Update app.py

app.py

@@ -1,134 +1,74 @@
 from dotenv import load_dotenv
 from openai import OpenAI
-import json
-import os
-import requests
 from pypdf import PdfReader
 import gradio as gr
+import datetime
+from collections import defaultdict
+import os
 
-
+# Load environment variables from .env (includes OPENAI_API_KEY)
 load_dotenv(override=True)
 
-def push(text):
-    requests.post(
-        "https://api.pushover.net/1/messages.json",
-        data={
-            "token": os.getenv("PUSHOVER_TOKEN"),
-            "user": os.getenv("PUSHOVER_USER"),
-            "message": text,
-        }
-    )
-
-
-def record_user_details(email, name="Name not provided", notes="not provided"):
-    push(f"Recording {name} with email {email} and notes {notes}")
-    return {"recorded": "ok"}
-
-def record_unknown_question(question):
-    push(f"Recording {question}")
-    return {"recorded": "ok"}
-
-record_user_details_json = {
-    "name": "record_user_details",
-    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "email": {
-                "type": "string",
-                "description": "The email address of this user"
-            },
-            "name": {
-                "type": "string",
-                "description": "The user's name, if they provided it"
-            }
-            ,
-            "notes": {
-                "type": "string",
-                "description": "Any additional information about the conversation that's worth recording to give context"
-            }
-        },
-        "required": ["email"],
-        "additionalProperties": False
-    }
-}
-
-record_unknown_question_json = {
-    "name": "record_unknown_question",
-    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "question": {
-                "type": "string",
-                "description": "The question that couldn't be answered"
-            },
-        },
-        "required": ["question"],
-        "additionalProperties": False
-    }
-}
-
-tools = [{"type": "function", "function": record_user_details_json},
-        {"type": "function", "function": record_unknown_question_json}]
+# In-memory daily question tracker
+user_question_counter = defaultdict(lambda: {"date": None, "count": 0})
 
 
 class Me:
-
     def __init__(self):
         self.openai = OpenAI()
-        self.name = "Ed Donner"
+        self.name = "Narendra"
+
+        # Load LinkedIn profile text from PDF
         reader = PdfReader("me/linkedin.pdf")
         self.linkedin = ""
         for page in reader.pages:
             text = page.extract_text()
             if text:
                 self.linkedin += text
+
+        # Load summary text
         with open("me/summary.txt", "r", encoding="utf-8") as f:
             self.summary = f.read()
 
-
-    def handle_tool_call(self, tool_calls):
-        results = []
-        for tool_call in tool_calls:
-            tool_name = tool_call.function.name
-            arguments = json.loads(tool_call.function.arguments)
-            print(f"Tool called: {tool_name}", flush=True)
-            tool = globals().get(tool_name)
-            result = tool(**arguments) if tool else {}
-            results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id})
-        return results
-    
     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. "
+        return (
+            f"You are acting as {self.name}, an experienced Python technical interviewer. "
+            f"You are helping users by asking or answering Python-related technical questions. "
+            f"Always stay professional, helpful, and concise. Do not generate responses over 100 tokens. "
+            f"The user can only ask 3 questions per day—enforce this limit politely. "
+            f"\n\n## About {self.name} (your interviewer):\n"
+            f"{self.summary}\n\n"
+            f"## LinkedIn Profile:\n{self.linkedin}\n\n"
+            f"Use this background to answer in character as {self.name}."
+        )
 
-        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 chat(self, message, history):
+        user_id = "user"  # Replace with session-based ID for real tracking
+        today = datetime.date.today()
+        record = user_question_counter[user_id]
+
+        # Reset question count if date changed
+        if record["date"] != today:
+            record["date"] = today
+            record["count"] = 0
+
+        # Check daily question limit
+        if record["count"] >= 3:
+            return "🚫 You've reached your daily limit of 3 questions. Please try again tomorrow."
+
+        # Prepare conversation
         messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
-        done = False
-        while not done:
-            response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
-            if response.choices[0].finish_reason=="tool_calls":
-                message = response.choices[0].message
-                tool_calls = message.tool_calls
-                results = self.handle_tool_call(tool_calls)
-                messages.append(message)
-                messages.extend(results)
-            else:
-                done = True
-        return response.choices[0].message.content
-    
+        
+        response = self.openai.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages,
+            max_tokens=100
+        )
+
+        record["count"] += 1
+        return f"👋 Narendra is your Python interviewer. Let's begin!\n\n{response.choices[0].message.content}"
+
 
 if __name__ == "__main__":
     me = Me()
     gr.ChatInterface(me.chat, type="messages").launch(share=True)
-