Update app.py

app.py

@@ -10,7 +10,7 @@ from groq import Groq
 # Load environment variables
 load_dotenv()
 
-# Streamlit UI setup
+# 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.")
@@ -23,7 +23,7 @@ except Exception as e:
     st.error(f"Failed to initialize Groq client: {str(e)}")
     client = None
 
-# Simple CNN model (dummy architecture)
+# Dummy CNN model definition
 class PlantDiseaseModel(nn.Module):
     def __init__(self, num_classes=28):
         super(PlantDiseaseModel, self).__init__()
@@ -36,14 +36,14 @@ class PlantDiseaseModel(nn.Module):
             nn.Linear(128 * 32 * 32, 512), nn.ReLU(), nn.Dropout(0.5),
             nn.Linear(512, num_classes)
         )
-    
+
     def forward(self, x):
         x = self.features(x)
         x = x.view(x.size(0), -1)
         x = self.classifier(x)
         return x
 
-# Cache the model
+# Load model
 @st.cache_resource
 def load_model():
     model = PlantDiseaseModel()
@@ -62,7 +62,7 @@ def preprocess_image(image):
     ])
     return transform(image).unsqueeze(0)
 
-# Dummy disease classes
+# Disease labels
 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 the image
+# Classify image
 def classify_disease(image):
     try:
         img_tensor = preprocess_image(image)
@@ -88,16 +88,17 @@ def classify_disease(image):
         st.error(f"Error during classification: {str(e)}")
         return "Unknown"
 
-# Fetch info from Groq API
+# Get disease info from Groq
 def get_disease_info(disease_name):
     if not client:
         return {
             "description": "API connection not available. Please check your GROQ_API_KEY.",
         }
+
     try:
         if disease_name.lower() == "healthy":
             return {
-                "description": "The plant appears to be healthy. No treatment needed.",
+                "description": "The plant appears to be healthy. No treatment is needed.",
             }
 
         response = client.chat.completions.create(
@@ -105,7 +106,7 @@ def get_disease_info(disease_name):
                 {"role": "system", "content": "You are a plant pathologist assistant."},
                 {"role": "user", "content": f"Describe {disease_name} in plants including symptoms, treatment, and prevention."}
             ],
-            model="mixtral-8x7b-32768",
+            model="llama-3.3-70b-versatile",
             temperature=0.3,
             max_tokens=1024
         )
@@ -116,14 +117,14 @@ def get_disease_info(disease_name):
             "description": "Unable to fetch disease info. Please try again later.",
         }
 
-# Main app function
+# Main UI
 def main():
     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_column_width=True)
+            st.image(image, caption="Uploaded Leaf Image", use_container_width=True)
 
             if st.button("🔍 Predict Disease"):
                 with st.spinner("Analyzing the leaf..."):