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import os
import google.generativeai as genai
from google.generativeai import GenerativeModel
import gradio as gr
from dotenv import load_dotenv
from PyPDF2 import PdfReader
# Load environment variables
load_dotenv()
api_key = os.environ.get('GOOGLE_API_KEY')
# Configure Gemini
genai.configure(api_key=api_key)
model = GenerativeModel("gemini-1.5-flash")
# Load profile data
with open("summary.txt", "r", encoding="utf-8") as f:
summary = f.read()
reader = PdfReader("Profile.pdf")
linkedin = ""
for page in reader.pages:
text = page.extract_text()
if text:
linkedin += text
# System prompt
name = "Rishabh Dubey"
system_prompt = f"""
You are acting as {name}. You are answering questions on {name}'s website,
particularly questions related to {name}'s career, background, skills and experience.
Your responsibility is to represent {name} for interactions on the website as faithfully as possible.
You are given a summary of {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, say so.
## Summary:
{summary}
## LinkedIn Profile:
{linkedin}
With this context, please chat with the user, always staying in character as {name}.
"""
def chat(message, history):
conversation = f"System: {system_prompt}\n"
for user_msg, bot_msg in history:
conversation += f"User: {user_msg}\nAssistant: {bot_msg}\n"
conversation += f"User: {message}\nAssistant:"
response = model.generate_content([conversation])
return response.text
if __name__ == "__main__":
# Make sure to bind to the port Render sets (default: 10000) for Render deployment
port = int(os.environ.get("PORT", 10000))
gr.ChatInterface(chat, chatbot=gr.Chatbot()).launch(server_name="0.0.0.0", server_port=port)
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