Update app.py

added debug points

app.py

@@ -3,24 +3,32 @@ import gradio as gr
 from datasets import load_dataset
 import torch
 
+
 dataset = load_dataset("cifar100")
 image = dataset["train"]["fine_label"]
+print("load and train dataset \n")
 
 def classify(image):
     feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+    print("feature extractor \n")
     model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+    print("load model \n")
     inputs = feature_extractor(images=image, return_tensors="pt")
+    print("define input \n")
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
     # model predicts one of the 1000 ImageNet classes
+    print("prediction \n")
     predicted_class_idx = logits.argmax(-1).item()
     return model.config.id2label[predicted_class_idx]
 
 def image2speech(image):
+    print("tts \n")
     txt = classify(image)
     return fastspeech(txt), txt
-
+    
+print("load tts interface \n")
 fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")
 
 '''
@@ -35,15 +43,16 @@ app = gr.Interface(fn=image2speech,
 
 app.launch(cache_examples=True)
 '''
-
+print("sets input and outputs \n")
 camera = gr.inputs.Image(label="Image from your camera", source="webcam")
 read = gr.outputs.Textbox(type="auto", label="Text")
 speak = gr.outputs.Audio(type="auto", label="Speech")
 
+print("define interface \n")
 app = gr.Interface(fn=image2speech, 
                    inputs=camera,
                    live=True,
                    description="Takes a snapshot of an object, identifies it, and then tell you what it is. \n Intended use is to help the visually impaired. Models and     dataset used is listed on the linked models and dataset", 
                    outputs=[speak, read])
-
+print("launch interface \n")
 app.launch()