Update app.py

app.py

@@ -10,10 +10,11 @@ from groq import Groq
 # Load environment variables
 load_dotenv()
 
-# Set up Streamlit app
-st.set_page_config(page_title="Leaves Disease Detection", layout="wide")
-st.title("🌿 Leaves Disease Detection")
-st.write("Upload an image of a plant leaf to check for diseases and get treatment recommendations.")
+# Set up the Streamlit page
+st.set_page_config(page_title="🌿 Leaf Disease Detector", layout="wide")
+st.markdown("<h1 style='text-align: center;'>🌿 Plant Leaf Disease Detection</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center;'>Upload a leaf image to detect plant diseases and receive treatment advice.</p>", unsafe_allow_html=True)
+st.markdown("---")
 
 # Initialize Groq client
 try:
@@ -23,7 +24,7 @@ except Exception as e:
     st.error(f"Failed to initialize Groq client: {str(e)}")
     client = None
 
-# Dummy CNN model definition
+# Define a simple CNN model (dummy for demonstration)
 class PlantDiseaseModel(nn.Module):
     def __init__(self, num_classes=28):
         super(PlantDiseaseModel, self).__init__()
@@ -43,7 +44,6 @@ class PlantDiseaseModel(nn.Module):
         x = self.classifier(x)
         return x
 
-# Load model
 @st.cache_resource
 def load_model():
     model = PlantDiseaseModel()
@@ -52,7 +52,7 @@ def load_model():
 
 model = load_model()
 
-# Preprocess image
+# Preprocessing image
 def preprocess_image(image):
     transform = transforms.Compose([
         transforms.Resize((256, 256)),
@@ -62,7 +62,7 @@ def preprocess_image(image):
     ])
     return transform(image).unsqueeze(0)
 
-# Disease labels
+# Disease classes
 disease_classes = [
     "Healthy", "Apple Scab", "Apple Black Rot", "Apple Cedar Rust", 
     "Cherry Powdery Mildew", "Corn Gray Leaf Spot", "Corn Common Rust", 
@@ -75,7 +75,7 @@ disease_classes = [
     "Tomato Target Spot", "Tomato Yellow Leaf Curl Virus", "Tomato Mosaic Virus"
 ]
 
-# Classify image
+# Predict disease
 def classify_disease(image):
     try:
         img_tensor = preprocess_image(image)
@@ -88,11 +88,11 @@ def classify_disease(image):
         st.error(f"Error during classification: {str(e)}")
         return "Unknown"
 
-# Get disease info from Groq
+# Get disease info
 def get_disease_info(disease_name):
     if not client:
         return {
-            "description": "API connection not available. Please check your GROQ_API_KEY.",
+            "description": "API not available. Check GROQ_API_KEY.",
         }
 
     try:
@@ -112,39 +112,37 @@ def get_disease_info(disease_name):
         )
         return {"description": response.choices[0].message.content}
     except Exception as e:
-        st.error(f"Error fetching disease information: {str(e)}")
+        st.error(f"Error fetching disease info: {str(e)}")
         return {
             "description": "Unable to fetch disease info. Please try again later.",
         }
 
-# Main UI
+# Main App
 def main():
-    uploaded_file = st.file_uploader("Upload a leaf image", type=["jpg", "jpeg", "png"])
+    uploaded_file = st.file_uploader("📷 Upload a leaf image", type=["jpg", "jpeg", "png"])
 
     if uploaded_file:
         try:
             image = Image.open(uploaded_file).convert("RGB")
-            st.image(image, caption="Uploaded Leaf Image", use_container_width=True)
+            st.image(image, caption="Uploaded Leaf Image", width=400)
 
             if st.button("🔍 Predict Disease"):
-                with st.spinner("Analyzing the leaf..."):
+                with st.spinner("Analyzing..."):
                     disease_name = classify_disease(image)
                     info = get_disease_info(disease_name)
 
+                    st.markdown("---")
                     st.subheader("🔬 Prediction Results")
-                    col1, col2 = st.columns(2)
+
+                    col1, col2 = st.columns([1, 2])
                     with col1:
-                        status = "Healthy" if disease_name.lower() == "healthy" else "Diseased"
+                        status = "Healthy ✅" if disease_name.lower() == "healthy" else "Diseased ⚠️"
                         st.markdown(f"**Status:** {status}")
-                        st.markdown(f"**Detected Disease:** {disease_name}")
-                    with col2:
-                        if disease_name.lower() == "healthy":
-                            st.success("✅ Plant is Healthy")
-                        else:
-                            st.warning("⚠️ Disease Detected")
+                        st.markdown(f"**Detected Disease:** `{disease_name}`")
 
-                    st.subheader("📋 Detailed Information")
-                    st.write(info["description"])
+                    with col2:
+                        st.markdown("**📋 Details:**")
+                        st.markdown(info["description"])
         except Exception as e:
             st.error(f"Error processing image: {str(e)}")