Update app.py

Updated app.py

app.py

@@ -1,188 +1,188 @@
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-import gradio as gr
-import numpy as np
-import scipy.io.wavfile
-import tempfile
-import os
-from transformers import VitsModel, AutoTokenizer
-import torch
-import re
-import traceback
-
-print("Starting application...")
-
-# Global variables for models
-punct_pipe = None
-model = None
-tokenizer = None
-
-def load_models():
-    global punct_pipe, model, tokenizer
-    
-    print("Loading punctuation model...")
-    try:
-        punctuation_model_id = "oliverguhr/fullstop-punctuation-multilang-large"
-        punct_tokenizer = AutoTokenizer.from_pretrained(punctuation_model_id)
-        punct_model = AutoModelForTokenClassification.from_pretrained(punctuation_model_id)
-        punct_pipe = pipeline("token-classification", model=punct_model, tokenizer=punct_tokenizer, aggregation_strategy="simple")
-        print("✓ Punctuation model loaded successfully")
-    except Exception as e:
-        print(f"✗ Error loading punctuation model: {e}")
-        punct_pipe = None
-
-    print("Loading TTS model...")
-    try:
-        model = VitsModel.from_pretrained("facebook/mms-tts-kmr-script_latin")
-        tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-kmr-script_latin")
-        print("✓ TTS model loaded successfully")
-    except Exception as e:
-        print(f"✗ Error loading TTS model: {e}")
-        model = None
-        tokenizer = None
-
-# Load models at startup
-load_models()
-
-# Simple number-to-Kurmanji-word mapping
-num2word = {
-    "0": "sifir", "1": "yek", "2": "du", "3": "sê", "4": "çar", "5": "pênc",
-    "6": "şeş", "7": "heft", "8": "heşt", "9": "neh", "10": "deh"
-}
-
-def replace_numbers_with_words(text):
-    def repl(match):
-        num = match.group()
-        return num2word.get(num, num)
-    return re.sub(r'\b\d+\b', repl, text)
-
-def restore_punctuation(text):
-    if punct_pipe is None:
-        print("Punctuation model not available, skipping...")
-        return text
-    
-    try:
-        results = punct_pipe(text)
-        punctuated = ""
-        for token in results:
-            word = token['word']
-            punct = token.get('entity_group', '')
-            if punct == "PERIOD":
-                punctuated += word + ". "
-            elif punct == "COMMA":
-                punctuated += word + ", "
-            else:
-                punctuated += word + " "
-        return punctuated.strip()
-    except Exception as e:
-        print(f"Punctuation error: {e}")
-        return text
-
-def text_to_speech(text):
-    print(f"=== TTS Function Called ===")
-    print(f"Input text: '{text}'")
-    
-    try:
-        # Basic validation
-        if not text or text.strip() == "":
-            error_msg = "Please enter some text"
-            print(f"Error: {error_msg}")
-            return None
-        
-        # Check if models are loaded
-        if model is None or tokenizer is None:
-            error_msg = "TTS model not loaded properly"
-            print(f"Error: {error_msg}")
-            return None
-        
-        print("Processing text...")
-        
-        # Process text
-        processed_text = text.strip()  # Start simple, skip punctuation for now
-        processed_text = replace_numbers_with_words(processed_text)
-        print(f"Processed text: '{processed_text}'")
-        
-        # Tokenize
-        print("Tokenizing...")
-        inputs = tokenizer(processed_text, return_tensors="pt")
-        print(f"Tokenized successfully, input_ids shape: {inputs['input_ids'].shape}")
-        
-        # Generate audio
-        print("Generating audio...")
-        with torch.no_grad():
-            output = model(**inputs).waveform
-        print(f"Audio generated, shape: {output.shape}")
-        
-        # Convert to numpy
-        waveform = output.squeeze().numpy()
-        print(f"Waveform shape: {waveform.shape}")
-        
-        # Save to file
-        print("Saving audio file...")
-        tmp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-        tmp_path = tmp_file.name
-        tmp_file.close()
-        
-        scipy.io.wavfile.write(
-            tmp_path, 
-            rate=model.config.sampling_rate, 
-            data=waveform
-        )
-        
-        print(f"✓ Audio saved to: {tmp_path}")
-        print("=== TTS Function Completed Successfully ===")
-        return tmp_path
-        
-    except Exception as e:
-        error_msg = f"Error in TTS: {str(e)}"
-        print(f"✗ {error_msg}")
-        print("Full traceback:")
-        traceback.print_exc()
-        return None
-
-# Simple test function to verify Gradio is working
-def test_function(text):
-    print(f"Test function called with: {text}")
-    return f"You entered: {text}"
-
-# Create a simple interface first to test
-print("Creating Gradio interface...")
-
-# Option 1: Simple test interface (uncomment to test basic functionality)
-# interface = gr.Interface(
-#     fn=test_function,
-#     inputs=gr.Textbox(label="Test Input"),
-#     outputs=gr.Textbox(label="Test Output"),
-#     title="Test Interface"
-# )
-
-# Option 2: Full TTS interface
-interface = gr.Interface(
-    fn=text_to_speech,
-    inputs=gr.Textbox(
-        label="Enter Kurmanji Text",
-        placeholder="e.g. Silav! Ez bi xêr im.",
-        lines=2,
-        value=""  # Default empty value
-    ),
-    outputs=gr.Audio(label="Generated Speech"),
-    title="Kurmanji Text-to-Speech",
-    description="Enter Kurmanji Kurdish text to convert to speech.",
-    examples=[
-        ["Silav"],
-        ["Ez bi xêr im"],
-        ["Spas"]
-    ],
-    cache_examples=False,
-    flagging_mode="never"
-)
-
-print("Launching interface...")
-
-if __name__ == "__main__":
-    interface.launch(
-        debug=True,
-        share=False,
-        show_error=True,
-        server_name="0.0.0.0" if "SPACE_ID" in os.environ else "127.0.0.1",
-        server_port=7860
+from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
+import gradio as gr
+import numpy as np
+import scipy.io.wavfile
+import tempfile
+import os
+from transformers import VitsModel, AutoTokenizer
+import torch
+import re
+import traceback
+
+print("Starting application...")
+
+# Global variables for models
+punct_pipe = None
+model = None
+tokenizer = None
+
+def load_models():
+    global punct_pipe, model, tokenizer
+    
+    print("Loading punctuation model...")
+    try:
+        punctuation_model_id = "oliverguhr/fullstop-punctuation-multilang-large"
+        punct_tokenizer = AutoTokenizer.from_pretrained(punctuation_model_id)
+        punct_model = AutoModelForTokenClassification.from_pretrained(punctuation_model_id)
+        punct_pipe = pipeline("token-classification", model=punct_model, tokenizer=punct_tokenizer, aggregation_strategy="simple")
+        print("✓ Punctuation model loaded successfully")
+    except Exception as e:
+        print(f"✗ Error loading punctuation model: {e}")
+        punct_pipe = None
+
+    print("Loading TTS model...")
+    try:
+        model = VitsModel.from_pretrained("facebook/mms-tts-kmr-script_latin")
+        tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-kmr-script_latin")
+        print("✓ TTS model loaded successfully")
+    except Exception as e:
+        print(f"✗ Error loading TTS model: {e}")
+        model = None
+        tokenizer = None
+
+# Load models at startup
+load_models()
+
+# Simple number-to-Kurmanji-word mapping
+num2word = {
+    "0": "sifir", "1": "yek", "2": "du", "3": "sê", "4": "çar", "5": "pênc",
+    "6": "şeş", "7": "heft", "8": "heşt", "9": "neh", "10": "deh"
+}
+
+def replace_numbers_with_words(text):
+    def repl(match):
+        num = match.group()
+        return num2word.get(num, num)
+    return re.sub(r'\b\d+\b', repl, text)
+
+def restore_punctuation(text):
+    if punct_pipe is None:
+        print("Punctuation model not available, skipping...")
+        return text
+    
+    try:
+        results = punct_pipe(text)
+        punctuated = ""
+        for token in results:
+            word = token['word']
+            punct = token.get('entity_group', '')
+            if punct == "PERIOD":
+                punctuated += word + ". "
+            elif punct == "COMMA":
+                punctuated += word + ", "
+            else:
+                punctuated += word + " "
+        return punctuated.strip()
+    except Exception as e:
+        print(f"Punctuation error: {e}")
+        return text
+
+def text_to_speech(text):
+    print(f"=== TTS Function Called ===")
+    print(f"Input text: '{text}'")
+    
+    try:
+        # Basic validation
+        if not text or text.strip() == "":
+            error_msg = "Please enter some text"
+            print(f"Error: {error_msg}")
+            return None
+        
+        # Check if models are loaded
+        if model is None or tokenizer is None:
+            error_msg = "TTS model not loaded properly"
+            print(f"Error: {error_msg}")
+            return None
+        
+        print("Processing text...")
+        
+        # Process text
+        processed_text = text.strip()  # Start simple, skip punctuation for now
+        processed_text = replace_numbers_with_words(processed_text)
+        print(f"Processed text: '{processed_text}'")
+        
+        # Tokenize
+        print("Tokenizing...")
+        inputs = tokenizer(processed_text, return_tensors="pt")
+        print(f"Tokenized successfully, input_ids shape: {inputs['input_ids'].shape}")
+        
+        # Generate audio
+        print("Generating audio...")
+        with torch.no_grad():
+            output = model(**inputs).waveform
+        print(f"Audio generated, shape: {output.shape}")
+        
+        # Convert to numpy
+        waveform = output.squeeze().numpy()
+        print(f"Waveform shape: {waveform.shape}")
+        
+        # Save to file
+        print("Saving audio file...")
+        tmp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        tmp_path = tmp_file.name
+        tmp_file.close()
+        
+        scipy.io.wavfile.write(
+            tmp_path, 
+            rate=model.config.sampling_rate, 
+            data=waveform
+        )
+        
+        print(f"✓ Audio saved to: {tmp_path}")
+        print("=== TTS Function Completed Successfully ===")
+        return tmp_path
+        
+    except Exception as e:
+        error_msg = f"Error in TTS: {str(e)}"
+        print(f"✗ {error_msg}")
+        print("Full traceback:")
+        traceback.print_exc()
+        return None
+
+# Simple test function to verify Gradio is working
+def test_function(text):
+    print(f"Test function called with: {text}")
+    return f"You entered: {text}"
+
+# Create a simple interface first to test
+print("Creating Gradio interface...")
+
+# Option 1: Simple test interface (uncomment to test basic functionality)
+# interface = gr.Interface(
+#     fn=test_function,
+#     inputs=gr.Textbox(label="Test Input"),
+#     outputs=gr.Textbox(label="Test Output"),
+#     title="Test Interface"
+# )
+
+# Option 2: Full TTS interface
+interface = gr.Interface(
+    fn=text_to_speech,
+    inputs=gr.Textbox(
+        label="Enter Kurmanji Text",
+        placeholder="e.g. Silav! Ez bi xêr im.",
+        lines=2,
+        value=""  # Default empty value
+    ),
+    outputs=gr.Audio(label="Generated Speech"),
+    title="Kurmanji Text-to-Speech",
+    description="Enter Kurmanji Kurdish text to convert to speech.",
+    examples=[
+        ["Silav"],
+        ["Ez bi xêr im"],
+        ["Spas"]
+    ],
+    cache_examples=False,
+    flagging_mode="never"
+)
+
+print("Launching interface...")
+
+if __name__ == "__main__":
+    interface.launch(
+        debug=True,
+        share=False,
+        show_error=True,
+        server_name="0.0.0.0" if "SPACE_ID" in os.environ else "127.0.0.1",
+        server_port=7860
     )