app.py

import gradio as gr
from together import Together
import os


client = Together(api_key=os.environ.get('TOGETHER_API_KEY'))


# Function to create the system prompt based on the selected detector
def create_system_prompt():
    system_prompt = """You are a expert GMAT profiler who specializes in understanding student profile. You use successful candidate profile as a baseline and compare it with current profile and provide recomendations on the good profile.
          Analyze the users input and provide guidance on what candidate can do to achieve a target college admission. 
          Provide a detailed response in 500 words. Keep it bulleted. Only provide recommendations if user's profile is not good enough.
          Always Provide the Response in following format only
          Format -
          Analysis : 4-5 lines
          Recommendation : 4-5 points
          Possible colleges with current profile:
          Target college Review and Recommendation:
          """
   
    return system_prompt

# Function to get response from OpenAI API
def analyze_chat( gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement):
    system_prompt = create_system_prompt()
    chat_input = """
    User's Profile Data. Assess the following aspects:
    - GMAT Score: {}
    - Undergraduate GPA: {}
    - Target College: {}
    - Work Experience: {} years
    - Leadership Roles: {}
    - Extracurricular Activities: {}
    - Personal Statement: {}
    Provide a comprehensive analysis and recommendations.
    """.format(gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement)
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.2-3B-Instruct-Turbo",  # Change to the OpenAI model you prefer
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": chat_input}
        ],)
    return response.choices[0].message.content.strip()

# Gradio interface
def gradio_interface(gmat_score, gpa, target_college,  work_experience, leadership_roles, extracurriculars, personal_statement):
    return analyze_chat( gmat_score, gpa, target_college,  work_experience, leadership_roles, extracurriculars, personal_statement)

# Custom CSS for input restriction
custom_css = """
#input-textbox textarea {
    maxlength: 210;
    overflow: hidden;
    resize: none;
}
"""
# Creating the Gradio UI
with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.sky,font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo:
    with gr.Row():
        gr.Markdown("## AI GMAT Profiler - Gozo Sensei")
    with gr.Row():
        with gr.Column(scale=2, min_width=300):
            gmat_score = gr.Number(label="GMAT Score")
            personal_statement = gr.TextArea(label="Enter your profile details", lines=4,elem_id="input-textbox",
            info="Please ensure that any Personal Identifiable Information (PII) is removed before submitting the chat.")
           
        with gr.Column(scale=2, min_width=300):
            gpa = gr.Number(label="Undergraduate GPA")
            work_experience = gr.Number(label="Work Experience (years)")
            target_college =  gr.Textbox(label="Target College")
        with gr.Column(scale=2, min_width=300):
            extracurriculars = gr.TextArea(label="Extracurricular Activities",lines=3)
            leadership_roles = gr.TextArea(label="Leadership Roles",lines=3)
    with gr.Row():
        gr.Markdown("## Response")
    with gr.Row():
        output = gr.Markdown(label="Analysis")
        #output = gr.TextArea(label="Analysis",info="Disclaimer: The information provided below is generated by AI based on text analytics with limited context. It must not be considered as absolute truth or final judgment.", interactive = False, max_lines=20)
    with gr.Row():
        btn = gr.Button("Analyze")
        btn.click(fn=gradio_interface, inputs=[gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement], outputs=output)
    
# Launch the app
demo.launch()