| import os | |
| import gradio as gr | |
| from google import genai | |
| from google.genai import types | |
| client = genai.Client(api_key=os.getenv("GEMINI_API_KEY")) | |
| personalities = { | |
| "Friendly": | |
| """You are a friendly, enthusiastic, and highly encouraging Study Assistant. | |
| Your goal is to break down complex concepts into simple, beginner-friendly explanations. | |
| Use analogies and real-world examples that beginners can relate to. | |
| Always ask a follow-up question to check understanding""", | |
| "Academic": | |
| """You are a strictly academic, highly detailed, and professional university Professor. | |
| Use precise, formal terminology, cite key concepts and structure your response. | |
| Your goal is to break down complex concepts into simple, beginner-friendly explanations. | |
| Use analogies and real-world examples that beginners can relate to. | |
| Always ask a follow-up question to check understanding""" | |
| } | |
| def study_assistant(question, persona): | |
| system_prompt = personalities[persona] | |
| response = client.models.generate_content( | |
| model="gemini-2.5-flash", | |
| config=types.GenerateContentConfig( | |
| system_instruction=system_prompt, | |
| temperature=0.4, | |
| max_output_tokens=2000 | |
| ), | |
| contents=question | |
| ) | |
| return response.text | |
| demo = gr.Interface( | |
| fn=study_assistant, | |
| inputs=[ | |
| gr.Textbox(lines=4, placeholder="Ask a question...", label="Question"), | |
| gr.Radio(choices=list(personalities.keys()), value="Friendly", label="Personality") | |
| ], | |
| outputs=gr.Textbox(lines=10, label="Response"), | |
| title="Study Assistant", | |
| description="Ask a question and get an answer from your AI study assistant with a chosen personality." | |
| ) | |
| demo.launch(debug=True) | |