Update app.py

app.py

@@ -1,21 +1,18 @@
 from transformers import pipeline
 
 classifier = pipeline("image-classification", model="Dalaix703/flowerr-model")
+
 import gradio as gr
 import numpy as np
 
 # Function to classify images into 7 classes
 def image_classifier(inp):
-    # Dummy classification logic
-    # Generating random confidence scores for each class
-    confidence_scores = np.random.rand(5)
-    # Normalizing confidence scores to sum up to 1
-    confidence_scores /= np.sum(confidence_scores)
-    # Creating a dictionary with class labels and corresponding confidence scores
-    classes =   ['crocus', 'daffodil', 'daisy', 'dandelion', 'fritillary']
-    result = {classes[i]: confidence_scores[i] for i in range(5)}
-    return result
+  confidence_scores = np.random.rand(5)
+  confidence_scores /= np.sum(confidence_scores)
+  classes =   ['crocus', 'daffodil', 'daisy', 'dandelion', 'fritillary']
+  result = {classes[i]: confidence_scores[i] for i in range(5)}
+  return result
 
 # Creating Gradio interface
 demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
-demo.launch()
+demo.launch(share=True)