Update app.py

app.py

@@ -1,63 +1,18 @@
 import gradio as gr
-from transformers import BertTokenizer, BertForSequenceClassification
-import torch
+from transformers import pipeline
 
-# Load the tokenizer and model
-tokenizer = BertTokenizer.from_pretrained('RAGFillerModel1')
-model = BertForSequenceClassification.from_pretrained('RAGFillerModel1', num_labels=30)
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-# Define the labels
-labels = [
-    "That's an interesting question... let me see.",
-    "Hmm, I need to consider that for a moment.",
-    "Let me think about how best to address that.",
-    "Well, I think it really depends on a few factors...",
-    "Good thought! I need a moment to process that.",
-    "You know, I've never really thought about it that way before.",
-    "Okay, let me break that down for a second.",
-    "That's a tough one... give me a second to gather my thoughts.",
-    "I want to make sure I give you the right answer, so let me think.",
-    "Let me reflect on that... there are a few angles to consider.",
-    "Alright, if I remember correctly, it goes something like this...",
-    "That's a good point, and I think the answer is...",
-    "Good question! Let me take a moment to unpack that.",
-    "Hmm, there's a lot to consider here. Give me a second.",
-    "Let me think about that... it's not a straightforward answer.",
-    "Interesting... I need to gather my thoughts on this.",
-    "Well, let me consider the various aspects before I answer.",
-    "Alright, let's break this down a bit before I answer.",
-    "Good thought! I want to make sure I address it properly.",
-    "Hmm, let's delve into that a bit more; I'll need a moment.",
-    "Great question! I want to provide a thoughtful response, so let me think.",
-    "That's a fascinating angle... let me think it through.",
-    "I'll need a moment to come up with an answer.",
-    "I'll take a quick moment to weigh my options.",
-    "I appreciate the question; let me think it through.",
-    "Let me take a step back and think that over.",
-    "Let me mull that over for just a moment.",
-    "I want to consider that carefully; let me pause for a second.",
-    "Let's explore that further; I need a moment to think.",
-    "I'd like to think that over before answering."
-]
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-def classify_text(text):
-    # Tokenize the input text
-    inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
-    outputs = model(**inputs)
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
 
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    # Convert predictions to numpy array
-    predictions = predictions.cpu().detach().numpy()
-
-    labeled_predictions = {labels[i]: predictions[0][i] for i in range(len(labels))}
-    max_label = labels[predictions[0].argmax()]
-    max_probability = predictions[0].max()
-
-    result = {max_label: max_probability}
-    return result
-
-# Create a Gradio interface
-iface = gr.Interface(fn=classify_text, inputs="text", outputs="label")
-
-# Launch the interface
-iface.launch()
+if __name__ == "__main__":
+    gradio_app.launch()