Update app.py

app.py

@@ -1,46 +1,15 @@
-import os
-import openai
 import gradio as gr
+from transformers import pipeline
 
-# Set the OpenAI API key as an environment variable
-openai.api_key = "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"
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-start_sequence = "\nAI:"
-restart_sequence = "\nHuman: "
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]: p["score"] for p in predictions}
 
-# Define the conversation prompt
-conversation_prompt = "Welcome to ChatRobo, kindly type in your enquiries: "
-
-# Define the function to generate a response from the GPT model
-def openai_chat_history(input, history):
-    history = history or []
-    if input.strip() != "":
-        s = list(sum(history, ()))
-        s.append(input)
-        inp = ' '.join(s)
-        output = openai_create(inp)
-        history.append((input, output))
-        return history[-1][1]
-    else:
-        return ""
-
-def openai_create(prompt):
-    response = openai.Completion.create(
-        model="text-davinci-003",
-        prompt=prompt,
-        temperature=0.9,
-        max_tokens=150,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0.6,
-        stop=["Human:", "AI:"]
-    )
-    
-    return response.choices[0].text
-
-block = gr.Interface(
-    fn=openai_chat_history, 
-    inputs=[gr.inputs.Textbox(placeholder=conversation_prompt)], 
-    outputs=[gr.outputs.Textbox(label="ChatRobo Output")]
-)
-block.launch()
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Hot Dog? Or Not?",
+).launch()