Update app.py

app.py

@@ -1,71 +1,98 @@
 import os
-import json
 import gradio as gr
-from transformers import AutoImageProcessor, AutoModelForImageClassification
-from PIL import Image
 import torch
-import numpy as np
+import requests
 import logging
+import numpy as np
+from PIL import Image
+from bs4 import BeautifulSoup
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 
 # Configure Logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
 # Load Model & Processor
-model_name = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
+MODEL_NAME = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
+
 try:
-    processor = AutoImageProcessor.from_pretrained(model_name, use_fast=True)
-    model = AutoModelForImageClassification.from_pretrained(model_name)
+    processor = AutoImageProcessor.from_pretrained(MODEL_NAME, use_fast=True)
+    model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
     logging.info("✅ Model and processor loaded successfully.")
 except Exception as e:
     logging.error(f"❌ Failed to load model: {str(e)}")
     raise RuntimeError("Failed to load the model. Please check the logs for details.")
 
-# Load Treatment Data
-treatment_file = os.path.join(os.getcwd(), "treatments.json")
-if os.path.exists(treatment_file):
+# Function to Fetch Treatment Suggestions from the Internet
+def fetch_treatment_info(disease_name):
     try:
-        with open(treatment_file, "r", encoding="utf-8") as f:
-            treatment_data = json.load(f)
-        logging.info("✅ Treatment data loaded successfully.")
-    except json.JSONDecodeError:
-        logging.error("❌ Invalid JSON format in treatments.json.")
-        treatment_data = {}
-else:
-    logging.warning("⚠️ treatments.json not found. Using empty treatment data.")
-    treatment_data = {}
+        search_url = f"https://www.bing.com/search?q=treatment+for+{disease_name.replace(' ', '+')}+in+plants"
+        headers = {"User-Agent": "Mozilla/5.0"}
+        response = requests.get(search_url, headers=headers)
+
+        if response.status_code == 200:
+            soup = BeautifulSoup(response.text, "html.parser")
+            snippets = soup.find_all("p")  
+            treatments = [s.text for s in snippets][:3]  
 
-# Function to Get Treatment Suggestions
-def get_treatment_suggestions(disease_name):
-    return treatment_data.get(disease_name, "⚠️ No treatment suggestions available for this disease.")
+            return "\n".join(treatments) if treatments else "No treatment suggestions found."
+        else:
+            return "Failed to fetch treatment suggestions."
+    except Exception as e:
+        logging.error(f"Error fetching treatment info: {str(e)}")
+        return "Error retrieving treatment details."
 
 # Define Prediction Function
 def predict(image):
     try:
         image = Image.fromarray(np.uint8(image)).convert("RGB")
         inputs = processor(images=image, return_tensors="pt")
+
         with torch.no_grad():
             outputs = model(**inputs)
         logits = outputs.logits
         predicted_class_idx = logits.argmax(-1).item()
+        confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_idx] * 100
         predicted_label = model.config.id2label[predicted_class_idx]
-        
-        # Get treatment suggestions
-        treatment = get_treatment_suggestions(predicted_label)
-        
-        return f"🌱 **Predicted Disease:** {predicted_label}\n💊 **Treatment:** {treatment}"
-    except Exception as e:
-        logging.error(f"❌ Prediction failed: {str(e)}")
-        return f"❌ **Error:** Unable to process image. Please try again."
 
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload or capture plant image"),
-    outputs=gr.Textbox(label="Result"),
-    title="🌿 AI-Powered Plant Disease Detector",
-    description="📸 Upload a plant leaf image to detect diseases and receive treatment suggestions.",
-    allow_flagging="never",
-)
+        treatment = fetch_treatment_info(predicted_label)
+
+        return predicted_label, confidence.item(), treatment
+    except Exception as e:
+        logging.error(f"Prediction failed: {str(e)}")
+        return "Error", 0, "Error in prediction."
 
-# Launch Gradio App
-iface.launch()
+# Create a Gradio UI with Tabs & Styling
+with gr.Blocks(css="body {background-color: #f7f9fc;}") as app:
+    gr.Markdown(
+        "<h1 style='text-align: center;'>🌿 AI-Powered Plant Disease Detector</h1>",
+        elem_id="title"
+    )
+    
+    with gr.Tabs():
+        with gr.TabItem("📷 Detect Disease"):
+            with gr.Row():
+                image_input = gr.Image(type="numpy", label="📸 Upload a plant image", interactive=True)
+            
+            with gr.Row():
+                btn_predict = gr.Button("🔍 Analyze", variant="primary")
+            
+            with gr.Row():
+                disease_output = gr.Textbox(label="🌱 Disease", interactive=False)
+                confidence_output = gr.Slider(minimum=0, maximum=100, interactive=False, label="🔬 Confidence (%)")
+            
+            with gr.Row():
+                treatment_output = gr.Textbox(label="💊 Suggested Treatment", interactive=False)
+            
+            btn_predict.click(predict, inputs=[image_input], outputs=[disease_output, confidence_output, treatment_output])
+        
+        with gr.TabItem("📖 Prevention Guide"):
+            gr.Markdown("""
+                ## 🌾 How to Keep Your Plants Healthy?
+                - 🏡 Ensure Proper Spacing to Avoid Fungal Growth
+                - 💧 Water in the Morning to Reduce Disease Spread
+                - 🛑 Regularly Inspect & Remove Infected Leaves
+                - ☀️ Allow Good Sunlight & Ventilation for Stronger Plants
+                - 🍀 Use Organic Pesticides & Fungicides When Necessary
+            """)
+    
+app.launch()