import streamlit as st
from groq import Groq
import time
import matplotlib.pyplot as plt

# API Key (replace with yours)
API_KEY = "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"

# Function to interact with the model
def get_model_response(prompt):
    try:
        client = Groq(api_key=API_KEY)
        chat_completion = client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            model="llama3-8b-8192",  # Update with your desired model
        )
        response = chat_completion.choices[0].message.content
        return response
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Streamlit App
st.set_page_config(page_title="AI Chat Interface", page_icon="shirt_template.png", layout="wide")

# Add a header with logo (optional)
st.markdown("""
<div style="text-align: center;">
  <img src="https://placehold.it/150x150" alt="Logo" style="border-radius: 50%;">
  <h1>AI Chat Interface</h1>
</div>
""", unsafe_allow_html=True)

# Progress bar animation for loading
with st.spinner("Thinking..."):
    time.sleep(2)  # Simulate processing time

# User input with placeholder and help text
user_input = st.text_input("Your Prompt", placeholder="Type your question here...", help="Feel free to ask anything!")

# Display "Thinking..." animation while fetching response
if st.button("Get Response"):
    with st.spinner("Fetching Response..."):
        response = get_model_response(user_input)
    st.write(f"**Model Response:** {response}")

# Success message animation
st.success("Response retrieved successfully!")

# Example of a simple chart (if applicable)
if "chart" in response.lower():
    # Generate some random data
    x = [1, 2, 3, 4, 5]
    y = [2, 4, 5, 4, 5]

    # Create a simple line chart
    plt.plot(x, y)
    plt.xlabel("X-axis")
    plt.ylabel("Y-axis")
    plt.title("Simple Line Chart")
    st.pyplot(plt)

# Additional tips:
# - Use custom CSS to style the app further.
# - Explore Streamlit's components library for more interactive elements.
# - Consider using a library like `plotly` for more advanced visualizations.
# - Implement error handling and logging for a robust app.
# - Test your app thoroughly on different browsers and devices.