Update app.py

app.py

@@ -1,13 +1,14 @@
-from fastai.vision.all import *
-import gradio as gr
 import fal_client
-from PIL import Image
+import gradio as gr
 import io
+import openai
+import os
 import random
 import requests
+from PIL import Image
+from dotenv import load_dotenv
+from fastai.vision.all import *
 from pathlib import Path
-import openai
-import os
 
 # Dictionary of plant names and their Wikipedia links
 search_terms_wikipedia = {
@@ -68,6 +69,12 @@ flowers_endangerment = {
     "goldfields coreopsis": "Varies by species; many not endangered."
 }
 
+
+def get_status(flower_name):
+    """Return the endangerment status of a given flower name."""
+    return flowers_endangerment.get(flower_name, "Flower not found in database.")
+
+
 # Templates for AI image generation
 prompt_templates = [
     "A dreamy watercolor scene of a {flower} on a misty morning trail, with golden sunbeams filtering through towering redwoods, and a curious hummingbird hovering nearby.",
@@ -84,42 +91,31 @@ example_images = [
     str(Path('example_images/example_3.jpg')),
     str(Path('example_images/example_4.jpg')),
     str(Path('example_images/example_5.jpg'))
-    
 ]
 
+
 # Function to handle AI generation progress updates
 def on_queue_update(update):
     if isinstance(update, fal_client.InProgress):
         for log in update.logs:
-           print(log["message"])
-
+            print(log["message"])
 
 
-def get_status(flower_name):
-    """Return the endangerment status of a given flower name."""
-    # Normalize input for dictionary lookup
-    normalized_name = flower_name.title()
-    return flowers_endangerment.get(normalized_name, "Flower not found in database.")
-
-
-
-
-# Main function to process the uploaded image
 # Main function to process the uploaded image
 def process_image(img):
     print("Starting prediction...")
     predicted_class, _, probs = learn.predict(img)
     print(f"Prediction complete: {predicted_class}")
-    
+
     classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
-    
+
     # Get Wikipedia link
     wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
-    
+
     # Get endangerment status
     endangerment_status = get_status(predicted_class)
     print(f"Status found: {endangerment_status}")
-    
+
     # Generate artistic interpretation using DALL-E
     print("Sending request to DALL-E...")
     try:
@@ -130,19 +126,19 @@ def process_image(img):
             quality="standard",
             n=1,
         )
-        
+
         # Get the image URL
         image_url = response.data[0].url
         print(f"Image URL: {image_url}")
-        
+
         # Download the generated image
         response = requests.get(image_url)
         generated_image = Image.open(io.BytesIO(response.content))
-        
+
     except Exception as e:
         print(f"Error generating image: {e}")
         generated_image = None
-    
+
     print("Image retrieved and ready to return")
     return classification_results, generated_image, wiki_url, endangerment_status
 
@@ -165,9 +161,8 @@ with gr.Blocks() as demo:
     # Input section
     with gr.Row():
         input_image = gr.Image(height=230, width=230, label="Upload Image for Classification", type="pil")
-    
+
     # Output section
-   # Output section
     with gr.Row():
         with gr.Column():
             label_output = gr.Label(label="Classification Results")
@@ -177,25 +172,24 @@ with gr.Blocks() as demo:
 
     # Add example images using local paths
     gr.Examples(
-    examples=example_images,
-    inputs=input_image,
-    examples_per_page=6,
-    fn=process_image,
-    outputs=[label_output, generated_image, wiki_output, status_output]  # ← UPDATED
-)
+        examples=example_images,
+        inputs=input_image,
+        examples_per_page=6,
+        fn=process_image,
+        outputs=[label_output, generated_image, wiki_output, status_output]  # ← UPDATED
+    )
 
     input_image.change(
         fn=process_image,
         inputs=input_image,
         outputs=[label_output, generated_image, wiki_output, status_output]  # ← UPDATED
-)
+    )
 
     input_image.clear(
         fn=clear_outputs,
         inputs=[],
         outputs=[label_output, generated_image, wiki_output, status_output]  # ← UPDATED
-)
-
+    )
 
 # Start the application
-demo.launch(inline=False)
+demo.launch(inline=False)