Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model pipeline
+pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection")
+
+# Define the image classification function
+def image_classifier(image):
+    # Perform image classification
+    outputs = pipe(image)
+    # Get the label of the first result
+    output_text = outputs[0]['label']
+    return output_text
+
+# Define app title and description with HTML formatting
+title = "<h1 style='text-align: center; color: #4CAF50;'>Indian Medicinal Plant Identification</h1>"
+description = "<p style='text-align: center; font-size: 18px;'></p>"
+
+# Define custom CSS styles for the Gradio app
+custom_css = """
+            .gradio-interface {
+                max-width: 600px;
+                margin: auto;
+                border-radius: 10px;
+                box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+            }
+            .title-container {
+                padding: 20px;
+                background-color: #f0f0f0;
+                border-top-left-radius: 10px;
+                border-top-right-radius: 10px;
+            }
+            .description-container {
+                padding: 20px;
+            }
+            """
+
+# Launch the Gradio interface with custom HTML and CSS
+demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="textbox", title=title, description=description, 
+                     theme="gstaff/sketch", css=custom_css,
+                   )
+demo.launch()