Update app.py

app.py

@@ -1,43 +1,15 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import pipeline
 
-model = AutoModelForCausalLM.from_pretrained(
-    "tiiuae/falcon-7b-instruct",
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    device_map="auto",
-    low_cpu_mem_usage=True,
-)
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]: p["score"] for p in predictions}
 
-def generate_text(input_text):
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    attention_mask = torch.ones(input_ids.shape)
-
-    output = model.generate(
-        input_ids,
-        attention_mask=attention_mask,
-        max_length=200,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-
-    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    print(output_text)
-
-    # Remove Prompt Echo from Generated Text
-    cleaned_output_text = output_text.replace(input_text, "")
-    return cleaned_output_text
-
-text_generation_interface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.inputs.Textbox(label="Input Text"),
-    ],
-    outputs=gr.inputs.Textbox(label="Generated Text"),
-    title="Falcon-7B Instruct",
+gr.Interface(
+    predict,
+    inputs=gr.Image(label="Upload hot dog candidate", type="filepath"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Hot Dog? Or Not?",
 ).launch()