bayan-ai / main.py
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from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import torch
from transformers import pipeline
import json
import os
from difflib import SequenceMatcher
from typing import Dict, Any, Optional
import tempfile
import subprocess
import shutil
app = FastAPI(
title="Bayan AI بيان",
description="",
version="1.0.0"
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Allow all origins for local development
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# CPU only on free tier
device = -1
# Load Whisper pipeline (model downloads/caches automatically on first run)
pipe = pipeline(
"automatic-speech-recognition",
model="tarteel-ai/whisper-tiny-ar-quran",
device=device,
)
# Standard Surah names (1–114)
surah_names = {
1: "Al-Fatiha (الفاتحة)",
2: "Al-Baqarah (البقرة)",
3: "Aal-E-Imran (آل عمران)",
4: "An-Nisa (النساء)",
5: "Al-Maidah (المائدة)",
6: "Al-An'am (الأنعام)",
7: "Al-A'raf (الأعراف)",
8: "Al-Anfal (الأنفال)",
9: "At-Tawbah (التوبة)",
10: "Yunus (يونس)",
11: "Hud (هود)",
12: "Yusuf (يوسف)",
13: "Ar-Ra'd (الرعد)",
14: "Ibrahim (إبراهيم)",
15: "Al-Hijr (الحجر)",
16: "An-Nahl (النحل)",
17: "Al-Isra (الإسراء)",
18: "Al-Kahf (الكهف)",
19: "Maryam (مريم)",
20: "Ta-Ha (طه)",
21: "Al-Anbiya (الأنبياء)",
22: "Al-Hajj (الحج)",
23: "Al-Mu'minun (المؤمنون)",
24: "An-Nur (النور)",
25: "Al-Furqan (الفرقان)",
26: "Ash-Shu'ara (الشعراء)",
27: "An-Naml (النمل)",
28: "Al-Qasas (القصص)",
29: "Al-Ankabut (العنكبوت)",
30: "Ar-Rum (الروم)",
31: "Luqman (لقمان)",
32: "As-Sajdah (السجدة)",
33: "Al-Ahzab (الأحزاب)",
34: "Saba (سبأ)",
35: "Fatir (فاطر)",
36: "Ya-Sin (يس)",
37: "As-Saffat (الصافات)",
38: "Sad (ص)",
39: "Az-Zumar (الزمر)",
40: "Ghafir (غافر)",
41: "Fussilat (فصلت)",
42: "Ash-Shura (الشورى)",
43: "Az-Zukhruf (الزخرف)",
44: "Ad-Dukhkhan (الدخان)",
45: "Al-Jathiya (الجاثية)",
46: "Al-Ahqaf (الأحقاف)",
47: "Muhammad (محمد)",
48: "Al-Fath (الفتح)",
49: "Al-Hujurat (الحجرات)",
50: "Qaf (ق)",
51: "Adh-Dhariyat (الذاريات)",
52: "At-Tur (الطور)",
53: "An-Najm (النجم)",
54: "Al-Qamar (القمر)",
55: "Ar-Rahman (الرحمن)",
56: "Al-Waqi'ah (الواقعة)",
57: "Al-Hadid (الحديد)",
58: "Al-Mujadila (المجادلة)",
59: "Al-Hashr (الحشر)",
60: "Al-Mumtahina (الممتحنة)",
61: "As-Saff (الصف)",
62: "Al-Jumu'ah (الجمعة)",
63: "Al-Munafiqoon (المنافقون)",
64: "At-Taghabun (التغابن)",
65: "At-Talaq (الطلاق)",
66: "At-Tahrim (التحريم)",
67: "Al-Mulk (الملك)",
68: "Al-Qalam (القلم)",
69: "Al-Haqqah (الحاقة)",
70: "Al-Ma'arij (المعارج)",
71: "Nooh (نوح)",
72: "Al-Jinn (الجن)",
73: "Al-Muzzammil (المزمل)",
74: "Al-Muddathir (المدثر)",
75: "Al-Qiyamah (القيامة)",
76: "Al-Insan (الإنسان)",
77: "Al-Mursalat (المرسلات)",
78: "An-Naba (النبأ)",
79: "An-Nazi'at (النازعات)",
80: "Abasa (عبس)",
81: "At-Takwir (التكوير)",
82: "Al-Infitar (الإنفطار)",
83: "Al-Mutaffifin (المطففين)",
84: "Al-Inshiqaq (الإنشقاق)",
85: "Al-Buruj (البروج)",
86: "At-Tariq (الطارق)",
87: "Al-A'la (الأعلى)",
88: "Al-Ghashiyah (الغاشية)",
89: "Al-Fajr (الفجر)",
90: "Al-Balad (البلد)",
91: "Ash-Shams (الشمس)",
92: "Al-Lail (الليل)",
93: "Ad-Duha (الضحى)",
94: "Ash-Sharh (الشرح)",
95: "At-Tin (التين)",
96: "Al-Alaq (العلق)",
97: "Al-Qadr (القدر)",
98: "Al-Bayyina (البينة)",
99: "Az-Zalzalah (الزلزلة)",
100: "Al-Adiyat (العاديات)",
101: "Al-Qari'ah (القارعة)",
102: "At-Takathur (التكاثر)",
103: "Al-Asr (العصر)",
104: "Al-Humazah (الهمزة)",
105: "Al-Fil (الفيل)",
106: "Quraish (قريش)",
107: "Al-Ma'un (الماعون)",
108: "Al-Kawthar (الكوثر)",
109: "Al-Kafirun (الكافرون)",
110: "An-Nasr (النصر)",
111: "Al-Masad (المسد)",
112: "Al-Ikhlas (الإخلاص)",
113: "Al-Falaq (الفلق)",
114: "An-Nas (الناس)",
}
# Phrases to ignore (e.g., common introductions)
PHRASES_TO_IGNORE = [
"بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ",
"أعوذ بالله من الشيطان الرجيم",
"صدق الله العظيم",
]
import re
def normalize_text(text: str) -> str:
"""Robust normalization for Arabic text."""
text = re.sub(r"[إأآاٱ]", "ا", text)
text = re.sub(r"ى", "ي", text)
text = re.sub(r"ؤ", "ء", text)
text = re.sub(r"ئ", "ء", text)
text = re.sub(r"g", "ة", text)
text = re.sub(r"ة", "ه", text)
text = re.sub(r"[\u064B-\u065F\u0670]", "", text) # Tashkeel
text = re.sub(r"[\u06D6-\u06ED]", "", text)
text = re.sub(r"ء", "", text) # Remove Hamza to handle varying forms
return " ".join(text.strip().split())
# Pre-load all verses at startup
all_verses = []
surahs_dir = "surahs_json_files"
if not os.path.isdir(surahs_dir):
raise FileNotFoundError("Missing 'surahs_json_files/' folder.")
for filename in sorted(os.listdir(surahs_dir)):
if filename.endswith(".json"):
try:
surah_number = int(filename.split("_")[0])
except:
continue
surah_name = surah_names.get(surah_number, f"Surah {surah_number}")
file_path = os.path.join(surahs_dir, filename)
with open(file_path, "r", encoding="utf-8") as f:
data = json.load(f)
verses = [ayah["text"] for ayah in data.get("ayahs", []) if "text" in ayah]
for ayah_number, verse_text in enumerate(verses, start=1):
verse_norm = normalize_text(verse_text)
all_verses.append({
"surah_number": surah_number,
"surah_name": surah_name,
"ayah_number": ayah_number,
"verse_text": verse_text,
"verse_norm": verse_norm
})
print(f"Loaded {len(all_verses)} verses from {len(os.listdir(surahs_dir))} surahs.")
def find_best_verse(transcription: str) -> Dict[str, Any]:
transcription_norm = normalize_text(transcription)
# Remove phrases to ignore (Bismillah, A'udhu billah)
for phrase in PHRASES_TO_IGNORE:
phrase_norm = normalize_text(phrase)
if phrase_norm in transcription_norm:
# Replace and clean up extra spaces
transcription_norm = transcription_norm.replace(phrase_norm, "").strip()
transcription_norm = " ".join(transcription_norm.split())
if not transcription_norm:
return {"error": "Empty transcription"}
candidates = []
# Pre-compile regex for whole word check
pattern_str = r'(?:^|\s)' + re.escape(transcription_norm) + r'(?:\s|$)'
whole_word_regex = re.compile(pattern_str)
for verse in all_verses:
verse_norm = verse["verse_norm"]
is_whole_word = False
containment = 0.0
ratio = 0.0
# Fast substring check
if transcription_norm in verse_norm:
containment = 1.0
matcher = SequenceMatcher(None, transcription_norm, verse_norm)
ratio = matcher.ratio()
# Check for whole word match
if whole_word_regex.search(verse_norm):
is_whole_word = True
else:
matcher = SequenceMatcher(None, transcription_norm, verse_norm)
match = matcher.find_longest_match(0, len(transcription_norm), 0, len(verse_norm))
containment = match.size / len(transcription_norm) if len(transcription_norm) > 0 else 0
ratio = matcher.ratio()
candidates.append({
"verse": verse,
"containment": containment,
"ratio": ratio,
"is_whole_word": is_whole_word
})
# Sort by whole_word (desc), containment (desc), ratio (desc)
candidates.sort(key=lambda x: (x["is_whole_word"], x["containment"], x["ratio"]), reverse=True)
# If we have whole word matches, ignore partial matches
if candidates and candidates[0]["is_whole_word"]:
candidates = [c for c in candidates if c["is_whole_word"]]
# Filter strong matches (>= 80% containment)
strong_matches = [c for c in candidates if c["containment"] >= 0.8]
def format_match(candidate):
verse_data = candidate["verse"]
return {
"surah_number": verse_data["surah_number"],
"surah_name": verse_data["surah_name"],
"ayah_number": verse_data["ayah_number"],
"verse_text": verse_data["verse_text"],
"similarity_score": round(candidate["containment"], 4)
}
if not strong_matches:
# No strong matches found
if candidates:
top_match = candidates[0]
return {
"error": "No confident match found",
"best_similarity": round(top_match["containment"], 4),
"possible_match": format_match(top_match)
}
else:
return {"error": "No matches found"}
if len(strong_matches) > 1:
# Multiple strong matches -> return top 5
top_5 = strong_matches[:5]
return {
"matches": [format_match(m) for m in top_5]
}
else:
# Single dominant match
return format_match(strong_matches[0])
@app.get("/")
def root():
return {"message": "Bayan AI بيان... LIVE!"}
@app.post("/recognize")
async def recognize(file: UploadFile = File(...)):
# Allow both audio and video
is_video = file.content_type and file.content_type.startswith("video/")
is_audio = file.content_type and file.content_type.startswith("audio/")
if not is_audio and not is_video:
raise HTTPException(status_code=400, detail="File must be an audio or video file")
# Save to temp file
contents = await file.read()
file_extension = os.path.splitext(file.filename)[1] or (".mp4" if is_video else ".wav")
with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as tmp:
tmp.write(contents)
input_path = tmp.name
audio_path = input_path
temp_audio_path = None
try:
if is_video:
# Check if ffmpeg is installed
if not shutil.which("ffmpeg"):
raise HTTPException(status_code=500, detail="ffmpeg not found on server")
temp_audio_path = input_path + "_converted.wav"
# Extract audio quickly and silently
# -vn: no video, -acodec pcm_s16le: wav format, -ar 16000: whisper preferred sample rate
# -y: overwrite, -loglevel error: be silent
cmd = [
"ffmpeg", "-y", "-i", input_path,
"-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1",
"-loglevel", "error",
temp_audio_path
]
subprocess.run(cmd, check=True)
audio_path = temp_audio_path
transcription = pipe(audio_path)["text"]
except subprocess.CalledProcessError as e:
raise HTTPException(status_code=500, detail=f"Video conversion error: {str(e)}")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Transcription error: {str(e)}")
finally:
# Clean up all temp files
if os.path.exists(input_path):
os.unlink(input_path)
if temp_audio_path and os.path.exists(temp_audio_path):
os.unlink(temp_audio_path)
result = find_best_verse(transcription)
result["transcription"] = transcription
return JSONResponse(content=result)