Update app.py

app.py

@@ -1,17 +1,18 @@
 import gradio as gr
-from transformers import pipeline
+import numpy as np
 
-pipeline = pipeline(task="image-classification", model="Deepak-05-galey/image-data-classifier")
+# 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(7)
+    # 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 = ['Hor', 'Jadrima', 'Kishuthara', 'Marthra', 'Pangtse', 'Serthra', 'Shinglo']
+    result = {classes[i]: confidence_scores[i] for i in range(7)}
+    return result
 
-def predict(input_image):
-  predictions = pipeline(input_image)
-  return input_image, {p["label"]: p["score"] for p in predictions}
-
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select bike, cars, cats, dogs, flowers candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="bike? Or cars? Or cats? Or dogs? Or flowers?",
-)
-
-gradio_app.launch()
+# Creating Gradio interface
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()