Update app.py

app.py

@@ -1,99 +1,100 @@
-import streamlit as st
-from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
-import speech_recognition as sr
-from gtts import gTTS
-from pydub import AudioSegment
-import os
-import tempfile
-
-# Load Translation Model
-@st.cache_resource
-def load_m2m_model():
-    model_name = "facebook/m2m100_418M"
-    model = M2M100ForConditionalGeneration.from_pretrained(model_name)
-    tokenizer = M2M100Tokenizer.from_pretrained(model_name)
-    return model, tokenizer
-
-model, tokenizer = load_m2m_model()
-
-# Language Mapping
-language_codes = {
-    "English": "en",
-    "Hindi": "hi",
-    "Marathi": "mr",
-    "Chinese": "zh",
-    "Japanese": "ja",
-    "Spanish": "es",
-    "Korean": "ko"
-}
-
-# Streamlit App
-st.markdown("<h1 style='text-align: center;'>🔄 PolyglotPal 🔄</h1>", unsafe_allow_html=True)
-st.markdown("<h5 style='text-align: center;'>Your friendly multilingual assistant</h5>", unsafe_allow_html=True)
-st.sidebar.header("Language Translation Options")
-
-# Input Language
-input_language = st.sidebar.selectbox("Select Input Language", list(language_codes.keys()))
-
-# Target Language
-target_language = st.sidebar.selectbox(
-    "Select Target Language",
-    [lang for lang in language_codes.keys() if lang != input_language]
-)
-
-# Input Method
-input_method = st.radio("Select Input Method", ["Text Input", "Speech Input"])
-
-if input_method == "Speech Input":
-    st.subheader("Upload Audio File for Translation")
-    audio_file = st.file_uploader("Upload an MP3 file", type=["mp3"])
-
-# Text Input or Speech-to-Text
-input_text = ""
-if input_method == "Text Input":
-    st.subheader(f"Input Text ({input_language})")
-    input_text = st.text_area("Enter text to translate:", height=150)
-elif input_method == "Speech Input" and audio_file:
-    # Process MP3 to WAV
-    try:
-        # Convert MP3 to WAV using pydub
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav_file:
-            audio = AudioSegment.from_file(audio_file, format="mp3")
-            audio.export(tmp_wav_file.name, format="wav")
-        
-        # Perform Speech-to-Text
-        recognizer = sr.Recognizer()
-        with sr.AudioFile(tmp_wav_file.name) as source:
-            audio_data = recognizer.record(source)
-            input_text = recognizer.recognize_google(audio_data, language=language_codes[input_language])
-            st.success(f"Recognized Speech: {input_text}")
-    except Exception as e:
-        st.error(f"Error processing audio: {e}")
-
-if st.button("Translate"):
-    if input_text.strip():
-        try:
-            # Set source and target languages
-            tokenizer.src_lang = language_codes[input_language]
-            encoded_input = tokenizer(input_text, return_tensors="pt")
-            
-            # Generate translation
-            generated_tokens = model.generate(
-                **encoded_input,
-                forced_bos_token_id=tokenizer.lang_code_to_id[language_codes[target_language]]
-            )
-            translated_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
-            
-            # Display Translation
-            st.subheader(f"Translated Text ({target_language})")
-            st.text_area("Translation Result:", value=translated_text, height=150, disabled=True)
-            
-            # Text-to-Speech
-            st.subheader("Text-to-Speech Output")
-            tts = gTTS(translated_text, lang=language_codes[target_language])
-            tts.save("translated_audio.mp3")
-            st.audio("translated_audio.mp3", format="audio/mp3")
-        except Exception as e:
-            st.error(f"Translation error: {e}")
-    else:
-        st.error("Please provide text or speech input for translation.")
+import streamlit as st
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+import speech_recognition as sr
+from gtts import gTTS
+from pydub import AudioSegment
+import os
+import tempfile
+
+# Load Translation Model
+@st.cache_resource
+def load_m2m_model():
+    model_name = "facebook/m2m100_418M"
+    model = M2M100ForConditionalGeneration.from_pretrained(model_name)
+    tokenizer = M2M100Tokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+model, tokenizer = load_m2m_model()
+
+# Language Mapping
+language_codes = {
+    "English": "en",
+    "Hindi": "hi",
+    "Marathi": "mr",
+    "Chinese": "zh",
+    "Japanese": "ja",
+    "Spanish": "es",
+    "Korean": "ko"
+}
+
+# Streamlit App
+st.markdown("<h1 style='text-align: center;'>🔄 PolyglotPal 🔄</h1>", unsafe_allow_html=True)
+st.markdown("<h5 style='text-align: center;'>Your friendly multilingual assistant</h5>", unsafe_allow_html=True)
+st.sidebar.header("Language Translation Options")
+
+# Input Language
+input_language = st.sidebar.selectbox("Select Input Language", list(language_codes.keys()))
+
+# Target Language
+target_language = st.sidebar.selectbox(
+    "Select Target Language",
+    [lang for lang in language_codes.keys() if lang != input_language]
+)
+
+# Input Method
+input_method = st.radio("Select Input Method", ["Text Input", "Speech Input"])
+
+if input_method == "Speech Input":
+    st.subheader("Upload Audio File for Translation")
+    audio_file = st.file_uploader("Upload an MP3 file", type=["mp3"])
+
+# Text Input or Speech-to-Text
+input_text = ""
+if input_method == "Text Input":
+    st.subheader(f"Input Text ({input_language})")
+    input_text = st.text_area("Enter text to translate:", height=150)
+elif input_method == "Speech Input" and audio_file:
+    # Process MP3 to WAV
+    try:
+        # Convert MP3 to WAV using pydub
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav_file:
+            audio = AudioSegment.from_file(audio_file, format="mp3")
+            audio.export(tmp_wav_file.name, format="wav")
+        
+        # Perform Speech-to-Text
+        recognizer = sr.Recognizer()
+        with sr.AudioFile(tmp_wav_file.name) as source:
+            audio_data = recognizer.record(source)
+            input_text = recognizer.recognize_google(audio_data, language=language_codes[input_language])
+            st.success(f"Recognized Speech: {input_text}")
+    except Exception as e:
+        st.error(f"Error processing audio: {e}")
+
+if st.button("Translate"):
+    if input_text.strip():
+        try:
+            # Set source and target languages
+            tokenizer.src_lang = language_codes[input_language]
+            encoded_input = tokenizer(input_text, return_tensors="pt")
+            
+            # Generate translation
+            generated_tokens = model.generate(
+                **encoded_input,
+                forced_bos_token_id=tokenizer.lang_code_to_id[language_codes[target_language]]
+            )
+            translated_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+            
+            # Display Translation
+            st.subheader(f"Translated Text ({target_language})")
+            st.text_area("Translation Result:", value=translated_text, height=150, disabled=True)
+            
+            # Text-to-Speech
+            # st.subheader("Text-to-Speech Output")
+            # tts = gTTS(translated_text, lang=language_codes[target_language])
+            # tts.save("translated_audio.mp3")
+            # st.audio("translated_audio.mp3", format="audio/mp3")
+        except:
+            #st.error(f"Translation error: {e}")
+            pass
+    else:
+        st.error("Please provide text or speech input for translation.")