Update app.py

some weird copy paste issues

app.py

@@ -146,155 +146,7 @@ class Me:
         )
         return system_prompt
 
-    # Main chat function
-    from dotenv import load_dotenv
-from openai import OpenAI
-import json
-import os
-from pypdf import PdfReader
-import gradio as gr
-import csv
-
-# Load environment variables
-load_dotenv(override=True)
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv("GOOGLE_API_KEY","AIzaSyDGXQliok_IEo51FGx_7EWiJ_WlCj0e0qc")
-gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-
-# CSV files for logging
-USER_CSV = "user_details.csv"
-UNKNOWN_CSV = "unknown_questions.csv"
-
-# Ensure CSV files exist with headers
-for file, headers in [(USER_CSV, ["email", "name", "notes"]),
-                      (UNKNOWN_CSV, ["question"])]:
-    if not os.path.exists(file):
-        with open(file, "w", newline="", encoding="utf-8") as f:
-            writer = csv.writer(f)
-            writer.writerow(headers)
-
-# Functions to log user details and unknown questions
-def record_user_details(email, name="Name not provided", notes="not provided"):
-    with open(USER_CSV, "a", newline="", encoding="utf-8") as f:
-        writer = csv.writer(f)
-        writer.writerow([email, name, notes])
-    return {"recorded": "ok"}
-
-def record_unknown_question(question):
-    with open(UNKNOWN_CSV, "a", newline="", encoding="utf-8") as f:
-        writer = csv.writer(f)
-        writer.writerow([question])
-    return {"recorded": "ok"}
-
-# JSON definitions for tools
-record_user_details_json = {
-    "name": "record_user_details",
-    "description": "Record user info when they provide email",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "email": {"type": "string", "description": "The user's email"},
-            "name": {"type": "string", "description": "User's name"},
-            "notes": {"type": "string", "description": "Extra info"}
-        },
-        "required": ["email"],
-        "additionalProperties": False
-    }
-}
-
-record_unknown_question_json = {
-    "name": "record_unknown_question",
-    "description": "Record any unanswered question",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "question": {"type": "string", "description": "The question not answered"},
-        },
-        "required": ["question"],
-        "additionalProperties": False
-    }
-}
-
-tools = [
-    {"type": "function", "function": record_user_details_json},
-    {"type": "function", "function": record_unknown_question_json}
-]
-
-class Me:
-    def __init__(self):
-        self.openai = gemini
-        self.name = "SnehaLeela"
-
-        # Load profile JSON
-        with open("profile.json", "r", encoding="utf-8") as f:
-            self.profile = json.load(f)
-
-        # Set attributes for easier access
-        self.personal_info = self.profile.get("personal_info", {})
-        self.expertise = self.profile.get("expertise", [])
-        self.experience = self.profile.get("experience", [])
-        self.education = self.profile.get("education", [])
-        self.friends = self.profile.get("friends", [])
-
-    # Handle tool calls
-    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)
-            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
-
-    # System prompt for LLM
-    def system_prompt(self):
-        # Combine experience into text
-        experience_text = ""
-        for company in self.experience:
-            experience_text += f"{company['company']}"
-            if 'location' in company:
-                experience_text += f" ({company['location']})"
-            for role in company.get('roles', []):
-                experience_text += f"\n- {role['title']} ({role.get('years', '')})"
-                for hl in role.get('highlights', []):
-                    experience_text += f"\n  • {hl}"
-            experience_text += "\n"
-
-        expertise_text = ", ".join(self.expertise)
-
-        education_text = ""
-        if hasattr(self, 'education') and self.education:
-            highest = self.education[0].get("highest_degree", {})
-            education_text = f"{highest.get('degree','')} in {highest.get('field_of_study','')} from {highest.get('university','')} ({highest.get('start_year','')}–{highest.get('end_year','')})"
-        
-        # Optional: prepare friends text for fun
-        friends_text = ""
-        if hasattr(self, 'friends') and self.friends:
-            friends_list = []
-            for f in self.friends:
-                friends_list.append(f"{f.get('Name','')} ({f.get('Company','')}): {f.get('Description','')}")
-            friends_text = "\n".join(friends_list)
-        
-        system_prompt = (
-            f"You are acting as {self.personal_info['name']} (aka {self.personal_info.get('nickname','')}). "
-            f"Answer questions about {self.personal_info['name']}'s career, background, skills, and experience. "
-            f"Represent {self.personal_info['name']} faithfully. "
-            f"If you don't know an answer, use record_unknown_question tool. "
-            f"If the user engages in discussion, try to steer them towards providing their email using record_user_details tool.\n\n"
-            f"## Summary:\n{self.personal_info['summary']}\n\n"
-            f"## Interests:\n{', '.join(self.personal_info.get('personal_interests', []))}\n\n"
-            f"## Travel History:\n{', '.join(self.personal_info.get('travel_history', []))}\n\n"
-            f"## Education:\n{education_text}\n\n"
-            f"## Expertise:\n{expertise_text}\n\n"
-            f"## Experience:\n{experience_text}\n\n"
-            f"## Friends (for fun):\n{friends_text}\n\n"
-            f"## LinkedIn Profile:\nhttps://www.linkedin.com/in/sneha-leela-0a450349/\n\n"
-            f"Chat with the user staying in character as {self.personal_info['name']}."
-        )
-        return system_prompt
-
+   
     # Main chat function
     def chat(self, message, history):
         # ✅ Convert Gradio's history (list of lists) into role/content dicts
@@ -360,38 +212,3 @@ if __name__ == "__main__":
         title="SnehaLeela's Careerbot",
         #css=css_code
     ).launch(share=True)
-
-
-
-# Custom CSS with your local image
-css_code = """
-div { 
-    background-image: url("file/Gemini_Generated.png");  /* Your local image */
-    background-size: cover;
-    background-position: center;
-    background-repeat: no-repeat;
-}
-
-.gradio-container {
-    background-color: rgba(255, 255, 255, 0.6);  /* Optional overlay for readability */
-}
-
-.chat-message.user {
-    background-color: rgba(208, 230, 255, 0.8);
-}
-
-.chat-message.bot {
-    background-color: rgba(224, 255, 224, 0.8);
-}
-"""
-
-# Launch Gradio interface
-if __name__ == "__main__":
-    me = Me()
-    #gr.ChatInterface(me.chat, type="messages",theme="NoCrypt/miku",).launch(share=True)
-    gr.ChatInterface(
-        me.chat,
-        #theme="NoCrypt/miku",
-        title="SnehaLeela's Careerbot",
-        #css=css_code
-    ).launch(share=True)