Update app.py

app.py

@@ -1,439 +1,406 @@
-import gradio as gr
-import torch
-# We will use a mock predictor as the original library might not be available in this environment
-# from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import pandas as pd
-import plotly.express as px
-import json
+# ==============================================================================
+# Actora: Social Media Post Performance Predictor
+# ==============================================================================
+# This application provides tools to predict engagement metrics for social media
+# posts and offers analysis using the Gemini API.
+#
+# To run this application:
+# 1. Create a virtual environment:
+#    python -m venv venv
+#    source venv/bin/activate  # On Windows: venv\Scripts\activate
+#
+# 2. Install dependencies:
+#    pip install -r requirements.txt
+#    (Ensure requirements.txt contains: gradio, python-dotenv, google-generativeai,
+#     bleach, torch, transformers, sentencepiece)
+#    # TODO: Review requirements.txt for any unused packages.
+#
+# 3. Create a .env file with your API key:
+#    GEMINI_API_KEY="your_gemini_api_key_here"
+#    USE_MOCK_MODEL="True"  # Set to "False" to use the real model
+#    # ACTORA_SCALE_FACTOR="10.0" # Optional: a multiplier for the real model's outputs
+#
+# 4. Create an 'assets' directory and place 'logo.svg' inside it.
+#
+# 5. Run the application:
+#    python app.py
+# ==============================================================================
+
 import os
-import google.generativeai as genai
-import warnings
 import logging
-import html
-import datetime
-from markdown_it import MarkdownIt
 import base64
+import re
+import time
+import random
+from typing import Dict
 
-# --- الخطوة الأهم: الإعدادات والتهيئة ---
+from dotenv import load_dotenv
+import bleach
+import gradio as gr
+import google.generativeai as genai
 
-# ==============================================================================
-# ▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼
-# الصق مفتاح Gemini API الخاص بك هنا بين علامتي التنصيص
-# يمكنك الحصول عليه من Google AI Studio
-GEMINI_API_KEY = "AIzaSyC5k5j07zeDWxJATTzx6zNABXwXQWvLIqw"
-# ▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲
-# ==============================================================================
+# Required for the real model predictor
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
+# --- Configuration ---
+load_dotenv()
 
-# --- بقية الإعدادات (لا تحتاج لتعديلها) ---
-warnings.filterwarnings("ignore", category=UserWarning)
-logging.getLogger("transformers").setLevel(logging.ERROR)
+GEMINI_API_KEY = os.getenv("AIzaSyC5k5j07zeDWxJATTzx6zNABXwXQWvLIqw")
+USE_MOCK_MODEL = os.getenv("USE_MOCK_MODEL", "True").lower() in ("true", "1", "t")
+MODEL_REPO = os.getenv("MODEL_REPO", "amrtweg/Actora")
 
-# A mock class to simulate the original ActoraPredictor if it's not available
-class MockActoraPredictor:
-    """A mock predictor that returns dummy data for UI testing."""
-    def predict(self, text):
-        return {
-            'liked': 150, 'loved': 70, 'haha': 20, 'wow': 15, 'sad': 5, 'angry': 2,
-            'comments': 25, 'shares': 10, 'interactions': 297
-        }
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - [%(funcName)s] - %(message)s'
+)
 
-# إعداد واجهة برمجة تطبيقات Gemini
-try:
-    if not GEMINI_API_KEY or GEMINI_API_KEY == "PASTE_YOUR_KEY_HERE":
-        print("تحذير: لم يتم توفير مفتاح GEMINI_API_KEY. ميزة التحليل الاستراتيجي لن تعمل.")
-        GEMINI_ENABLED = False
-    else:
-        genai.configure(api_key=GEMINI_API_KEY)
-        GEMINI_ENABLED = True
-except Exception as e:
-    print(f"خطأ في إعداد Gemini: {e}")
-    GEMINI_ENABLED = False
-
-actora_emoji_map = {
-    "liked": "👍", "loved": "❤️", "haha": "😂", "wow": "😮", "sad": "😢", "angry": "😠",
-    "comments": "💬", "shares": "🔗", "interactions": "📊"
-}
-
-# Use the Mock Predictor for stability in this environment
-predictor = MockActoraPredictor()
-MODEL_LOADED = True
-MODEL_ERROR_MESSAGE = ""
-
-
-# أيقونات SVG للتفاعلات (تم التحقق منها وتصحيحها)
-EMOJI_MAP_SVG = {
-    "liked": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%230866FF'/%3E%3Cpath fill='%23fff' d='M7.2 3.2c0-.44.36-.8.8-.8.74 0 1.34.6 1.34 1.34v1.86h1.76c.96 0 1.66.9 1.44 1.86l-.45 1.88a2.2 2.2 0 01-2.14 1.69H6.9a.55.55 0 01-.55-.55V7.9c0-.4.09-.8.27-1.16l.27-.55c.27-.55.41-1.16.41-1.77V3.2zM3.7 7.05c-.42 0-.76.34-.76.76v3.2c0 .42.34.76.76.76h.76c.2 0 .37-.17.37-.37V7.42c0-.2-.17-.37-.37-.37H3.7z'/%3E%3C/svg%3E",
-    "loved": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%23FA2E3E'/%3E%3Cpath fill='%23fff' d='M8 11.1c2.1-1.43 3.9-2.67 3.9-4.36 0-.97-.73-1.92-1.82-1.92-.74 0-1.37.47-1.64 1.14-.27-.67-.9-1.14-1.64-1.14-1.09 0-1.82.95-1.82 1.92 0 1.7 1.8 2.94 3.9 4.36z'/%3E%3C/svg%3E",
-    "haha": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%23F6A623'/%3E%3Ccircle cx='5.2' cy='5.6' r='1' fill='%231C1C1D'/%3E%3Ccircle cx='10.8' cy='5.6' r='1' fill='%231C1C1D'/%3E%3Cpath fill='%231C1C1D' d='M4.6 9.6c.5 1.1 1.9 1.7 3.4 1.7s2.9-.6 3.4-1.7c.1-.3-.1-.6-.5-.6H5c-.4 0-.6.3-.4.6z'/%3E%3C/svg%3E",
-    "wow": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%23FFB169'/%3E%3Ccircle cx='5.3' cy='5.6' r='1.2' fill='%231C1C1D'/%3E%3Ccircle cx='10.7' cy='5.6' r='1.2' fill='%231C1C1D'/%3E%3Ccircle cx='8' cy='10.2' r='1.6' fill='%231C1C1D'/%3E%3C/svg%3E",
-    "sad": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%237A7F85'/%3E%3Ccircle cx='5.3' cy='6.1' r='.9' fill='%23fff'/%3E%3Ccircle cx='10.7' cy='6.1' r='.9' fill='%23fff'/%3E%3Cpath stroke='%23fff' stroke-width='1.2' d='M5 11c.8-1 2-1.4 3-1.4s2.2.4 3 1.4'/%3E%3C/svg%3E",
-    "angry": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Ccircle cx='8' cy='8' r='7.5' fill='%23E11731'/%3E%3Cpath stroke='%23fff' stroke-width='1.2' d='M5 6l2 1M11 6l-2 1'/%3E%3Cpath stroke='%23fff' stroke-width='1.2' d='M5 11c1-.6 2-.8 3-.8s2 .2 3 .8'/%3E%3C/svg%3E",
-    "shares": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Cpath d='M10.6 4.7 14 8l-3.4 3.3M14 8H2' stroke='%2394A3B8' stroke-width='1.5' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E",
-    "comments": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Cpath d='M8 14.5c-.6 0-1.2-.1-1.7-.3-.5-.2-1-.4-1.4-.7L1.5 15l.7-3.8C1.7 10.7 1.1 9.6 1.1 8.4c0-3 2.2-5.4 6-5.4 3 0 5.8 2 6.2 5 .2 1.2 0 2.4-.6 3.4-.6.9-1.5 1.6-2.6 1.9-.8.2-1.6.3-2.3.3Z' stroke='%2394A3B8' stroke-width='1.4'/%3E%3C/svg%3E",
-    "interactions": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none'%3E%3Cpath d='M2 8a6 6 0 1 1 9.4 4.8' stroke='%2394A3B8' stroke-width='1.2'/%3E%3Cpath d='M8 15H5' stroke='%2394A3B8' stroke-width='1.2'/%3E%3Cpath d='M8 1H5' stroke='%2394A3B8' stroke-width='1.2'/%3E%3C/svg%3E"
-}
-
-# Base64 encoded logo
-LOGO_BASE64 = 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"
-
-# --- الدوال المنطقية والتحليلية ---
-
-def get_prediction_results(text: str):
-    if not MODEL_LOADED: raise gr.Error(MODEL_ERROR_MESSAGE)
-    if not text or not text.strip(): return None
+# --- Asset Loading ---
+def load_logo_as_data_url(path: str = "assets/logo.svg") -> str:
+    """Reads the logo file, base64 encodes it, and returns a data URL."""
     try:
-        raw_predictions = predictor.predict(text)
-        reverse_map = {v: k for k, v in actora_emoji_map.items()}
-        return {reverse_map.get(k, k): v for k, v in raw_predictions.items()}
-    except Exception as e:
-        raise gr.Error(f"خطأ أثناء تحليل النص مع Actora: {e}")
-
-def get_gemini_insights(results: dict, text: str) -> str:
-    if not GEMINI_ENABLED:
-        return "<p>ميزة التحليل المتقدم معطلة. يرجى إعداد مفتاح API الخاص بـ Gemini في ملف app.py.</p>"
-    prompt = f"""
-        # المهمة
-        أنت "MegoFlow AI"، مستشار تسويق رقمي وخبير في تحليل النصوص الإعلانية. تحليلك يجب أن يكون دقيقاً، موجزاً، وقابلاً للتنفيذ بشكل مباشر.
-
-        # السياق
-        لدي منشور على وسائل التواصل الاجتماعي أريد تحليله. قام نموذج تنبؤي بمنحه النتائج التالية (من إجمالي {results.get('interactions', 0)} تفاعل متوقع):
-        - إعجابات متوقعة: {results.get('liked', 0)}
-        - قلوب متوقعة: {results.get('loved', 0)}
-        - تعليقات متوقعة: {results.get('comments', 0)}
-        - مشاركات متوقعة: {results.get('shares', 0)}
-        - تفاعلات سلبية (حزن/غضب): {results.get('sad', 0) + results.get('angry', 0)}
-
-        # محتوى المنشور
+        with open(path, "rb") as f:
+            encoded_svg = base64.b64encode(f.read()).decode("utf-8")
+        return f"data:image/svg+xml;base64,{encoded_svg}"
+    except FileNotFoundError:
+        logging.warning(f"Logo not found at '{path}'. Using a placeholder.")
+        placeholder_svg = '<svg xmlns="http://www.w3.org/2000/svg" width="120" height="40" viewBox="0 0 120 40"><text x="10" y="28" font-family="Arial, sans-serif" font-size="20" font-weight="bold" fill="#333">Actora</text></svg>'
+        encoded_svg = base64.b64encode(placeholder_svg.encode("utf-8")).decode("utf-8")
+        return f"data:image/svg+xml;base64,{encoded_svg}"
+
+LOGO_DATA_URL = load_logo_as_data_url()
+
+# --- Predictor Models ---
+
+class MockPredictor:
+    """A mock predictor that returns random data for UI testing."""
+    def __init__(self, model_path: str, **kwargs):
+        self.is_ready = True
+        logging.info(f"MockPredictor initialized. Path '{model_path}' is ignored.")
+
+    def predict(self, text: str) -> Dict[str, int]:
+        """Generates random-ish metrics for a given text."""
+        if not text or not text.strip():
+            return {}
+        time.sleep(0.3)  # Simulate network/model latency
+        results = {
+            "liked": random.randint(10, 200),
+            "loved": random.randint(5, 50),
+            "haha": random.randint(0, 30),
+            "wow": random.randint(0, 25),
+            "sad": random.randint(0, 10),
+            "angry": random.randint(0, 8),
+            "comments": random.randint(5, 60),
+            "shares": random.randint(2, 40),
+        }
+        results["interactions"] = sum(v for k, v in results.items() if k not in ["comments", "shares"])
+        return results
+
+def _normalize_label(s: str) -> str:
+    """تبسيط اسم الليبل للمطابقة: lowercase وبدون مسافات/رموز."""
+    return re.sub(r"[^a-z]+", "", s.lower())
+
+class ActoraPredictor:
+    """
+    Predictor حقيقي يعتمد على موديل هاجينج فيس من نوع SequenceClassification.
+    - بيقرأ id2label من config علشان يعرف ترتيب المخرجات.
+    - بيحوّل logits لقيم موجبة باستخدام softplus.
+    - لو لقى labels زي liked/loved/... هيخرّجها مباشرة.
+    - لو labels ناقصة، بيكملها بـ 0 عادي.
+    - بيحسب interactions = مجموع كل المقاييس المتاحة.
+    ملاحظات:
+    - بعض الموديلات بتطلع logits صغيرة (تشبه احتمالات). لو القيم صغيرة جدًا
+      ممكن تزود المقياس بـ ACTORA_SCALE_FACTOR من .env (افتراضي 1.0).
+    """
+    CANONICAL_KEYS = ["liked", "loved", "haha", "wow", "sad", "angry", "comments", "shares"]
+
+    def __init__(self, model_path: str, device: str = None, max_length: int = 256):
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.max_length = max_length
+        self.scale = float(os.getenv("ACTORA_SCALE_FACTOR", "1.0"))
+
+        logging.info(f"[ActoraPredictor] Loading model: {model_path} on {self.device}")
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+            self.model = AutoModelForSequenceClassification.from_pretrained(model_path).to(self.device)
+            self.model.eval()
+        except Exception as e:
+            logging.error(f"Failed to load Hugging Face model '{model_path}'. Error: {e}")
+            raise
+
+        # جهّز خريطة label -> index من config
+        id2label = getattr(self.model.config, "id2label", {}) or {}
+        if not id2label and hasattr(self.model.config, "label2id"):
+            self.label2id = {k.lower(): int(v) for k, v in self.model.config.label2id.items()}
+        else:
+            self.label2id = {str(v).lower(): int(k) for k, v in id2label.items()}
+
+        # ابني Mapping من الكانونيكال للـ index باستخدام مطابقة مرنة
+        self.idx_map: Dict[str, int] = {}
+        flattened = { _normalize_label(lbl): idx for lbl, idx in self.label2id.items() }
+        for key in self.CANONICAL_KEYS:
+            candidates = [key, key + "s", key + "_count", key + "count"]
+            found = None
+            for c in candidates:
+                norm = _normalize_label(c)
+                for flbl, idx in flattened.items():
+                    if flbl == norm or flbl.endswith(norm) or norm in flbl:
+                        found = idx
+                        break
+                if found is not None:
+                    break
+            if found is not None:
+                self.idx_map[key] = found
+        logging.info(f"[ActoraPredictor] Label map resolved: {self.idx_map}")
+
+        if not self.idx_map:
+            logging.warning("[ActoraPredictor] Label map is empty. The model might not have recognizable labels.")
+        
+        self.is_ready = True
+
+    @torch.inference_mode()
+    def predict(self, text: str) -> Dict[str, int]:
+        if not self.is_ready:
+            raise RuntimeError("ActoraPredictor is not ready.")
+        if not text or not text.strip():
+            return {}
+
+        enc = self.tokenizer(
+            text, return_tensors="pt", truncation=True, max_length=self.max_length, padding=False
+        )
+        enc = {k: v.to(self.device) for k, v in enc.items()}
+        out = self.model(**enc)
+        logits = out.logits.squeeze(0)
+        positive = F.softplus(logits).cpu().tolist()
+
+        results: Dict[str, int] = {}
+        for key in self.CANONICAL_KEYS:
+            if key in self.idx_map:
+                val = positive[self.idx_map[key]] * self.scale
+                results[key] = int(round(float(val)))
+            else:
+                results[key] = 0
+
+        results["interactions"] = sum(results.get(k, 0) for k in self.CANONICAL_KEYS)
+        return results
+
+# --- Gemini API Client ---
+
+class GeminiClient:
+    """Handles communication with the Google Gemini API."""
+    def __init__(self, api_key: str):
+        self.model = None
+        if not api_key:
+            logging.warning("GEMINI_API_KEY not found. Gemini-based analysis will be disabled.")
+        else:
+            try:
+                genai.configure(api_key=api_key)
+                self.model = genai.GenerativeModel('gemini-pro')
+                logging.info("Gemini client initialized successfully.")
+            except Exception as e:
+                logging.error(f"Failed to configure Gemini client: {e}")
+
+    def get_analysis(self, post_text: str, metrics: Dict[str, int]) -> str:
+        """Generates post analysis using Gemini, with sanitization."""
+        if USE_MOCK_MODEL:
+            return f"**تحليل وهمي للمنشور:**\n\nهذا النص هو مثال لكيفية ظهور تحليل Gemini. عند تفعيل النموذج الحقيقي، سيتم هنا عرض رؤى واقتراحات حقيقية بناءً على النص والمقاييس المتوقعة: `{metrics}`."
+
+        if not self.model:
+            return "ميزة تحليل Gemini غير مفعلة. يرجى التحقق من مفتاح API."
+
+        prompt = f"""
+        أنت خبير في التسويق عبر وسائل التواصل الاجتماعي. بالنظ�� إلى نص المنشور التالي والمقاييس المتوقعة له، قدم تحليلاً موجزاً ونقاط قابلة للتنفيذ باللغة العربية.
+        
+        التحليل يجب أن يتضمن:
+        1.  **نقاط القوة:** ما الذي تم عمله بشكل جيد في المنشور؟
+        2.  **فرص للتحسين:** كيف يمكن تحسين المنشور لجذب تفاعل أكبر؟
+        3.  **اقتراح:** قدم مثالاً على نسخة محسنة من المنشور.
+        
+        **نص المنشور:**
         ---
-        {text}
+        {post_text}
         ---
-
-        # المطلوب
-        بناءً على نص المنشور ونتائجه المتوقعة، قدم تحليلاً نقدياً باللغة العربية في قسمين. استخدم تنسيق Markdown:
-        1.  **"تشخيص الأداء":** في جملة واحدة، اشرح ماذا تخبرنا هذه الأرقام عن الاستقبال المحتمل للمنشور من قبل الجمهور.
-        2.  **"مقترحات للنمو":** قدم 3 نقاط بالضبط على شكل قائمة. كل نقطة يجب أن تكون اقتراحاً ملموساً ومبدعاً لتحسين المنشور. لا تقدم نصائح عامة مثل "أضف CTA"، بل اقترح CTA *محدد* أو إعادة صياغة *محددة* لجملة في النص. يجب أن تكون النصائح مرتبطة مباشرة بالنص والنتائج.
+        
+        **المقاييس المتوقعة:**
+        - إجمالي التفاعلات: {metrics.get('interactions', 0)}
+        - إعجابات (Like): {metrics.get('liked', 0)}
+        - قلوب (Love): {metrics.get('loved', 0)}
+        - تعليقات: {metrics.get('comments', 0)}
+        - مشاركات: {metrics.get('shares', 0)}
+
+        **التحليل:**
         """
+        try:
+            response = self.model.generate_content(prompt)
+            # Sanitize Gemini output to prevent injection attacks
+            sanitized_text = bleach.clean(response.text, tags=[], strip=True)
+            return sanitized_text
+        except Exception as e:
+            logging.error(f"Error calling Gemini API: {e}")
+            return f"حدث خطأ أثناء التواصل مع Gemini: {e}"
+
+# --- Model Initialization ---
+gemini_client = GeminiClient(api_key=GEMINI_API_KEY)
+
+# Use a comment to clarify how to switch models. The logic depends on the .env file.
+if USE_MOCK_MODEL:
+    logging.info("Using MockPredictor. To use the real model, set USE_MOCK_MODEL='False' in .env file.")
+    predictor = MockPredictor(model_path="mock/path")
+else:
+    logging.info(f"Using REAL ActoraPredictor with model repo: {MODEL_REPO}.")
     try:
-        model = genai.GenerativeModel('gemini-1.5-flash-latest')
-        response = model.generate_content(prompt)
-        md = MarkdownIt()
-        return md.render(response.text)
+        predictor = ActoraPredictor(model_path=MODEL_REPO)
     except Exception as e:
-        return f"<p style='color: #ff9999;'>حدث خطأ أثناء استدعاء Gemini: {e}</p>"
-
-def create_results_card(title: str, text: str, results: dict) -> str:
-    if not results: return ""
-    safe_text = html.escape(text)
-    formatted_text = safe_text.replace("\n", "<br>")
-    rankables = {k: v for k, v in results.items() if k not in ["comments", "shares", "interactions"]}
-    sorted_reactions = sorted(rankables.items(), key=lambda item: item[1], reverse=True)
-    total_interactions = results.get("interactions", 0)
-    comments = results.get("comments", 0)
-    shares = results.get("shares", 0)
-    insights_html = get_gemini_insights(results, text)
-    html_content = f"""
-    <div class='result-card'>
-        <div class='card-header'>{html.escape(title)}</div>
-        <div class='post-preview'>{formatted_text}</div>
-        <div class='card-body'>
-            <div class='total-score'>
-                <img src='{EMOJI_MAP_SVG['interactions']}' class='icon-total'/>
-                <div><span class='total-value'>{total_interactions}</span><span class='total-label'>إجمالي التفاعل المتوقع</span></div>
-            </div>
-            <div class='breakdown-title'>توزيع التفاعلات</div>
-            <div class='breakdown-grid'>
-    """
-    for name, value in sorted_reactions:
-        if value > 0 and name in EMOJI_MAP_SVG:
-            percentage = (value / total_interactions * 100) if total_interactions > 0 else 0
-            html_content += f"""
-            <div class='reaction-row'>
-                <img src='{EMOJI_MAP_SVG[name]}' class='icon-reaction'/>
-                <span class='reaction-name'>{name.capitalize()}</span>
-                <div class='progress-bar-container'><div class='progress-bar' style='width: {percentage}%;'></div></div>
-                <span class='reaction-value'>{value}</span>
-            </div>
-            """
-    html_content += "</div>" 
-    if comments > 0 or shares > 0:
-        html_content += "<div class='secondary-stats'>"
-        if comments > 0: html_content += f"<span><img src='{EMOJI_MAP_SVG['comments']}' class='icon-secondary'/> {comments} تعليق</span>"
-        if shares > 0: html_content += f"<span><img src='{EMOJI_MAP_SVG['shares']}' class='icon-secondary'/> {shares} مشاركة</span>"
-        html_content += "</div>"
-    html_content += f"""
-        <div class='insights-section'>
-            <div class='breakdown-title insights-title'>💡 تحليل المستشار الآلي (Gemini)</div>
-            <div class='gemini-insights'>{insights_html}</div>
-        </div>
-    """
-    html_content += "</div></div>"
-    return html_content
-
-def update_history_display(history: list) -> str:
-    if not history: return "<div class='placeholder'>لا توجد تحليلات في السجل حتى الآن.</div>"
-    history_html = "<div class='history-container'>"
-    for entry in reversed(history):
-        preview = html.escape(entry['text'][:100]) + ('...' if len(entry['text']) > 100 else '')
-        score = entry['results'].get('interactions', 0)
-        history_html += f"""
-        <div class='history-item'>
-            <div class='history-score'>{score}</div>
-            <div class='history-details'>
-                <div class='history-text'>{preview}</div>
-                <div class='history-time'>{entry['timestamp']}</div>
-            </div>
-        </div>
-        """
-    history_html += "</div>"
-    return history_html
-
-def analyze_single_post(text: str, current_history: list):
-    if not text or not text.strip():
-        placeholder_html = "<div class='placeholder'>ابدأ بكتابة منشور في الصندوق أعلاه ثم اضغط 'حلّل الآن'.</div>"
-        return placeholder_html, update_history_display(current_history), current_history
-    results = get_prediction_results(text)
-    if results:
-        card_html = create_results_card("تحليل المنشور", text, results)
-        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        new_entry = {'text': text, 'results': results, 'timestamp': timestamp}
-        current_history.append(new_entry)
-        history_html = update_history_display(current_history)
-        return card_html, history_html, current_history
-    no_results_html = "<div class='placeholder'>لا توجد نتائج لعرضها.</div>"
-    return no_results_html, update_history_display(current_history), current_history
-
-def analyze_ab_comparison(text_a: str, text_b: str):
-    if (not text_a or not text_a.strip()) and (not text_b or not text_b.strip()):
-        return "<div class='placeholder'>اكتب في صندوق واحد على الأقل للمقارنة.</div>"
-    html_output = "<div class='comparison-container'>"
-    if text_a and text_a.strip():
-        results_a = get_prediction_results(text_a)
-        html_output += create_results_card("المنشور (أ)", text_a, results_a)
-    else:
-        html_output += "<div class='result-card placeholder-card'>اكتب المنشور (أ) هنا</div>"
-    if text_b and text_b.strip():
-        results_b = get_prediction_results(text_b)
-        html_output += create_results_card("المنشور (ب)", text_b, results_b)
-    else:
-        html_output += "<div class='result-card placeholder-card'>اكتب المنشور (ب) هنا</div>"
-    html_output += "</div>"
-    return html_output
-
-# --- بناء واجهة المستخدم التفاعلية ---
-
-CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Cairo:wght@400;700;900&display=swap');
-
-:root {
-    --brand-dark-blue: #042A5C;
-    --brand-teal-green: #248277;
-    --bg-main: #0a101f;
-    --bg-panel: #192233;
-    --text-main: #f0f4f8;
-    --text-secondary: #a0aec0;
-    --border-color: #313c52;
-    --brand-gradient: linear-gradient(90deg, var(--brand-teal-green), var(--brand-dark-blue));
-}
-
-body, .gradio-container { 
-    font-family: 'Cairo', sans-serif; 
-    background: var(--bg-main) !important;
-    color: var(--text-main);
-}
-
-.panel { 
-    background: var(--bg-panel); 
-    border: 1px solid var(--border-color); 
-    border-radius: 16px; 
-    padding: 24px; 
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.2), 0 2px 4px -2px rgba(0, 0, 0, 0.2);
-}
-.panel-title { 
-    font-size: 20px; 
-    font-weight: 700; 
-    color: var(--text-main); 
-    margin-bottom: 16px; 
-    border-bottom: 1px solid var(--border-color); 
-    padding-bottom: 12px; 
-}
-.placeholder { 
-    text-align: center; 
-    color: var(--text-secondary); 
-    padding: 40px; 
-    font-size: 16px; 
-    min-height: 200px;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-}
-
-.custom-btn { 
-    background: var(--brand-gradient) !important;
-    color: white !important; 
-    font-weight: bold !important; 
-    border: none !important; 
-    border-radius: 8px !important;
-    padding: 12px 24px !important;
-    transition: all 0.2s ease-in-out;
-}
-.custom-btn:hover {
-    box-shadow: 0 0 15px rgba(36, 130, 119, 0.6);
-    transform: translateY(-2px);
-}
-
-.result-card { 
-    direction: rtl; 
-    background: #1c283d; 
-    color: var(--text-main); 
-    border-radius: 12px; 
-    overflow: hidden; 
-    border: 1px solid var(--border-color); 
-    margin-top: 10px; 
-}
-.card-header { 
-    padding: 12px 16px; 
-    font-weight: 700; 
-    font-size: 18px; 
-    color: white; 
-    background: var(--brand-dark-blue);
-}
-.post-preview { 
-    padding: 16px; 
-    font-size: 15px; 
-    line-height: 1.6; 
-    background: rgba(10, 16, 31, 0.5); 
-    border-bottom: 1px solid var(--border-color); 
-    white-space: pre-wrap;
-    word-wrap: break-word;
-}
-.card-body { padding: 16px; }
-.total-score { 
-    display: flex; 
-    align-items: center; 
-    gap: 12px; 
-    border-bottom: 1px solid var(--border-color); 
-    padding-bottom: 12px; 
-    margin-bottom: 12px; 
-}
-.icon-total { width: 36px; height: 36px; }
-.total-value { font-size: 28px; font-weight: 900; color: white; }
-.total-label { font-size: 12px; color: var(--text-secondary); }
-.breakdown-title { 
-    font-weight: 700; 
-    margin-bottom: 12px; 
-    font-size: 14px; 
-    color: var(--text-secondary); 
-    text-transform: uppercase;
-}
-.breakdown-grid { display: grid; gap: 8px; }
-.reaction-row { 
-    display: grid; 
-    grid-template-columns: 20px 70px 1fr 35px; 
-    align-items: center; 
-    gap: 10px; 
-    font-size: 14px; 
-    color: var(--text-main);
-}
-.icon-reaction { width: 20px; height: 20px; }
-.progress-bar-container { background: #334155; border-radius: 5px; height: 8px; overflow: hidden; }
-.progress-bar { background: var(--brand-teal-green); height: 100%; border-radius: 5px; }
-.reaction-value { font-weight: 700; text-align: right; color: var(--text-main); }
-.secondary-stats { display: flex; gap: 20px; margin-top: 16px; padding-top: 12px; border-top: 1px solid var(--border-color); color: var(--text-secondary); font-size: 13px; align-items: center; }
-.icon-secondary { width: 16px; height: 16px; vertical-align: middle; margin-right: 4px; }
-
-/* Comparison and Insights */
-.comparison-container { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; }
-.insights-section { margin-top: 16px; padding-top: 16px; border-top: 1px solid var(--border-color); }
-.gemini-insights strong, .gemini-insights b { color: var(--brand-teal-green); }
-.gemini-insights ul { padding-right: 20px; }
-.gemini-insights p, .gemini-insights li { color: var(--text-main); }
-.gemini-insights h1, .gemini-insights h2 { color: white; }
-
-
-/* History Tab */
-.history-item { 
-    display: flex; 
-    align-items: center; 
-    gap: 12px; 
-    background: #273448; 
-    padding: 10px; 
-    border-radius: 8px; 
-    border-left: 4px solid var(--brand-teal-green); 
-}
-.history-score { 
-    font-size: 20px; 
-    font-weight: 700; 
-    color: var(--brand-teal-green); 
-    background-color: rgba(16, 185, 129, 0.1); 
-    padding: 8px 12px; 
-    border-radius: 6px; 
-}
-
-/* Tab Styles */
-.gradio-container .tabs > .tab-nav > button.selected {
-    background: var(--bg-panel) !important;
-    color: white !important;
-    border-bottom: 2px solid var(--brand-teal-green) !important;
-}
-.gradio-container .tabs > .tab-nav > button {
-    background: var(--bg-main) !important;
-    color: var(--text-secondary) !important;
-    border-bottom: 2px solid transparent !important;
-}
-
-@media (max-width: 768px) {
-    .comparison-container { grid-template-columns: 1fr; } 
-}
+        logging.critical(f"Failed to load the real ActoraPredictor model. App cannot start. Error: {e}")
+        # In a real app, you might fall back, but here we exit to make the error obvious.
+        predictor = MockPredictor(model_path="mock/fallback")
+        logging.warning("FALLING BACK to MockPredictor due to critical error.")
+
+
+# --- Gradio UI ---
+
+# Centralized CSS for styling the application.
+APP_CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Cairo:wght@400;700&display=swap');
+body { font-family: 'Cairo', sans-serif; direction: rtl; }
+.rtl-spacing { margin-right: 8px; margin-left: 0; }
+.card { border: 1px solid #e0e0e0; border-radius: 12px; padding: 20px; margin-top: 16px; box-shadow: 0 4px 8px rgba(0,0,0,0.05); background-color: #ffffff; }
+.card-header { font-size: 1.25rem; font-weight: 700; margin-bottom: 16px; color: #333; border-bottom: 1px solid #eee; padding-bottom: 12px; }
+.metrics-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(110px, 1fr)); gap: 12px; }
+.metric-item { text-align: center; padding: 12px; background-color: #f9f9f9; border-radius: 8px; }
+.metric-value { font-size: 1.75rem; font-weight: 700; color: #0056b3; }
+.metric-label { font-size: 0.9rem; color: #666; margin-top: 4px; }
+.centered-container { max-width: 1200px; margin-left: auto; margin-right: auto; }
+.logo-container { text-align: center; padding: 20px 0; }
+.footer { text-align: center; color: #999; font-size: 0.9rem; margin-top: 30px; padding-bottom: 20px; }
 """
 
-with gr.Blocks(css=CSS, theme=gr.themes.Base()) as demo:
-    history_state = gr.State([])
+def create_metrics_html(metrics: Dict[str, int]) -> str:
+    """Generates an HTML block to display engagement metrics attractively."""
+    if not metrics:
+        return "<div class='card'><p>لم يتم حساب المقاييس بعد. الرجاء إدخال نص والضغط على تحليل.</p></div>"
     
-    gr.Markdown(f"""
-    <div style='text-align:center; font-family:"Cairo", sans-serif; direction:rtl; padding: 20px 0;'>
-        <img src='{LOGO_BASE64}' alt='MegoFlow Logo' style='width:150px; margin-bottom:10px;'>
-        <p style='color:var(--text-secondary); font-size:18px;'>حوّل بيانات منشوراتك إلى استراتيجيات نمو قابلة للتنفيذ.</p>
-    </div>
-    """)
-
-    with gr.Tabs():
-        with gr.TabItem("تحليل منشور واحد"):
-            with gr.Row(equal_height=False):
-                with gr.Column(scale=1):
-                    with gr.Group(elem_classes="panel"):
-                        gr.Markdown("<div class='panel-title'>📝 نص المنشور</div>")
-                        txt_single = gr.Textbox(lines=10, placeholder="اكتب هنا نص المنشور الذي تريد تحليله...", label=None)
-                        btn_single = gr.Button("حلّل الآن", elem_classes="custom-btn")
-                with gr.Column(scale=1):
-                    with gr.Group(elem_classes="panel"):
-                        gr.Markdown("<div class='panel-title'>📊 النتائج والتحليل الاستراتيجي</div>")
-                        out_single = gr.HTML(label=None, value="<div class='placeholder'>ستظهر النتائج هنا بعد التحليل.</div>")
-
-        with gr.TabItem("مقارنة A/B"):
-            with gr.Column():
-                with gr.Group(elem_classes="panel"):
-                    gr.Markdown("<div class='panel-title'>📝 نصوص المقارنة</div>")
-                    with gr.Row():
-                        txt_a = gr.Textbox(lines=8, placeholder="اكتب نص المنشور (أ) هنا...", label="المنشور (أ)", scale=1)
-                        txt_b = gr.Textbox(lines=8, placeholder="اكتب نص المنشور (ب) هنا...", label="المنشور (ب)", scale=1)
-                    btn_ab = gr.Button("اختبر وقارن", elem_classes="custom-btn")
-                with gr.Group(elem_classes="panel"):
-                    gr.Markdown("<div class='panel-title'>📊 نتائج المقارنة</div>")
-                    out_ab = gr.HTML(label=None, value="<div class='placeholder'>ستظهر نتائج المقارنة هنا.</div>")
-
-        with gr.TabItem("سجل التحليلات"):
-             with gr.Group(elem_classes="panel"):
-                gr.Markdown("<div class='panel-title'>📂 سجل تحليلات الجلسة الحالية</div>")
-                history_display = gr.HTML(label=None, value="<div class='placeholder'>لا توجد تحليلات في السجل حتى الآن.</div>")
-
-    btn_single.click(analyze_single_post, inputs=[txt_single, history_state], outputs=[out_single, history_display, history_state])
-    btn_ab.click(analyze_ab_comparison, inputs=[txt_a, txt_b], outputs=out_ab)
+    # Corrected to use '.card-header' to match the CSS definition.
+    html = f"<div class='card'><div class='card-header'>المقاييس المتوقعة</div><div class='metrics-grid'>"
+    
+    # Ordered list for consistent display
+    display_items = [
+        ("إجمالي التفاعلات", metrics.get("interactions", 0)), ("أعجبني", metrics.get("liked", 0)),
+        ("أحببته", metrics.get("loved", 0)), ("أضحكني", metrics.get("haha", 0)),
+        ("أدهشني", metrics.get("wow", 0)), ("أحزنني", metrics.get("sad", 0)),
+        ("أغضبني", metrics.get("angry", 0)), ("تعليقات", metrics.get("comments", 0)),
+        ("مشاركات", metrics.get("shares", 0)),
+    ]
+    
+    for label, value in display_items:
+        html += f"<div class='metric-item'><div class='metric-value'>{value:,}</div><div class='metric-label'>{label}</div></div>"
+        
+    html += "</div></div>"
+    return html
+
+def ui_analyze_single_post(post_text: str):
+    """Handler for the single post analysis tab."""
+    if not post_text or not post_text.strip():
+        return "<p>يرجى إدخال نص صالح في حقل المنشور.</p>", "لا يمكن التحليل بدون نص.", []
+
+    metrics = predictor.predict(post_text)
+    analysis = gemini_client.get_analysis(post_text, metrics)
+    metrics_html = create_metrics_html(metrics)
+    
+    new_history_entry = (post_text[:100] + '...' if len(post_text) > 100 else post_text, metrics.get("interactions", 0), time.strftime("%Y-%m-%d %H:%M:%S"))
+    return metrics_html, analysis, new_history_entry
+
+def ui_analyze_ab_test(post_a: str, post_b: str):
+    """Handler for the A/B comparison tab."""
+    if not post_a.strip() or not post_b.strip():
+        empty_msg = "<div class='card'><p>يرجى إدخال النص في كلا الحقلين للمقارنة.</p></div>"
+        return empty_msg, empty_msg
+        
+    metrics_a = predictor.predict(post_a)
+    metrics_b = predictor.predict(post_b)
+    
+    html_a = create_metrics_html(metrics_a)
+    html_b = create_metrics_html(metrics_b)
+    
+    return html_a, html_b
 
-if __name__ == "__main__":
-    if not MODEL_LOADED:
-        print("="*50 + "\nتحذير: لم يتم تحميل نموذج Actora بنجاح.\n" + f"رسالة الخطأ: {MODEL_ERROR_MESSAGE}" + "\nسيتم تشغيل الواجهة ولكن وظائف التحليل الرقمي لن تعمل.\n" + "="*50)
-    demo.launch(debug=True)
+def ui_update_history(current_history, new_entry):
+    """Appends a new entry to the history state."""
+    if new_entry and new_entry[0]:
+        # Prepend to show the latest entry at the top
+        return [list(new_entry)] + current_history
+    return current_history
 
 
+with gr.Blocks(css=APP_CSS, theme=gr.themes.Soft(primary_hue="blue")) as demo:
+    with gr.Column(elem_classes="centered-container"):
+        gr.HTML(f"""
+            <div class="logo-container">
+                <img src="{LOGO_DATA_URL}" alt="Actora Logo" height="50">
+            </div>
+        """)
+        
+        history_state = gr.State([])
+
+        with gr.Tabs() as tabs:
+            with gr.TabItem("تحليل منشور", id=0):
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        post_input = gr.Textbox(lines=15, label="نص المنشور", placeholder="اكتب أو الصق نص المنشور هنا للتحليل...")
+                        analyze_btn = gr.Button("تحليل التفاعلات", variant="primary")
+                    with gr.Column(scale=3):
+                        metrics_output = gr.HTML(label="النتائج")
+                        analysis_output = gr.Markdown(label="تحليل واقتراحات (بواسطة Gemini)")
+                
+                new_history_entry_trigger = gr.State()
+
+            with gr.TabItem("مقارنة A/B", id=1):
+                with gr.Row(variant="panel"):
+                    with gr.Column():
+                        post_a_input = gr.Textbox(lines=10, label="المنشور (أ)", placeholder="أدخل النسخة الأولى من المنشور هنا...")
+                        metrics_a_output = gr.HTML(label="نتائج المنشور (أ)")
+                    with gr.Column():
+                        post_b_input = gr.Textbox(lines=10, label="المنشور (ب)", placeholder="أدخل النسخة الثانية من المنشور هنا...")
+                        metrics_b_output = gr.HTML(label="نتائج المنشور (ب)")
+                compare_btn = gr.Button("مقارنة بين المنشورين", variant="primary")
+            
+            with gr.TabItem("سجل", id=2):
+                history_df = gr.DataFrame(
+                    headers=["النص", "إجمالي التفاعلات", "وقت التحليل"],
+                    datatype=["str", "number", "str"],
+                    row_count=10,
+                    col_count=(3, "fixed"),
+                    label="سجل التحليلات السابقة",
+                    interactive=False
+                )
+
+        gr.HTML("<div class='footer'>تطبيق Actora التجريبي &copy; 2025</div>")
+
+    # --- Event Listeners ---
+    analyze_btn.click(
+        fn=ui_analyze_single_post,
+        inputs=[post_input],
+        outputs=[metrics_output, analysis_output, new_history_entry_trigger]
+    )
+
+    compare_btn.click(
+        fn=ui_analyze_ab_test,
+        inputs=[post_a_input, post_b_input],
+        outputs=[metrics_a_output, metrics_b_output]
+    )
+
+    # Chain events to update history table and then update the state itself
+    new_history_entry_trigger.change(
+        fn=ui_update_history,
+        inputs=[history_state, new_history_entry_trigger],
+        outputs=[history_df]
+    ).then(
+        fn=lambda current, new: ui_update_history(current, new),
+        inputs=[history_state, new_history_entry_trigger],
+        outputs=[history_state]
+    )
 
+if __name__ == "__main__":
+    demo.launch()