Update app.py

app.py

@@ -17,7 +17,7 @@ def get_session_id():
 
 # 🧠 STaR Algorithm Implementation
 class SelfTaughtReasoner:
-    def __init__(self, model_engine="text-davinci-003"):
+    def __init__(self, model_engine="gpt-3.5-turbo"):
         self.model_engine = model_engine
         self.prompt_examples = []  # Initialize with an empty list
         self.iterations = 0
@@ -35,52 +35,45 @@ class SelfTaughtReasoner:
             'Answer': answer
         })
 
-    def construct_prompt(self, problem: str, include_answer: bool = False, answer: str = "") -> str:
+    def construct_prompt(self, problem: str, include_answer: bool = False, answer: str = "") -> List[dict]:
         """
         📝 Constructs the prompt for the OpenAI API call.
+        Converts examples into the new chat format, where each example is a user message.
         """
-        prompt = ""
+        messages = []
         for example in self.prompt_examples:
-            prompt += f"Problem: {example['Problem']}\n"
-            prompt += f"Rationale: {example['Rationale']}\n"
-            prompt += f"Answer: {example['Answer']}\n\n"
+            messages.append({"role": "system", "content": f"Problem: {example['Problem']}\nRationale: {example['Rationale']}\nAnswer: {example['Answer']}\n"})
 
-        prompt += f"Problem: {problem}\n"
+        messages.append({"role": "user", "content": f"Problem: {problem}\nRationale:"})
+        
         if include_answer:
-            prompt += f"Answer (as hint): {answer}\n"
-        prompt += "Rationale:"
-        return prompt
+            messages.append({"role": "system", "content": f"Answer: {answer}"})
+        
+        return messages
 
     def generate_rationale_and_answer(self, problem: str) -> Tuple[str, str]:
         """
-        🤔 Generates a rationale and answer for a given problem.
+        🤔 Generates a rationale and answer for a given problem using openai.ChatCompletion.create.
         """
-        prompt = self.construct_prompt(problem)
+        messages = self.construct_prompt(problem)
         try:
-            response = openai.Completion.create(
-                engine=self.model_engine,
-                prompt=prompt,
+            response = openai.ChatCompletion.create(
+                model=self.model_engine,
+                messages=messages,
                 max_tokens=150,
-                temperature=0.7,
-                top_p=1,
-                frequency_penalty=0,
-                presence_penalty=0,
-                stop=["\n\n", "Problem:", "Answer:"]
+                temperature=0.7
             )
-            rationale = response.choices[0].text.strip()
-            # 📝 Now generate the answer using the rationale
-            prompt += f" {rationale}\nAnswer:"
-            answer_response = openai.Completion.create(
-                engine=self.model_engine,
-                prompt=prompt,
+            rationale = response.choices[0].message['content'].strip()
+            
+            # Now generate the answer using the rationale
+            messages.append({"role": "system", "content": f"Rationale: {rationale}\nAnswer:"})
+            answer_response = openai.ChatCompletion.create(
+                model=self.model_engine,
+                messages=messages,
                 max_tokens=10,
-                temperature=0,
-                top_p=1,
-                frequency_penalty=0,
-                presence_penalty=0,
-                stop=["\n", "\n\n", "Problem:"]
+                temperature=0
             )
-            answer = answer_response.choices[0].text.strip()
+            answer = answer_response.choices[0].message['content'].strip()
             return rationale, answer
         except Exception as e:
             st.error(f"❌ Error generating rationale and answer: {e}")