Update app.py

app.py

@@ -1,198 +1,24 @@
-import os
-import re
-from camel_tools.tokenizers.word import simple_word_tokenize
-from camel_tools.ner import NERecognizer
-import nltk
-import torch
-from collections import Counter
-from transformers import pipeline, AutoModel, AutoTokenizer
-import PyPDF2
 import gradio as gr
-import openai
-
-# تعيين التوكن الخاص بـ OpenAI
-openai.api_key = "sk-proj-62TDbO5KABSdkZaFPPD4T3BlbkFJkhqOYpHhL6OucTzNdWSU"
-
-# تحميل وتفعيل الأدوات المطلوبة
-nltk.download('punkt')
-
-# التحقق من توفر GPU واستخدامه
-device = 0 if torch.cuda.is_available() else -1
-
-# تحميل نماذج التحليل اللغوي
-analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english", device=device)
-
-# تحميل نماذج BERT، GPT2، ELECTRA، و AraBERT
-arabic_bert_tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-base-arabic")
-arabic_bert_model = AutoModel.from_pretrained("asafaya/bert-base-arabic")
-
-arabic_gpt2_tokenizer = AutoTokenizer.from_pretrained("aubmindlab/aragpt2-base")
-arabic_gpt2_model = AutoModel.from_pretrained("aubmindlab/aragpt2-base")
-
-arabic_electra_tokenizer = AutoTokenizer.from_pretrained("aubmindlab/araelectra-base-discriminator")
-arabic_electra_model = AutoModel.from_pretrained("aubmindlab/araelectra-base-discriminator")
-
-arabert_tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-base-arabertv02")
-arabert_model = AutoModel.from_pretrained("aubmindlab/bert-base-arabertv02")
-
-# دالة لتحليل النص باستخدام camel_tools
-def camel_ner_analysis(text):
-    ner = NERecognizer.pretrained()
-    tokens = simple_word_tokenize(text)
-    entities = ner.predict(tokens)
-    entity_dict = {"PERSON": [], "LOC": [], "ORG": [], "DATE": []}
-    for token, tag in zip(tokens, entities):
-        if tag in entity_dict:
-            entity_dict[tag].append((token, tag))
-    return entity_dict
-
-# دالة لتحليل المشاعر
-def analyze_sentiments(text):
-    sentiments = analyzer(text)
-    return sentiments
-
-# دالة لتجزئة النص إلى جمل
-def nltk_extract_sentences(text):
-    sentences = nltk.tokenize.sent_tokenize(text, language='arabic')
-    return sentences
-
-# دالة لاستخراج الاقتباسات من النص
-def nltk_extract_quotes(text):
-    quotes = []
-    sentences = nltk.tokenize.sent_tokenize(text, language='arabic')
-    for sentence in sentences:
-        if '"' in sentence or '«' in sentence or '»' in sentence:
-            quotes.append(sentence)
-    return quotes
-
-# دالة لعد الرموز في النص
-def count_tokens(text):
-    tokens = simple_word_tokenize(text)
-    return len(tokens)
-
-# دالة لاستخراج النص من ملفات PDF
-def extract_pdf_text(file_path):
-    with open(file_path, "rb") as pdf_file:
-        pdf_reader = PyPDF2.PdfReader(pdf_file)
-        text = ""
-        for page_num in range(len(pdf_reader.pages)):
-            page = pdf_reader.pages[page_num]
-            text += page.extract_text()
-    return text
-
-# دالة لاستخراج المشاهد من النص
-def extract_scenes(text):
-    scenes = re.split(r'داخلي|خارجي', text)
-    scenes = [scene.strip() for scene in scenes if scene.strip()]
-    return scenes
-
-# دالة لاستخراج تفاصيل المشهد (المكان والوقت)
-def extract_scene_details(scene):
-    details = {}
-    location_match = re.search(r'(داخلي|خارجي)', scene)
-    time_match = re.search(r'(ليلاً|نهاراً|شروق|غروب)', scene)
-    
-    if location_match:
-        details['location'] = location_match.group()
-    if time_match:
-        details['time'] = time_match.group()
-    
-    return details
-
-# دالة لاستخراج أعمار الشخصيات
-def extract_ages(text):
-    ages = re.findall(r'\b(\d{1,2})\s*(?:عام|سنة|سنوات)\s*(?:من العمر|عمره|عمرها)', text)
-    return ages
-
-# دالة لاستخراج وصف الشخصيات
-def extract_character_descriptions(text):
-    descriptions = re.findall(r'شخصية\s*(.*?)\s*:\s*وصف\s*(.*?)\s*(?:\.|،)', text, re.DOTALL)
-    return descriptions
-
-# دالة لاستخراج تكرار الشخصيات
-def extract_character_frequency(entities):
-    persons = [ent[0] for ent in entities['PERSON']]
-    frequency = Counter(persons)
-    return frequency
-
-# دالة لاستخراج الحوارات وتحديد المتحدثين
-def extract_dialogues(text):
-    dialogues = re.findall(r'(.*?)(?:\s*:\s*)(.*?)(?=\n|$)', text, re.DOTALL)
-    return dialogues
-
-# دالة لتحليل النصوص واستخراج المعلومات وحفظ النتائج
-def analyze_and_complete(file_paths):
-    results = []
-    output_directory = os.getenv("SPACE_DIR", "/app/output")
-    
-    for file_path in file_paths:
-        if file_path.endswith(".pdf"):
-            text = extract_pdf_text(file_path)
-        else:
-            with open(file_path, "r", encoding="utf-8") as file:
-                text = file.read()
-        
-        filename_prefix = os.path.splitext(os.path.basename(file_path))[0]
-        
-        camel_entities = camel_ner_analysis(text)
-        sentiments = analyze_sentiments(text)
-        sentences = nltk_extract_sentences(text)
-        quotes = nltk_extract_quotes(text)
-        token_count = count_tokens(text)
-        scenes = extract_scenes(text)
-        ages = extract_ages(text)
-        character_descriptions = extract_character_descriptions(text)
-        character_frequency = extract_character_frequency(camel_entities)
-        dialogues = extract_dialogues(text)
-        
-        scene_details = [extract_scene_details(scene) for scene in scenes]
-        
-        # حفظ النتائج إلى ملفات
-        with open(os.path.join(output_directory, f"{filename_prefix}_entities.txt"), "w", encoding="utf-8") as file:
-            file.write(str(camel_entities))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_sentiments.txt"), "w", encoding="utf-8") as file:
-            file.write(str(sentiments))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_sentences.txt"), "w", encoding="utf-8") as file:
-            file.write("\n".join(sentences))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_quotes.txt"), "w", encoding="utf-8") as file:
-            file.write("\n".join(quotes))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_token_count.txt"), "w", encoding="utf-8") as file:
-            file.write(str(token_count))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_scenes.txt"), "w", encoding="utf-8") as file:
-            file.write("\n".join(scenes))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_scene_details.txt"), "w", encoding="utf-8") as file:
-            file.write(str(scene_details))
-        
-        with open(os.path.join(output_directory, f"{filename_prefix}_ages.txt"), "w", encoding="utf-8") as file:
-            file.write(str(ages))
-
-        with open(os.path.join(output_directory, f"{filename_prefix}_character_descriptions.txt"), "w", encoding="utf-8") as file:
-            file.write(str(character_descriptions))
-        
-        with open(os.path.join(output_directory, f"{filename_prefix}_character_frequency.txt"), "w", encoding="utf-8") as file:
-            file.write(str(character_frequency))
-        
-        with open(os.path.join(output_directory, f"{filename_prefix}_dialogues.txt"), "w", encoding="utf-8") as file:
-            file.write(str(dialogues))
-        
-        results.append((str(camel_entities), str(sentiments), "\n".join(sentences), "\n".join(quotes), str(token_count), "\n".join(scenes), str(scene_details), str(ages), str(character_descriptions), str(character_frequency), str(dialogues)))
-    
-    return results
-
-## تعريف واجهة Gradio
-interface = gr.Interface(
-    fn=analyze_and_complete,
-    inputs=gr.File(file_count="multiple", type="filepath"),
-    outputs=gr.JSON(),
-    title="Movie Script Analyzer and Completer",
-    description="Upload text, PDF, or DOCX files to analyze and complete the movie script."
+import subprocess
+
+def run_scripts():
+    outputs = []
+    scripts = ["firstkha.py", "alf.py"]
+    for script in scripts:
+        try:
+            result = subprocess.run(["python", script], check=True, text=True, capture_output=True)
+            outputs.append(f"Output of {script}:\n{result.stdout}")
+        except subprocess.CalledProcessError as e:
+            outputs.append(f"Error running {script}:\n{e.stderr}")
+    return "\n\n".join(outputs)
+
+iface = gr.Interface(
+    fn=run_scripts,
+    inputs=[],
+    outputs="text",
+    live=True,
+    description="Run the scripts firstkha.py and alf.py"
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    iface.launch()