Update app.py

app.py

@@ -69,10 +69,16 @@ record_unknown_question_json = {
 tools = [{"type": "function", "function": record_user_details_json},
         {"type": "function", "function": record_unknown_question_json}]
 
-
+# Create a Pydantic model for the Evaluation
+class Evaluation(BaseModel):
+    is_acceptable: bool
+    feedback: str
+    
 class Me:
 
     def __init__(self):
+        # when saving secret in HF space, don't use "" :-)
+        # Initialize Open Router client using OpenAI format
         # open_router_api_key = os.getenv('OPEN_ROUTER_API_KEY')        
         # if open_router_api_key:
         #     print(f"Checking Keys: Open router API Key exists and begins {open_router_api_key[:8]}")
@@ -81,6 +87,11 @@ class Me:
         self.client = OpenAI(
                             base_url="https://openrouter.ai/api/v1",
                             api_key= os.getenv('OPEN_ROUTER_API_KEY') )  # open_router_api_key
+        # Initialize Gemini client using OpenAI format
+        self.gemini = OpenAI(
+            api_key=os.getenv("GOOGLE_API_KEY"), 
+            base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+        )
         self.name = "Chaoran Zhou"
         reader = PdfReader("me/linkedin.pdf")
         self.linkedin = ""
@@ -115,6 +126,73 @@ If the user is engaging in discussion, try to steer them towards getting in touc
         system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
         system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
         return system_prompt
+
+    def system_prompt(self):
+        system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
+particularly questions related to {self.name}'s career, background, skills and experience. \
+Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
+You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \
+Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
+If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
+If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. "
+
+        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
+        system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
+        return system_prompt
+
+    def evaluator_system_prompt(self):
+        evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \
+You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \
+The Agent is playing the role of {self.name} and is representing {self.name} on their website. \
+The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \
+The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:"
+
+        evaluator_system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
+        evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback."
+        return evaluator_system_prompt
+
+    def evaluator_user_prompt(self, reply, message, history):
+        user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n"
+        user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n"
+        user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n"
+        user_prompt += f"Please evaluate the response, replying with whether it is acceptable and your feedback."
+        return user_prompt
+
+    def evaluate(self, reply, message, history) -> Evaluation:
+        messages = [
+            {"role": "system", "content": self.evaluator_system_prompt()},
+            {"role": "user", "content": self.evaluator_user_prompt(reply, message, history)}
+        ]
+        response = self.gemini.beta.chat.completions.parse(
+            model="gemini-2.5-flash-preview-05-20", 
+            messages=messages, 
+            response_format=Evaluation
+        )
+        return response.choices[0].message.parsed
+
+    def rerun(self, reply, message, history, feedback):
+        updated_system_prompt = self.system_prompt() + f"\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n"
+        updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n"
+        updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n"
+        messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}]
+        
+        done = False
+        while not done:
+            response = self.gemini.chat.completions.create(
+                model="gemini-2.5-flash-preview-05-20",
+                messages=messages, 
+                tools=tools
+            )
+            
+            if response.choices[0].finish_reason == "tool_calls":
+                message_obj = response.choices[0].message
+                tool_calls = message_obj.tool_calls
+                results = self.handle_tool_call(tool_calls)
+                messages.append(message_obj)
+                messages.extend(results)
+            else:
+                done = True
+        return response.choices[0].message.content
     
     def chat(self, message, history):
         messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
@@ -148,7 +226,24 @@ If the user is engaging in discussion, try to steer them towards getting in touc
             #     print(f"Error during OpenAI API call: {e}")
             #     return "Sorry, there was an error processing your request. Please check your API key and try again."
            
-        return response.choices[0].message.content
+        reply = response.choices[0].message.content
+
+        # Evaluate the response
+        try:
+            evaluation = self.evaluate(reply, message, history)
+            
+            if evaluation.is_acceptable:
+                print("Passed evaluation - returning reply")
+            else:
+                print("Failed evaluation - retrying")
+                print(f"Feedback: {evaluation.feedback}")
+                reply = self.rerun(reply, message, history, evaluation.feedback)
+        except Exception as e:
+            print(f"Evaluation failed with error: {e}")
+            print("Proceeding with original reply")
+            
+        return reply
+        
     
 
 if __name__ == "__main__":