import streamlit as st
from PIL import Image
import os
from groq import Groq
from dotenv import load_dotenv

load_dotenv()

st.set_page_config(page_title="AI Trade Predictor", layout="wide")
st.markdown("""
    <style>
        .main {
            background-color: #f5f7fa;
        }
        h1 {
            color: #3b3b3b;
        }
        .stButton > button {
            color: white;
            background-color: #4CAF50;
            font-size: 16px;
            border-radius: 10px;
            padding: 10px 24px;
        }
    </style>
""", unsafe_allow_html=True)

st.title("📈 AI Trade Predictor")

uploaded_file = st.file_uploader("Upload a candlestick chart image", type=["png", "jpg", "jpeg"])

if uploaded_file:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Chart', use_column_width=True)
    st.write("Analyzing chart using AI model...")

    # Convert image to base64 string
    import base64
    import io
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")

    # Connect to Groq and send the image analysis prompt
    client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

    user_prompt = f"""
    Analyze the following candlestick chart image (base64-encoded PNG) and provide a trading decision:
    - Tell whether the action should be BUY, SELL, or HOLD.
    - Give confidence level in percentage.
    - Suggest timeframes (e.g., 30 min, 1 hour, 4 hour, 1 day) and what signal applies to each.
    - List any risks or reasons the prediction may fail.
    - Use clear language that a beginner can understand.
    - Give a short summary at the end.

    Image (base64 PNG): {img_str}
    """

    try:
        chat_completion = client.chat.completions.create(
            messages=[
                {"role": "user", "content": user_prompt}
            ],
            model="meta-llama/llama-guard-4-12b"
        )
        response = chat_completion.choices[0].message.content
        st.success("Prediction Complete")
        st.markdown(response)

    except Exception as e:
        st.error(f"Something went wrong: {e}")


# --- FOOTER ---
st.markdown("---")
st.markdown("Made ❤️ by Abdullah's AI Labs")