Update app.py

app.py

@@ -3,125 +3,153 @@ import pandas as pd
 import uuid
 import shutil
 import re
-import asyncio
-import edge_tts
 from sentence_transformers import SentenceTransformer, util
 import os
 import tempfile
+import asyncio
+import edge_tts
 
 # Load Bhagavad Gita dataset
 df = pd.read_csv("bhagavad_gita.csv")
 
-# Load SentenceTransformer model & precompute embeddings
+# Load sentence transformer model and precompute embeddings (once)
 model = SentenceTransformer("all-MiniLM-L6-v2")
-verse_embeddings = model.encode(df["meaning_in_english"].tolist(), convert_to_tensor=True)
+verse_embeddings = model.encode(df['meaning_in_english'].tolist(), convert_to_tensor=True)
 
 bg_music_path = "krishna_bg_music.mp3"
+
+# Create temp directory for audio files
 TEMP_DIR = tempfile.mkdtemp()
 
-# ✅ Clean unwanted characters
+def shorten_explanation(text, max_sentences=2):
+    sentences = text.split('. ')
+    shortened = '. '.join(sentences[:max_sentences]).strip()
+    if not shortened.endswith('.'):
+        shortened += '.'
+    return shortened
+
 def clean_english(text):
-    text = re.sub(r"[^\x00-\x7F]+", " ", text)
-    return " ".join(text.split())
+    text = re.sub(r'[^\x00-\x7F]+', ' ', text)
+    text = ' '.join(text.split())
+    return text
+
+async def generate_voice_edge_tts_async(text, use_krishna_voice):
+    """Generate speech using Edge TTS with en-IN-PrabhatNeural."""
+    if not text.strip():
+        return None
 
-# ✅ Microsoft TTS async function
-async def generate_voice_async(text, voice="en-IN-PrabhatNeural"):
     filename = os.path.join(TEMP_DIR, f"voice_{uuid.uuid4()}.mp3")
+
     try:
-        communicate = edge_tts.Communicate(text, voice=voice)
+        communicate = edge_tts.Communicate(
+            text=text,
+            voice="en-IN-PrabhatNeural",
+            rate="-2%",
+            volume="+0%"
+        )
         await communicate.save(filename)
-        return filename if os.path.exists(filename) else None
+        return filename
     except Exception as e:
-        print("TTS Error:", e)
+        print(f"Edge TTS ERROR: {e}")
         return None
 
-# ✅ Wrapper for sync Gradio compatibility
-def generate_voice(text, use_krishna_voice):
-    voice = "en-IN-PrabhatNeural" if use_krishna_voice else "en-US-GuyNeural"
+def generate_voice_edge_tts(text, use_krishna_voice):
+    """Wrapper to run the async Edge TTS speech synthesis synchronously."""
     try:
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        result = loop.run_until_complete(generate_voice_async(text, voice))
-        loop.close()
-        return result
-    except:
-        return None
+        return asyncio.run(generate_voice_edge_tts_async(text, use_krishna_voice))
+    except RuntimeError:
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(generate_voice_edge_tts_async(text, use_krishna_voice))
 
-# ✅ Select background music
 def get_unique_bgm():
     if not os.path.exists(bg_music_path):
         return None
-    path = os.path.join(TEMP_DIR, f"bgm_{uuid.uuid4()}.mp3")
-    shutil.copy(bg_music_path, path)
-    return path
 
-def shorten_explanation(text, max_sentences=2):
-    parts = text.split(". ")
-    shortened = ". ".join(parts[:max_sentences]).strip()
-    return shortened + "." if not shortened.endswith(".") else shortened
+    unique_path = os.path.join(TEMP_DIR, f"bgm_{uuid.uuid4()}.mp3")
+    try:
+        shutil.copy(bg_music_path, unique_path)
+        return unique_path
+    except Exception as e:
+        print(f"BGM Error: {e}")
+        return None
 
-# ✅ Main bot logic
 def versewise_bot(question, play_music, use_krishna_voice):
     if not question.strip():
-        return "Please ask a meaningful question.", None, None
+        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()
     verse = df.iloc[idx]
 
-    sanskrit = verse["verse_in_sanskrit"]
-    translation = verse["translation_in_english"]
-    explanation = shorten_explanation(verse["meaning_in_english"])
-    verse_number = verse["verse_number"]
+    sanskrit = verse['verse_in_sanskrit']
+    translation = verse['translation_in_english']
+    explanation = shorten_explanation(verse['meaning_in_english'])
+    verse_number = verse['verse_number']
 
-    reply = f"""
-📖 Bhagavad Gita — {verse_number}
+    reply = f"""📖 Bhagavad Gita {verse_number}
 
-🕉 Sanskrit:  
-[translate:{sanskrit[:60]}...]
+🕉 "[translate:{sanskrit[:60]}...]"
 
 "{translation}"
 
 🕊 {explanation}
 
-🌼 Trust the process — Krishna walks with you.
-"""
+🌼 Stay strong — Krishna walks with you."""
 
-    tts_text = clean_english(f"{translation}. {explanation}")
-    audio_path = generate_voice(tts_text, use_krishna_voice)
-    bgm = get_unique_bgm() if play_music else None
+    voice_text = clean_english(f"{translation}. {explanation}")
+    audio_path = generate_voice_edge_tts(voice_text, use_krishna_voice)
+    music = get_unique_bgm() if play_music else None
 
-    return reply, audio_path, bgm
+    return reply, audio_path, music
 
-# Quote of the day
 def get_quote_of_the_day():
     verse = df.sample(1).iloc[0]
-    return f"""
-<b>Quote of the Day — Gita {verse['verse_number']}</b><br>
-<i>[translate:{verse['verse_in_sanskrit'][:60]}...]</i><br>
-"{verse['translation_in_english']}"
+    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>
+    <span style="color:#2d2d2d;">"{translation}"</span></div>"""
+
+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;
+  background-size: cover !important;
+  background-repeat: no-repeat !important;
+  background-position: center center !important;
+  background-attachment: fixed !important;
+}
+.gradio-container, .gradio-interface {
+  background-color: rgba(255,255,255,0.92) !important;
+  border-radius: 18px;
+  padding: 25px;
+  max-width: 760px;
+  box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
+  margin: auto;
+}
 """
 
-# ✅ Gradio UI
 interface = gr.Interface(
     fn=versewise_bot,
     inputs=[
         gr.Textbox(label="Ask Krishna", placeholder="Why am I struggling in life?", lines=2),
         gr.Checkbox(label="Play Background Music", value=True),
-        gr.Checkbox(label="Use Krishna's Voice (PrabhatNeural)", value=True),
+        gr.Checkbox(label="Use Krishna's Voice (Male Voice)", value=True)
     ],
     outputs=[
-        gr.Textbox(label="🧘 Krishna’s Answer"),
-        gr.Audio(label="🔊 Krishna Speaks", type="filepath"),
-        gr.Audio(label="🎶 Background Music", autoplay=True, type="filepath"),
+        gr.Textbox(label="🧘‍♂ Krishna's Answer"),
+        gr.Audio(label="🔊 Listen to Krishna's Voice", type="filepath"),
+        gr.Audio(label="🎶 Background Music", autoplay=True, type="filepath")
     ],
-    title="🕉 VerseWise — Direct Wisdom from the Gita",
-    description="Ask a question, receive Krishna’s answer via Bhagavad Gita.",
+    title="🕉 VerseWise - Divine Wisdom from the Gita",
+    description="Ask any question, and receive a Gita verse with Krishna's loving wisdom.",
     article=get_quote_of_the_day(),
     flagging_mode="never",
+    theme="soft",
+    css=custom_css
 )
 
 if __name__ == "__main__":
-    print(f"TTS temp directory: {TEMP_DIR}")
+    print(f"Temp directory for audio: {TEMP_DIR}")
     interface.launch()