Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from transformers import pipeline
+import pandas_ta as ta
+import requests
+
+# 1. تحميل نموذجك المدرب (أو تدريبه هنا)
+def load_model():
+    try:
+        # مثال: تحميل نموذج من Hugging Face Hub
+        return pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis")
+    except:
+        return None
+
+model = load_model()
+
+# 2. جلب بيانات العملات
+def fetch_crypto_data(coin_id="bitcoin", days=30):
+    url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart?vs_currency=usd&days={days}"
+    data = requests.get(url).json()
+    df = pd.DataFrame(data['prices'], columns=['timestamp', 'price'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    return df
+
+# 3. تحليل فني + تنبؤ
+def analyze(coin):
+    df = fetch_crypto_data(coin)
+    
+    # حساب المؤشرات الفنية
+    df['RSI'] = ta.rsi(df['price'])
+    df['MACD'] = ta.macd(df['price'])['MACD_12_26_9']
+    
+    # تنبؤ مبسط (استبدل بنموذجك الفعلي)
+    last_price = df['price'].iloc[-1]
+    prediction = last_price * (1 + np.random.uniform(-0.1, 0.1))
+    
+    # تحليل المشاعر
+    sentiment = model("Cryptocurrency market is booming")[0]['label'] if model else "Neutral"
+    
+    return {
+        "price": last_price,
+        "prediction": prediction,
+        "rsi": df['RSI'].iloc[-1],
+        "sentiment": sentiment
+    }
+
+# 4. واجهة Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚀 محلل العملات المشفرة بالذكاء الاصطناعي")
+    
+    with gr.Row():
+        coin = gr.Dropdown(["bitcoin", "ethereum"], label="اختر العملة")
+        btn = gr.Button("حلل الآن")
+    
+    with gr.Row():
+        price = gr.Textbox(label="السعر الحالي")
+        prediction = gr.Textbox(label="التنبؤ")
+    
+    with gr.Row():
+        rsi = gr.Textbox(label="مؤشر RSI")
+        sentiment = gr.Textbox(label="مشاعر السوق")
+    
+    btn.click(
+        fn=lambda c: analyze(c),
+        inputs=coin,
+        outputs=[price, prediction, rsi, sentiment]
+    )
+
+demo.launch()