Create app.py

app.py

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+# app.py
+import os
+import streamlit as st
+from PIL import Image
+from groq import Groq
+import base64
+import io
+
+# Set GROQ API Key (put your key directly for Colab or use environment variables)
+os.environ["GROQ_API_KEY"] = "your-groq-api-key-here"
+
+# Initialize GROQ client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+st.set_page_config(page_title="AI Trade Predictor", layout="wide")
+st.markdown("""
+    <style>
+    .main {
+        background-color: #0d1117;
+        color: white;
+    }
+    .stButton>button {
+        background-color: #1f6feb;
+        color: white;
+        font-weight: bold;
+    }
+    .stFileUploader label {
+        color: #58a6ff;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("\U0001F4B0 AI Trade Predictor")
+st.markdown("Upload a candlestick chart image and get a trading signal analysis using AI")
+
+# Upload chart image
+uploaded_file = st.file_uploader("Upload Candlestick Chart Image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Chart", use_column_width=True)
+
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    img_str = base64.b64encode(buffer.getvalue()).decode()
+
+    if st.button("Analyze Chart \U0001F52C"):
+        with st.spinner("Analyzing chart and generating predictions..."):
+            prompt = f"""
+            You are an expert trading analyst AI.
+            Analyze the attached candlestick chart image (base64 below).
+            Apply technical strategies like RSI, MACD, moving averages, support/resistance, candlestick patterns.
+            Then tell:
+            1. Whether to BUY or SELL.
+            2. The confidence level in %.
+            3. The best timeframe for this prediction.
+            4. The risk level and how it might go wrong.
+            5. Why this prediction was made.
+            Base64 image: {img_str}
+            """
+            
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                model="llama-3.3-70b-versatile"
+            )
+
+            result = chat_completion.choices[0].message.content
+            st.markdown("### \U0001F4C8 Prediction Result")
+            st.markdown(result)
+
+else:
+    st.info("Please upload a candlestick chart image to begin analysis.")