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Update app.py
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app.py
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import os
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import requests
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import gradio as gr
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from fastapi import FastAPI, Request
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from transformers import pipeline
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# ======================
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# 1) تحميل المودل من الريبو حقك
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# ======================
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deployed_repo_id = "maryaa4/my-arabic-sentiment-model"
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loaded_sentiment_pipeline = pipeline(
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"sentiment-analysis",
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model=deployed_repo_id,
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trust_remote_code=True,
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)
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def predict_sentiment(text: str) -> str:
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if not text or text.strip() == "":
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return "رجاءً أدخل نص عربي عشان أقدر أحلله 📝"
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result = loaded_sentiment_pipeline(text)
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label = result[0][
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score = result[0][
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# تقدرين تغيرين طريقة العرض هنا
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return f"التصنيف: {label}\nنسبة الثقة: {score:.2f}"
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# ======================
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# 2) واجهة Gradio للسبيس
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# ======================
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iface = gr.Interface(
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fn=predict_sentiment,
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inputs=gr.Textbox(lines=5, placeholder="أدخل النص العربي هنا..."),
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outputs="text",
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title="Arabic Sentiment Analysis",
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description="
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)
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#
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# 3) إعداد FastAPI + Telegram Webhook
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# ======================
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app = FastAPI()
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# نحط التوكن في Secrets في السبيس باسم TELEGRAM_TOKEN
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BOT_TOKEN = os.getenv("TELEGRAM_TOKEN")
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TELEGRAM_API = (
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f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
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if BOT_TOKEN
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else None
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)
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@app.post("/telegram")
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async def telegram_webhook(request: Request):
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"""
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هذا هو الويبهوك اللي تيليجرام يناديه
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"""
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data = await request.json()
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# نتأكد إن فيه رسالة
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if "message" in data and "text" in data["message"]:
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chat_id = data["message"]["chat"]["id"]
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user_text = data["message"]["text"]
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# نستخدم نفس الدالة حقت Gradio
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reply = predict_sentiment(user_text)
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if TELEGRAM_API:
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try:
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requests.post(
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TELEGRAM_API,
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json={"chat_id": chat_id, "text": reply},
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)
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except Exception as e:
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print("Error sending message to Telegram:", e)
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return {"ok": True}
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# ======================
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# 4) تركيب Gradio فوق FastAPI
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# ======================
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# هذا يخلي السبيس يفتح واجهة Gradio على المسار /
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# والـ API حق تيليجرام على /telegram
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app = gr.mount_gradio_app(app, iface, path="/")
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import gradio as gr
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from transformers import pipeline
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deployed_repo_id = "maryaa4/my-arabic-sentiment-model"
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loaded_sentiment_pipeline = pipeline('sentiment-analysis', model=deployed_repo_id, trust_remote_code=True)
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def predict_sentiment(text):
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if not text:
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return "Please enter some text for sentiment analysis."
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result = loaded_sentiment_pipeline(text)
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label = result[0]['label']
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score = result[0]['score']
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return f"Sentiment: {label.capitalize()}, Confidence: {score:.4f}"
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iface = gr.Interface(
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fn=predict_sentiment,
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inputs=gr.Textbox(lines=5, placeholder="أدخل النص العربي هنا..."),
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outputs="text",
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title="Arabic Sentiment Analysis",
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description=f"A sentiment analysis model for Arabic text, deployed from maryaa4/my-arabic-sentiment-model. Enter Arabic text and get the predicted sentiment."
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)
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iface.launch(share=True) # share=True for a temporary public link
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