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app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, SiglipForImageClassification, pipeline
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+import os
+
+# -------------------------------
+# Model paths (local folders)
+# -------------------------------
+hf_model_names = {
+    "Rice": "models/Rice-Leaf-Disease",
+    "Sugarcane": "models/sugarcane-plant-diseases-classification",
+    "Tomato": "models/tomato-leaf-disease-classification-resnet50",
+    "Corn/Wheat": "models/crop_leaf_diseases_vit"
+}
+
+# -------------------------------
+# Utility: Load model offline or online
+# -------------------------------
+def load_model_or_fallback(model_name, model_path, use_pipeline=False, skip_processor=False):
+    if os.path.exists(model_path):
+        print(f"✅ Loading local model: {model_path}")
+        if use_pipeline:
+            return pipeline("image-classification", model=model_path)
+        elif skip_processor:
+            model = SiglipForImageClassification.from_pretrained(model_path)
+            return None, model
+        else:
+            processor = AutoImageProcessor.from_pretrained(model_path)
+            model = SiglipForImageClassification.from_pretrained(model_path)
+            return processor, model
+    else:
+        print(f"🌐 Model not found locally. Fetching from Hugging Face Hub: {model_name}")
+        if use_pipeline:
+            return pipeline("image-classification", model=model_name)
+        elif skip_processor:
+            model = SiglipForImageClassification.from_pretrained(model_name)
+            return None, model
+        else:
+            processor = AutoImageProcessor.from_pretrained(model_name)
+            model = SiglipForImageClassification.from_pretrained(model_name)
+            return processor, model
+
+# -------------------------------
+# Load models
+# -------------------------------
+hf_processors = {}
+hf_models = {}
+
+# Rice
+hf_processors['Rice'], hf_models['Rice'] = load_model_or_fallback(
+    "prithivMLmods/Rice-Leaf-Disease", hf_model_names["Rice"]
+)
+# Sugarcane (skip processor)
+_, hf_models['Sugarcane'] = load_model_or_fallback(
+    "dwililiya/sugarcane-plant-diseases-classification",
+    hf_model_names["Sugarcane"],
+    skip_processor=True
+)
+# Tomato (pipeline)
+hf_models['Tomato'] = load_model_or_fallback(
+    "wellCh4n/tomato-leaf-disease-classification-resnet50", 
+    hf_model_names["Tomato"], use_pipeline=True
+)
+# Corn/Wheat (pipeline)
+hf_models['Corn/Wheat'] = load_model_or_fallback(
+    "wambugu71/crop_leaf_diseases_vit", 
+    hf_model_names["Corn/Wheat"], use_pipeline=True
+)
+
+# -------------------------------
+# Sugarcane manual preprocessing
+# -------------------------------
+sugarcane_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+# -------------------------------
+# Disease mapping
+# -------------------------------
+disease_dict = {
+    "Rice": ["Bacterial Blight", "Blast", "Brown Spot", "Healthy", "Tungro"],
+    "Sugarcane": ["Bacterial Blight", "Healthy", "Mosaic", "Red Rot", "Rust", "Yellow"],
+    "Tomato": ["Early Blight", "Late Blight", "Healthy"],
+    "Corn/Wheat": ["Healthy", "Rust", "Blight", "Leaf Spot"]  # Adjust based on your model labels
+}
+
+# Remedies mapping
+remedies = {
+    "Early Blight": "Remove infected leaves, apply fungicide.",
+    "Late Blight": "Use fungicides and remove infected plants.",
+    "Bacterial Blight": "Use resistant varieties and avoid overhead watering.",
+    "Blast": "Use balanced fertilizer, apply fungicide.",
+    "Brown Spot": "Ensure proper field drainage and avoid overcrowding.",
+    "Tungro": "Control green leafhoppers and remove infected plants.",
+    "Mosaic": "Remove infected plants, avoid spread.",
+    "Red Rot": "Remove infected plants, apply fungicide.",
+    "Rust": "Use fungicide and resistant varieties.",
+    "Yellow": "Monitor plant, apply preventive measures.",
+    "Leaf Spot": "Remove affected leaves and apply fungicide.",
+    "Blight": "Use disease-free seeds and apply fungicides.",
+    "Healthy": "No action needed."
+}
+
+# -------------------------------
+# Prediction function
+# -------------------------------
+def predict_disease(crop, img):
+    if img is None:
+        return "No image uploaded", "Please upload a leaf image."
+
+    img_pil = Image.fromarray(img).convert("RGB")
+
+    if crop == "Rice":
+        inputs = hf_processors[crop](images=img_pil, return_tensors="pt")
+        with torch.no_grad():
+            outputs = hf_models[crop](**inputs)
+            logits = outputs.logits
+            probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+            predicted_idx = int(np.argmax(probs))
+        disease = disease_dict[crop][predicted_idx]
+        advice = remedies.get(disease, "No advice available.")
+        return disease, advice
+
+    elif crop == "Sugarcane":
+        img_tensor = sugarcane_transform(img_pil).unsqueeze(0)
+        with torch.no_grad():
+            outputs = hf_models[crop](img_tensor)
+            logits = outputs.logits
+            probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+            predicted_idx = int(np.argmax(probs))
+        disease = disease_dict[crop][predicted_idx]
+        advice = remedies.get(disease, "No advice available.")
+        return disease, advice
+
+    elif crop in ["Tomato", "Corn/Wheat"]:
+        result = hf_models[crop](img_pil)[0]
+        disease = result['label']
+        advice = remedies.get(disease, "No advice available.")
+        return disease, advice
+
+    else:
+        return "Error", f"Model for {crop} is not available."
+
+# -------------------------------
+# Gradio Interface
+# -------------------------------
+custom_css = """
+body, .gradio-container {
+    background-image: url('https://media.istockphoto.com/id/1328004520/photo/healthy-young-soybean-crop-in-field-at-dawn.jpg?s=612x612&w=0&k=20&c=XRw20PArfhkh6LLgFrgvycPLm0Uy9y7lu9U7fLqabVY=');
+    background-size: cover;
+    background-repeat: no-repeat;
+    background-attachment: fixed;
+    min-height: 100vh !important;
+}
+.gradio-container > * {
+    background-color: rgba(255, 255, 255, 0.88) !important;
+    border-radius: 15px;
+    padding: 20px;
+}
+"""
+
+with gr.Blocks(css=custom_css) as app:
+    gr.Markdown("## 🌿 Crop Disease Detector")
+    gr.Markdown("Upload a leaf image of your crop and get AI-based disease prediction with remedies.")
+
+    with gr.Row():
+        with gr.Column():
+            crop_input = gr.Dropdown(list(hf_model_names.keys()), label="Select Crop")
+            img_input = gr.Image(type="numpy", label="Upload Leaf Image")
+            predict_btn = gr.Button("🔍 Predict Disease")
+
+        with gr.Column():
+            disease_output = gr.Textbox(label="Predicted Disease")
+            advice_output = gr.Textbox(label="Recommended Action")
+
+    predict_btn.click(predict_disease, inputs=[crop_input, img_input], outputs=[disease_output, advice_output])
+
+# Launch
+app.launch(server_name="127.0.0.1", server_port=7860, share=True)