NeuralNodeAI/sentic-ai-backend

1from fastapi import FastAPI2from fastapi.responses import HTMLResponse  3from pydantic import BaseModel4from fastapi.middleware.cors import CORSMiddleware5from transformers import pipeline6from langdetect import detect7import re8 9# إنشاء تطبيق الـ FastAPI10app = FastAPI()11 12# إضافة نظام الـ CORS13app.add_middleware(14    CORSMiddleware,15    allow_origins=["*"],16    allow_methods=["*"],17    allow_headers=["*"],18)19 20# تعريف الموديلات (Pipelines)21print("Loading models... please wait.") # رسالة عشان تعرف في اللوجز إنه بيحمل22sentiment_model = pipeline("sentiment-analysis")23topic_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")24 25# تعريف شكل البيانات26class UserRequest(BaseModel):27    text: str28 29# 2. تعديل الـ Home ليعرض ملف HTML بدلاً من رسالة JSON30@app.get("/", response_class=HTMLResponse)31def home():32    # هنا السيرفر بيقرأ ملف index.html اللي أنت رفعته وبيرجعه للمتصفح33    try:34        with open("index.html", "r", encoding="utf-8") as f:35            return f.read()36    except FileNotFoundError:37        return "<h1>Error: index.html not found. Please upload the file.</h1>"38 39@app.post("/analyze")40def analyze_content(request: UserRequest):41    # تنظيف النص42    input_text = request.text.strip()43 44    # التحقق من المدخلات45    if not input_text or not re.search('[a-zA-Zا-ي]', input_text):46        return {"error": "Invalid input. Please provide a clear and meaningful text for analysis."}47 48    # تحليل المشاعر49    sentiment_data = sentiment_model(input_text)[0]50    sentiment_label = sentiment_data['label']51    52    # صياغة الرد53    if sentiment_label == "NEGATIVE":54        sentiment_feedback = "We detected a negative tone. Remember that challenges are just opportunities for growth."55    else:56        sentiment_feedback = "We detected a positive tone. Your optimism is truly inspiring and adds great value."57 58    # كشف اللغة59    try:60        language_code = detect(input_text)61    except:62        language_code = "Unknown"63 64    # تصنيف الموضوع65    possible_categories = ["Politics", "Sports", "Technology", "Economy", "Health"]66    classification_output = topic_model(input_text, candidate_labels=possible_categories)67    dominant_topic = classification_output['labels'][0]68 69    # تجميع النتائج70    formatted_response = (71        f"Content Analysis: This text is classified under [{dominant_topic}]. "72        f"Detected Language: [{language_code}]. "73        f"AI Insight: {sentiment_feedback}"74    )75 76    return {77        "status": "success",78        "result": formatted_response,79        "raw_data": {80            "category": dominant_topic,81            "language": language_code,82            "sentiment": sentiment_label83        }84    }85