Update app.py

app.py

@@ -18,4 +18,28 @@ question = tg.Variable(question_string,
                        requires_grad=False)
 
 answer = model(question)
+st.write(answer)
+
+answer.set_role_description("concise and accurate answer to the question")
+
+# Step 2: Define the loss function and the optimizer, just like in PyTorch! 
+# Here, we don't have SGD, but we have TGD (Textual Gradient Descent) 
+# that works with "textual gradients". 
+optimizer = tg.TGD(parameters=[answer])
+evaluation_instruction = (f"Here's a question: {question_string}. " 
+                           "Evaluate any given answer to this question, "
+                           "be smart, logical, and very critical. "
+                           "Just provide concise feedback.")
+                            
+
+# TextLoss is a natural-language specified loss function that describes 
+# how we want to evaluate the reasoning.
+loss_fn = tg.TextLoss(evaluation_instruction)
+
+# Step 3: Do the loss computation, backward pass, and update the punchline. 
+# Exact same syntax as PyTorch!
+loss = loss_fn(answer)
+st.write(loss)
+loss.backward()
+optimizer.step()
 st.write(answer)