multi.py

multi.py

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+# Imports
+import gradio as gr
+import wikipedia
+import numpy as np
+import faiss
+from langdetect import detect
+from gtts import gTTS
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import tempfile, os
+import torch
+import speech_recognition as sr
+from functools import lru_cache
+from pydub import AudioSegment
+
+# ===== Model Setup =====
+models = {}
+def load_models():
+    models['encoder'] = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
+    models['to_en'] = pipeline('translation', model='Helsinki-NLP/opus-mt-mul-en')
+    for lang in ['fr', 'ar', 'zh', 'es']:
+        models[f'en_to_{lang}'] = pipeline('translation_en_to_' + lang, model=f'Helsinki-NLP/opus-mt-en-{lang}')
+    models['answer_gen'] = pipeline('text2text-generation', model='google/flan-t5-base', max_length=1024)  # increased length
+
+load_models()
+
+# ===== Utility Functions =====
+def detect_language(text):
+    try:
+        return detect(text)
+    except:
+        return 'en'
+
+def translate(text, src, tgt):
+    if src == tgt:
+        return text
+    if src != 'en':
+        text = models['to_en'](text)[0]['translation_text']
+    if f'en_to_{tgt}' in models:
+        return models[f'en_to_{tgt}'](text)[0]['translation_text']
+    return text
+
+def tts_play(text, lang):
+    tts = gTTS(text=text, lang=lang)
+    path = tempfile.mktemp(suffix=".mp3")
+    tts.save(path)
+    return path
+
+def chunk_text(text, max_words=100):  # increased chunk size
+    sentences = text.split('. ')
+    chunks, current_chunk, current_len = [], [], 0
+    for sent in sentences:
+        words = sent.split()
+        if current_len + len(words) > max_words:
+            chunks.append('. '.join(current_chunk))
+            current_chunk = [sent]
+            current_len = len(words)
+        else:
+            current_chunk.append(sent)
+            current_len += len(words)
+    if current_chunk:
+        chunks.append('. '.join(current_chunk))
+    return chunks
+
+def build_faiss_index(chunks, model):
+    embeddings = model.encode(chunks, convert_to_numpy=True)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    return index
+
+@lru_cache(maxsize=20)
+def prepare_faiss_for_topic(topic):
+    wikipedia.set_lang('en')
+    page = wikipedia.page(topic)
+    content = page.content[:5000]  # increase content for better answers
+    chunks = chunk_text(content)
+    index = build_faiss_index(chunks, models['encoder'])
+    return chunks, index
+
+def retrieve_context(question, index, chunks, model, top_k=5):  # increased top_k
+    q_emb = model.encode([question], convert_to_numpy=True)
+    _, indices = index.search(q_emb, top_k)
+    return ' '.join([chunks[i] for i in indices[0]])
+
+# ===== Main Inference Function =====
+def qa_system(audio, text_question, topic, output_lang):
+    question = ""
+    if audio is not None:
+        try:
+            r = sr.Recognizer()
+            audio_wav_path = tempfile.mktemp(suffix=".wav")
+            sound = AudioSegment.from_file(audio)
+            sound.export(audio_wav_path, format="wav")
+            with sr.AudioFile(audio_wav_path) as source:
+                audio_data = r.record(source)
+                question = r.recognize_google(audio_data)
+        except Exception as e:
+            return f"❌ Could not understand the audio: {e}", None, None
+    elif text_question:
+        question = text_question.strip()
+    else:
+        return "❌ Please provide a voice or text question.", None, None
+
+    input_lang = detect_language(question)
+
+    try:
+        chunks, faiss_index = prepare_faiss_for_topic(topic)
+    except:
+        return "Error loading topic from Wikipedia", None, None
+
+    context = retrieve_context(question, faiss_index, chunks, models['encoder'], top_k=5)
+    question_en = translate(question, input_lang, 'en')
+    prompt = f"Answer based on the context:\nContext: {context}\nQuestion: {question_en}"
+    answer_en = models['answer_gen'](prompt)[0]['generated_text']
+
+    if output_lang == 'en':
+        answer = answer_en
+    elif output_lang == 'am':
+        answer = "Amharic translation not supported."
+    else:
+        answer = translate(answer_en, 'en', output_lang)
+
+    audio_path = tts_play(answer, output_lang)
+    return f"You asked: {question}\n\nAnswer: {answer}", audio_path, answer
+
+# ===== Gradio UI =====
+lang_options = ['en', 'am', 'fr', 'ar', 'es', 'zh']
+
+demo = gr.Interface(
+    fn=qa_system,
+    inputs=[
+        gr.Audio(type="filepath", label="🎤 Ask your Question by Voice (optional)"),
+        gr.Textbox(label="✍️ Or type your Question here (optional)"),
+        gr.Textbox(value="Artificial intelligence", label="📚 Wikipedia Topic"),
+        gr.Dropdown(choices=lang_options, value='en', label="🌍 Output Language")
+    ],
+    outputs=[
+        gr.Textbox(label="🤖 Answer Output"),
+        gr.Audio(label="🔊 Answer Playback"),
+        gr.Textbox(label="📝 Translated Answer Text")
+    ],
+    title="🌍 Multilingual Voice/Text Q&A Assistant",
+    description="""
+    <h3 style='text-align: center; font-weight: bold; font-style: italic;'>👋 Welcome to the Multilingual Wikipedia Q&A Assistant</h3>
+    <p style='text-align: center;'>You can ask questions using voice or text in different languages, and get spoken and translated answers using AI + Wikipedia. 🌐</p>
+    """
+)
+
+# Launch the app
+demo.launch()