Update app.py

app.py

@@ -5,40 +5,33 @@ import os
 
 tg.set_backward_engine("gpt-4o", override=True)
 
-# Step 1: Get an initial response from an LLM.
-engine = tg.get_engine(engine_name='gpt-4o')
-model = tg.BlackboxLLM(engine)
-
-question_string = ("If it takes 1 hour to dry 25 shirts under the sun, "
-                   "how long will it take to dry 30 shirts under the sun? "
-                   "Reason step by step")
-
-question = tg.Variable(question_string, 
-                       role_description="question to the LLM", 
-                       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(eval_system_prompt = 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)
+import textgrad as tg
+tg.set_backward_engine(tg.get_engine("gpt-4o"))
+
+initial_solution = """To solve the equation 3x^2 - 7x + 2 = 0, we use the quadratic formula:
+x = (-b ± √(b^2 - 4ac)) / 2a
+a = 3, b = -7, c = 2
+x = (7 ± √((-7)^2 + 4(3)(2))) / 6
+x = (7 ± √73) / 6
+The solutions are:
+x1 = (7 + √73)
+x2 = (7 - √73)"""
+
+solution = tg.Variable(initial_solution,
+                       requires_grad=True,
+                       role_description="solution to the math question")
+
+loss_system_prompt = tg.Variable("""You will evaluate a solution to a math question. 
+Do not attempt to solve it yourself, do not give a solution, only identify errors. Be super concise.""",
+                                 requires_grad=False,
+                                 role_description="system prompt")
+                              
+loss_fn = tg.TextLoss(loss_system_prompt)
+optimizer = tg.TGD([solution])
+
+loss = loss_fn(solution)
+st.write(loss.value)
+
 loss.backward()
 optimizer.step()
-st.write(answer)
+st.write(solution.value)