Update app.py

app.py

@@ -1,45 +1,14 @@
 import gradio as gr
-import torch
-from transformers import AutoModelForMultipleChoice, AutoTokenizer
 
-# Load the model and tokenizer
-model_id = "roberta-large-mnli"
-model = AutoModelForMultipleChoice.from_pretrained(model_id)
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+def greet(name, is_morning, temperature):
+    salutation = "Good morning" if is_morning else "Good evening"
+    greeting = f"{salutation} {name}. It is {temperature} degrees today"
+    celsius = (temperature - 32) * 5 / 9
+    return greeting, round(celsius, 2)
 
-# Define the preprocessing function
-def preprocess(sample):
-    question = sample["question"]
-    choices = [sample[choice] for choice in "ABCDE"]
-    inputs = [f"{question} {choice}" for choice in choices]
-    tokenized = tokenizer(inputs, truncation=True, padding=True, return_tensors="pt")
-    return {
-        "input_ids": tokenized["input_ids"],
-        "attention_mask": tokenized["attention_mask"]
-    }
-
-# Define the prediction function
-def predict(data):
-    inputs = torch.stack(data["input_ids"])
-    masks = torch.stack(data["attention_mask"])
-    with torch.no_grad():
-        logits = model(inputs, attention_mask=masks).logits
-    predicted_indices = torch.argmax(logits, dim=1)
-    answers = [chr(ord('A') + idx) for idx in predicted_indices]
-    return answers
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Input(type="json"),
-    outputs=gr.outputs.Label(num_top_classes=1, label="Predicted Answer"),
-    live=True,
-    examples=[
-        {"question": "What is the capital of France?", "A": "Paris", "B": "London", "C": "Berlin", "D": "Madrid", "E": "Rome"}
-    ],
-    title="Multiple-Choice Question Answering",
-    description="Enter a question and answer choices (A to E) to get the predicted answer.",
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "checkbox", gr.Slider(0, 100)],
+    outputs=["text", "number"],
 )
-
-# Run the interface
-iface.launch()
+demo.launch(share=True)