]    # Standard ImageNet stds
+    )
+])
+
+# Load class labels (You should replace this with your specific disease classes)
+# For demonstration, we'll use ImageNet labels
+LABELS_URL = "https://s3.amazonaws.com/outcome-blog/imagenet/labels.json"
+response = requests.get(LABELS_URL)
+labels = {int(key): value for key, value in response.json().items()}
+
+def detect_disease(image):
+    # Preprocess the image
+    img = preprocess(image).unsqueeze(0)  # Add batch dimension
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(img)
+        _, predicted = torch.max(outputs, 1)
+        class_id = predicted.item()
+        disease = labels.get(class_id, "Unknown Disease")
+
+    if disease == "Unknown Disease":
+        return disease, "Sorry, the disease could not be identified. Please consult a local agricultural extension office."
+
+    # Generate remedies using OpenAI's ChatGPT
+    prompt = f"The following disease has been detected on a plant leaf: {disease}. Please provide detailed remedies and treatment options for this disease."
+
+    try:
+        completion = openai.ChatCompletion.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "You are a helpful agricultural expert."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.7,
+            max_tokens=500
+        )
+        remedies = completion.choices[0].message.content.strip()
+    except Exception as e:
+        remedies = "An error occurred while fetching remedies. Please try again later."
+
+    return disease, remedies
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=detect_disease,
+    inputs=gr.inputs.Image(type="pil", label="Upload Leaf Image"),
+    outputs=[
+        gr.outputs.Textbox(label="Detected Disease"),
+        gr.outputs.Textbox(label="Remedies")
+    ],
+    title="Leaf Disease Detector",
+    description="Upload an image of a leaf, and the system will detect the disease and provide remedies."
+)
+
+if __name__ == "__main__":
+    iface.launch()