Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+from moviepy.video.io.VideoFileClip import VideoFileClip
+import librosa
+import soundfile as sf
+import numpy as np
+import re
+import os
+
+# ======================
+# 1. الإعدادات الأساسية
+# ======================
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ======================
+# 2. تحميل النماذج
+# ======================
+pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium", device=0 if device=="cuda" else -1)
+bart_model = AutoModelForSeq2SeqLM.from_pretrained("ahmedabdo/arabic-summarizer-bart")
+bart_tokenizer = AutoTokenizer.from_pretrained("ahmedabdo/arabic-summarizer-bart")
+
+# ======================
+# 3. الدوال المساعدة
+# ======================
+def clean_text(text):
+    return re.sub(r'\s+', ' ', text).strip()
+
+def convert_audio_to_text(uploaded_file):
+    try:
+        if not uploaded_file:
+            return "⛔ الرجاء رفع ملف أولاً"
+
+        input_path = uploaded_file if isinstance(uploaded_file, str) else uploaded_file.name
+        output_path = "/tmp/processed.wav"
+
+        # معالجة ملفات الفيديو
+        if input_path.split('.')[-1].lower() in ['mp4', 'avi', 'mov', 'mkv']:
+            video = VideoFileClip(input_path)
+            if video.audio:
+                video.audio.write_audiofile(output_path, codec='pcm_s16le')
+            else:
+                return "⛔ لا يوجد صوت في الفيديو!"
+        else:
+            output_path = input_path
+
+        audio, rate = librosa.load(output_path, sr=16000)
+        transcripts = []
+        # تقسيم الصوت إلى مقاطع للتعامل مع الملفات الكبيرة
+        for start in np.arange(0, len(audio)/rate, 30):
+            end = min(start + 30, len(audio)/rate)
+            segment = audio[int(start*rate):int(end*rate)]
+            sf.write(f"/tmp/segment_{int(start)}.wav", segment, rate)
+            transcripts.append(pipe(f"/tmp/segment_{int(start)}.wav")["text"])
+        return " ".join(transcripts)
+    except Exception as e:
+        return f"⛔ خطأ: {str(e)}"
+
+def summarize_text(text):
+    cleaned_text = clean_text(text)
+    inputs = bart_tokenizer(cleaned_text, return_tensors="pt", max_length=1024, truncation=True).to(device)
+    summary_ids = bart_model.generate(
+        inputs.input_ids,
+        max_length=150,
+        num_beams=4,
+        early_stopping=True
+    )
+    return bart_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# ======================
+# 4. المثال التجريبي
+# ======================
+EXAMPLE_AUDIO_PATH = "_⁨ما_هو_الذكاء_الإصطناعي_؟_فِهموجرافيك_١⁩.m4a"
+
+def process_example_audio():
+    if not os.path.exists(EXAMPLE_AUDIO_PATH):
+        return "⛔ الملف التجريبي غير موجود"
+    return convert_audio_to_text(EXAMPLE_AUDIO_PATH)
+
+# ======================
+# 5. واجهة المستخدم
+# ======================
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎤 استخراج النص وتلخيصه")
+    
+    gr.Markdown("### 🔊 مثال تجريبي")
+    gr.Audio(EXAMPLE_AUDIO_PATH)
+    example_btn = gr.Button("تجربة المثال ⚡", elem_classes="custom-button")
+    
+    file_input = gr.File(file_types=[".wav", ".mp3", ".mp4"])
+    extract_btn = gr.Button("استخراج النص", elem_classes="custom-button")
+    extracted_text = gr.Textbox(label="📝 النص المستخرج", lines=8)
+    summarize_btn = gr.Button("تلخيص النص", elem_classes="custom-button")
+    summary_output = gr.Textbox(label="📌 الملخص", lines=6)
+    
+    extract_btn.click(convert_audio_to_text, inputs=file_input, outputs=extracted_text)
+    summarize_btn.click(summarize_text, inputs=extracted_text, outputs=summary_output)
+    example_btn.click(process_example_audio, outputs=extracted_text)
+
+if __name__ == "__main__":
+    demo.launch()