Create flower.py

flower.py

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+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
+
+# Creating Gradio interface
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()