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import gradio as gr
import openai
import os
# Set OpenAI API Key
openai.api_key = os.getenv("TRY_NEW_THINGS")
openai.api_base = "https://api.groq.com/openai/v1"
# Function to get response from GROQ API
def get_groq_response(message, category):
system_message = ""
if category == "Study Tips":
system_message = "Provide effective study strategies, time management advice, and tips for staying organized and focused."
elif category == "Exam Preparation":
system_message = "Offer tips for preparing for exams, managing anxiety, and optimizing performance on test day."
elif category == "Project Guidance":
system_message = "Provide advice on how to tackle academic projects, group assignments, and presentations effectively."
elif category == "Time Management":
system_message = "Help the user manage their time effectively with practical scheduling and prioritization tips."
elif category == "Motivation & Focus":
system_message = "Provide encouraging advice to stay motivated and maintain focus on long-term goals."
elif category == "Building Confidence":
system_message = "Offer advice to help boost self-esteem, overcome self-doubt, and build confidence."
elif category == "Friendship & Social Skills":
system_message = "Provide tips for making friends, improving communication skills, and navigating social dynamics."
elif category == "Networking & Career Building":
system_message = "Offer advice on networking, building professional relationships, and finding internships or job opportunities."
elif category == "Work-Life Balance":
system_message = "Help the user balance academics, social life, and personal well-being."
elif category == "Mental Health":
system_message = "Provide empathetic advice to manage stress, anxiety, and other mental health challenges. Encourage seeking professional help when needed."
elif category == "Physical Health":
system_message = "Share tips for maintaining physical health, including fitness, sleep, and nutrition."
elif category == "Stress Management":
system_message = "Offer calming techniques and advice to handle stress effectively."
elif category == "Budgeting Tips":
system_message = "Provide practical advice for managing money, creating a budget, and saving effectively as a student."
elif category == "Finding Part-Time Jobs":
system_message = "Share tips for finding and balancing part-time work with academic responsibilities."
elif category == "Scholarships & Financial Aid":
system_message = "Provide advice on applying for scholarships, grants, and understanding financial aid options."
elif category == "Resume Building":
system_message = "Offer tips for creating an impressive resume tailored for internships or entry-level positions."
elif category == "Interview Preparation":
system_message = "Provide advice on how to prepare for interviews, including common questions and presentation tips."
elif category == "Skill Development":
system_message = "Suggest ways to build skills, gain certifications, and stand out in competitive fields."
elif category == "Exploring New Hobbies":
system_message = "Encourage trying new activities and provide ideas for hobbies that fit college life."
elif category == "Clubs & Extracurriculars":
system_message = "Offer advice on joining clubs, participating in activities, and enhancing the college experience."
elif category == "Creative Projects":
system_message = "Provide inspiration and resources for personal or group creative endeavors."
else:
system_message = "Category not recognized. Respond appropriately to the user's input."
try:
response = openai.ChatCompletion.create(
model="llama-3.1-70b-versatile",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": message}
]
)
return response.choices[0].message["content"]
except Exception as e:
return f"Error: {str(e)}"
# Chatbot function
def chatbot(user_input, category, history=[]):
bot_response = get_groq_response(user_input, category)
history.append((f"You: {user_input}", f"Bot: {bot_response}"))
return history, history
# Gradio Interface with enhanced styling
chat_interface = gr.Blocks(css="""
body {
font-family: 'Poppins', sans-serif;
background: linear-gradient(120deg, #ff9a9e, #fad0c4, #a1c4fd);
animation: gradientBG 10s ease infinite;
margin: 0;
padding: 0;
color: #333;
}
@keyframes gradientBG {
0% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
100% { background-position: 0% 50%; }
}
button {
background: linear-gradient(90deg, #6a11cb, #2575fc);
color: white;
padding: 0.8rem 1.5rem;
font-size: 1rem;
font-weight: bold;
border-radius: 20px;
border: none;
cursor: pointer;
transition: transform 0.2s ease, background 0.2s ease;
}
button:hover {
background: linear-gradient(90deg, #2575fc, #6a11cb);
transform: scale(1.1);
}
header {
text-align: center;
margin-bottom: 20px;
padding: 10px;
border-radius: 15px;
background: linear-gradient(90deg, #ff758c, #ff7eb3);
color: white;
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
}
.chat-container {
border: 2px solid #ff7eb3;
background: rgba(255, 255, 255, 0.8);
border-radius: 15px;
padding: 15px;
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
max-height: 300px;
overflow-y: auto;
}
""")
with chat_interface:
with gr.Row():
gr.Markdown("<h1 style='text-align:center;'>π Vibrant Personal Assistant Chatbot π</h1>")
with gr.Row():
gr.Markdown("<p style='text-align:center;'>Select a category and type your message to get tailored responses.</p>")
with gr.Row():
user_input = gr.Textbox(label="Your Message", placeholder="Type something...", lines=2)
category_dropdown = gr.Dropdown(
choices=[
"Study Tips", "Exam Preparation", "Project Guidance", "Time Management", "Motivation & Focus",
"Building Confidence", "Friendship & Social Skills", "Networking & Career Building", "Work-Life Balance",
"Mental Health", "Physical Health", "Stress Management", "Budgeting Tips", "Finding Part-Time Jobs",
"Scholarships & Financial Aid", "Resume Building", "Interview Preparation", "Skill Development",
"Exploring New Hobbies", "Clubs & Extracurriculars", "Creative Projects"
],
label="Choose Chat Category"
)
with gr.Row():
send_button = gr.Button("Send")
with gr.Row():
with gr.Column() as chat_container:
chatbot_output = gr.Chatbot(label="Chat History")
# Add functionality to handle interactions
def handle_chat(user_input, category, history):
if not user_input.strip():
return history, history
updated_history, _ = chatbot(user_input, category, history)
return updated_history, updated_history
send_button.click(
handle_chat,
inputs=[user_input, category_dropdown, chatbot_output],
outputs=[chatbot_output, chatbot_output]
)
chat_interface.launch()
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