Update app.py

app.py

@@ -1,17 +1,13 @@
 import pandas as pd
 import gradio as gr
 
-# Load your CSV data into a Pandas DataFrame (replace 'your_data.csv' with the actual file path)
 data = pd.read_csv('plant.csv')
 
-# Get unique values for botanical name and useful part
 botanical_names = list(data['Botanical Name'].unique())
 useful_parts = list(data['Useful Part'].unique())
 
-# Create a dictionary mapping (botanical_name, useful_part) to disease cured
 disease_dict = {(row['Botanical Name'], row['Useful Part']): row['Disease Cures'] for _, row in data.iterrows()}
 
-# Define the Gradio interface
 def predict_disease(botanical_name, useful_part):
     try:
         disease_cured = disease_dict[(botanical_name, useful_part)]
@@ -26,9 +22,8 @@ iface = gr.Interface(
     outputs="text",
     examples=[
         [botanical_names[0], useful_parts[0]],
-        [botanical_names[1], useful_parts[1]],
-        # Add more examples if needed
-    ]
+        ["Bixa Orellana", "Leaf"],
+    ], title = "Botanical Cure Finder"
 )
 
 iface.launch()