app.py

app.py

@@ -1,31 +1,36 @@
 import torch
+import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Load the model and tokenizer
-model_name = "AI"  # Replace with the name or path of the model you want to use
-tokenizer = AutoTokenizer.from_pretrained("AI")
-model = AutoModelForSequenceClassification.from_pretrained("AI")
+def predict_rooms(new_students, new_temperature):
+    # Load the model and tokenizer
+    model_name = "AI"  # Replace with the name or path of the model you want to use
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# Values for the new scenario
-new_students = int(input("Enter the number of students in the new scenario: "))
-new_temperature = int(input("Enter the temperature in the new scenario: "))
+    # Convert the input to tokens
+    inputs = tokenizer.encode_plus(
+        "Number of students: {}, Temperature: {}".format(new_students, new_temperature),
+        padding="max_length",
+        truncation=True,
+        max_length=64,
+        return_tensors="pt"
+    )
 
-# Convert the input to tokens
-inputs = tokenizer.encode_plus(
-    "Number of students: {}, Temperature: {}".format(new_students, new_temperature),
-    padding="max_length",
-    truncation=True,
-    max_length=64,
-    return_tensors="pt"
-)
+    # Make the prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_rooms = torch.argmax(logits, dim=1).item()
+
+    return predicted_rooms
 
-# Make the prediction
-with torch.no_grad():
-    outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_rooms = torch.argmax(logits, dim=1).item()
+iface = gr.Interface(
+    fn=predict_rooms,
+    inputs=["number", "number"],
+    outputs="number",
+    title="Room Prediction",
+    description="Predict the number of rooms based on the number of students and temperature."
+)
 
-# Print the results
-print("Number of students:", new_students)
-print("Temperature:", new_temperature)
-print("Predicted label for number of rooms:", predicted_rooms)
+iface.launch()