Adding app file

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import tempfile
+import soundfile as sf
+import torch
+import easyocr
+
+# ---------------------------
+# Caching the OCR reader for performance
+# ---------------------------
+@st.cache_resource(show_spinner=False)
+def load_ocr_reader(languages):
+    # EasyOCR expects language codes like "en", "es", "ch_sim", "ar"
+    return easyocr.Reader(languages, gpu=False)
+
+# ---------------------------
+# Caching TTS model loading (Silero TTS)
+# ---------------------------
+@st.cache_resource(show_spinner=False)
+def load_tts_model(language):
+    # Map our language codes to Silero model speakers.
+    # Note: Silero officially supports 'en' (and some community models for other languages).
+    # For demonstration, if a language isn’t available, we fallback to English.
+    lang_speaker_map = {
+        'en': 'v3_en',
+        'es': 'v3_es',  # if available; otherwise, you might need to train or use an English model
+        'ch': 'v3_en',  # fallback to English for now (or replace with an experimental Chinese model)
+        'ar': 'v3_en'   # fallback to English (or an experimental Arabic model if available)
+    }
+    speaker = lang_speaker_map.get(language, 'v3_en')
+    device = torch.device('cpu')
+    # Load the Silero TTS model from torch.hub.
+    # This command will download the model the first time you run it.
+    model, example_text, sample_rate, speakers = torch.hub.load(
+        repo_or_dir='snakers4/silero-models',
+        model='silero_tts',
+        language=language,
+        speaker=speaker
+    )
+    return model, sample_rate, speaker
+
+def synthesize_speech(text, language):
+    model, sample_rate, speaker = load_tts_model(language)
+    # Synthesize speech; the output is a NumPy array with the audio waveform.
+    audio = model.apply_tts(text=text, speaker=speaker, sample_rate=sample_rate)
+    return audio, sample_rate
+
+def save_audio(audio, sample_rate):
+    # Save audio to a temporary file and return its path.
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as f:
+        sf.write(f.name, audio, sample_rate)
+        return f.name
+
+def extract_text_from_image(image_array, languages):
+    reader = load_ocr_reader(languages)
+    results = reader.readtext(image_array)
+    # Concatenate detected text parts.
+    extracted_text = " ".join([res[1] for res in results])
+    return extracted_text
+
+# ---------------------------
+# Mapping for EasyOCR language codes (EasyOCR uses 'ch_sim' for Chinese simplified)
+# ---------------------------
+ocr_language_map = {
+    'en': 'en',
+    'es': 'es',
+    'ch': 'ch_sim',
+    'ar': 'ar'
+}
+
+# ---------------------------
+# Streamlit App UI
+# ---------------------------
+st.title("Image-to-Audio Description App")
+st.write("Upload an image or enter text to generate audio descriptions.")
+
+# Select language for both OCR and TTS
+language = st.selectbox("Select language", options=['en', 'es', 'ch', 'ar'], index=0)
+
+# Choose input method
+input_method = st.radio("Input method", options=["Upload Image", "Enter Text"])
+
+text = ""
+
+if input_method == "Upload Image":
+    uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        # Convert PIL image to numpy array for EasyOCR
+        image_array = np.array(image)
+        with st.spinner("Extracting text from image..."):
+            # EasyOCR expects language codes; here we wrap our choice.
+            ocr_lang = [ocr_language_map.get(language, 'en')]
+            text = extract_text_from_image(image_array, ocr_lang)
+        st.write("**Extracted Text:**")
+        st.write(text)
+else:
+    text = st.text_area("Enter text to synthesize", "Type your description here...")
+
+if text and st.button("Generate Speech"):
+    with st.spinner("Synthesizing speech..."):
+        audio, sr = synthesize_speech(text, language)
+        audio_file = save_audio(audio, sr)
+    st.success("Audio generated!")
+    st.audio(audio_file)