Update app.py

app.py

@@ -1,3 +1,5 @@
+# app.py
+
 import gradio as gr
 import pandas as pd
 import uuid
@@ -6,72 +8,39 @@ import re
 import asyncio
 import edge_tts
 from sentence_transformers import SentenceTransformer, util
-import os
-import tempfile
 
-# Load Bhagavad Gita dataset
+# Load Gita dataset
 df = pd.read_csv("bhagavad_gita.csv")
 
-# Load sentence transformer and precompute embeddings
+# Load model
 model = SentenceTransformer("all-MiniLM-L6-v2")
 verse_embeddings = model.encode(df['meaning_in_english'].tolist(), convert_to_tensor=True)
 
-# Temp folder for audio files
-TEMP_DIR = tempfile.mkdtemp()
-
-# Background music file path
 bg_music_path = "krishna_bg_music.mp3"
 
 def shorten_explanation(text, max_sentences=2):
-    sentences = text.split('. ')
-    shortened = '. '.join(sentences[:max_sentences]).strip()
-    if not shortened.endswith('.'):
-        shortened += '.'
-    return shortened
+    return '. '.join(text.split('. ')[:max_sentences]).strip() + "."
 
 def clean_english(text):
-    text = re.sub(r'[^\x00-\x7F]+', ' ', text)
-    return ' '.join(text.split())
-
-async def tts_generate_async(text, voice="en-IN-PrabhatNeural"):
-    """Generate audio asynchronously using edge-tts with Krishna male voice."""
-    filename = os.path.join(TEMP_DIR, f"voice_{uuid.uuid4()}.mp3")
-    try:
-        communicate = edge_tts.Communicate(text, voice=voice)
-        await communicate.save(filename)
-        if os.path.exists(filename) and os.path.getsize(filename) > 1000:
-            return filename
-    except Exception as e:
-        print(f"TTS Error: {e}")
-    return None
-
-def generate_tts(text):
-    """Synchronous wrapper for async TTS generation."""
-    if not text.strip():
-        return None
-    try:
-        return asyncio.run(tts_generate_async(clean_english(text)))
-    except Exception as e:
-        print(f"TTS Generation failed: {e}")
-        return None
+    return re.sub(r'[^\x00-\x7F]+', ' ', text)
+
+# Async wrapper for TTS
+async def generate_voice_async(text):
+    voice = "en-IN-PrabhatNeural"
+    filename = f"voice_{uuid.uuid4()}.mp3"
+    communicate = edge_tts.Communicate(text, voice=voice)
+    await communicate.save(filename)
+    return filename
+
+def generate_voice(text):
+    return asyncio.run(generate_voice_async(text))
 
 def get_unique_bgm():
-    """Return unique copy of background music (optional)."""
-    if not os.path.exists(bg_music_path):
-        return None
-    unique_bgm_path = os.path.join(TEMP_DIR, f"bgm_{uuid.uuid4()}.mp3")
-    try:
-        shutil.copy(bg_music_path, unique_bgm_path)
-        return unique_bgm_path
-    except Exception as e:
-        print(f"BGM Error: {e}")
-        return None
+    unique_path = f"bgm_{uuid.uuid4()}.mp3"
+    shutil.copy(bg_music_path, unique_path)
+    return unique_path
 
 def versewise_bot(question, play_music):
-    """Main chatbot logic: find the most similar verse and return the answer."""
-    if not question.strip():
-        return "Please ask a valid question.", None, None
-
     query_embedding = model.encode(question, convert_to_tensor=True)
     similarity_scores = util.pytorch_cos_sim(query_embedding, verse_embeddings)[0]
     idx = similarity_scores.argmax().item()
@@ -84,7 +53,7 @@ def versewise_bot(question, play_music):
 
     reply = f"""📖 Bhagavad Gita {verse_number}
 
-🕉 "[translate:{sanskrit[:60]}...]"
+🕉 "{sanskrit[:60]}..."
 
 "{translation}"
 
@@ -92,23 +61,21 @@ def versewise_bot(question, play_music):
 
 🌼 Stay strong — Krishna walks with you."""
 
-    voice_text = f"{translation}. {explanation}"
-    audio_path = generate_tts(voice_text)
-    music_path = get_unique_bgm() if play_music else None
+    voice_text = f"{clean_english(translation)}. {clean_english(explanation)}"
+    audio_path = generate_voice(voice_text)
+    music = get_unique_bgm() if play_music else None
 
-    return reply, audio_path, music_path
+    return reply, audio_path, music
 
 def get_quote_of_the_day():
     verse = df.sample(1).iloc[0]
     sanskrit = verse['verse_in_sanskrit']
     translation = verse['translation_in_english']
     verse_number = verse['verse_number']
-    return f"""<div style="font-size:1.1em;padding:10px 0;">
-    <b>Quote of the Day (Gita {verse_number}):</b><br>
-    <i>[translate:{sanskrit[:60]}...]</i><br>
+    return f"""<div style="font-size:1.1em;padding:10px 0;"><b>Quote of the Day (Gita {verse_number}):</b><br>
+    <i>{sanskrit[:60]}...</i><br>
     <span style="color:#2d2d2d;">"{translation}"</span></div>"""
 
-# Custom UI theme styling
 custom_css = """
 body, .gradio-container, .gradio-interface, html {
   background-image: url('https://static.vecteezy.com/system/resources/previews/022/592/272/large_2x/image-of-divine-beautiful-closed-eyes-blue-colored-krishna-generative-ai-free-photo.jpeg') !important;
@@ -125,9 +92,17 @@ body, .gradio-container, .gradio-interface, html {
   box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
   margin: auto;
 }
+@media only screen and (max-width: 768px) {
+  .gradio-container {
+    width: 95% !important;
+    padding: 15px !important;
+  }
+}
+input[type="text"], textarea {
+  font-size: 16px !important;
+}
 """
 
-# Gradio interface without voice toggle; Krishna's male voice fixed
 interface = gr.Interface(
     fn=versewise_bot,
     inputs=[
@@ -136,17 +111,15 @@ interface = gr.Interface(
     ],
     outputs=[
         gr.Textbox(label="🧘‍♂ Krishna's Answer"),
-        gr.Audio(label="🔊 Krishna’s Voice", type="filepath"),
-        gr.Audio(label="🎶 Background Music", autoplay=True, type="filepath")
+        gr.Audio(label="🔊 Listen to Krishna's Voice"),
+        gr.Audio(label="🎶 Background Music", autoplay=True)
     ],
     title="🕉 VerseWise - Divine Wisdom from the Gita",
-    description="Ask any question, and receive a verse from the Bhagavad Gita with divine guidance.",
+    description="Ask any question, and receive a Gita verse with Krishna’s loving wisdom.",
     article=get_quote_of_the_day(),
-    flagging_mode="never",
+    allow_flagging="never",
     theme="soft",
     css=custom_css
 )
 
-if __name__ == "__main__":
-    print(f"Temp directory for audio files: {TEMP_DIR}")
-    interface.launch()
+interface.launch()