Sure, here is a basic example of predicting sales using Theano:

```
import theano
import numpy as np

# Define input variables
inputs = np.array([3, 5, 7]).astype('float32')
weights = np.array([0.4, 0.5, 0.6]).astype('float32')
bias = np.array([0.1]).astype('float32')

# Define the model
X = theano.tensor.fvector('X')
W = theano.shared(weights, name='W')
b = theano.shared(bias, name='b')
prediction = theano.tensor.dot(X, W) + b

# Compile the prediction function
predict_fn = theano.function([X], prediction)

# Predict sales for input values
sales = predict_fn(inputs)

print('Predicted sales:', sales)
```

Here, we define an input vector `inputs` and weights `weights` and bias `bias` for our model. Then, we define the Theano model using these inputs and specify the output as a dot product of the inputs and the weights plus the bias term. Finally, we compile the prediction function and use it to make a prediction for the input values, and print the result.
target_variable = 'vendite'
Crea un codice theano per fare una previsione.