app.py

app.py

@@ -1,35 +1,35 @@
-# Create the dataset
-data = {
-    'num_students': [500, 600, 700, 800, 900],
-    'temperature': [20, 21, 22, 23, 24],
-    'num_rooms': [30, 35, 40, 45, 50]
-}
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Split the dataset into features (X) and target (y)
-X = [[1, num_students, temperature] for num_students, temperature in zip(data['num_students'], data['temperature'])]
-y = data['num_rooms']
-
-# Calculate the coefficients using normal equations
-XTX = [[sum(x[i] * x[j] for x in X) for j in range(len(X[0]))] for i in range(len(X[0]))]
-XTy = [sum(X[i][j] * y[i] for i in range(len(X))) for j in range(len(X[0]))]
-
-coefficients = [0] * len(X[0])
-for i in range(len(X[0])):
-    coefficients[i] = sum(XTX[i][j] * XTy[j] for j in range(len(X[0])))
-
-# Print the coefficients
-print("Coefficients:", coefficients)
+# Load the model and tokenizer
+model_name = "your_model_name"  # Replace with the name 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 NEW STUDETNS : "))  # Number of students in the new scenario
-new_temperature =int(input("ENTER NEW TEMPERATURE : "))  # Temperature in the new scenario
+new_students = int(input("Enter new number of students: "))  # Number of students in the new scenario
+new_temperature = int(input("Enter new temperature: "))  # Temperature in the new scenario
 
 # Create the feature array for the new scenario
 new_scenario = [1, new_students, new_temperature]
 
+# Convert the input to tokens
+inputs = tokenizer.encode_plus(
+    "Number of students: {}, Temperature: {}".format(new_students, new_temperature),
+    add_special_tokens=True,
+    padding="max_length",
+    truncation=True,
+    max_length=64,
+    return_tensors="pt"
+)
+
 # Make the prediction
-predicted_rooms = sum(coefficients[i] * new_scenario[i] for i in range(len(new_scenario)))
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_rooms = torch.argmax(logits).item()
 
+# Print the results
 print("Number of students:", new_students)
 print("Temperature:", new_temperature)
 print("Predicted number of rooms:", predicted_rooms)