Update app.py

Updated app.py

app.py

@@ -1,56 +1,62 @@
-import gradio as gr
-import numpy as np
-
-from transformers import (
-    pipeline,
-    WhisperForConditionalGeneration,
-    AutoTokenizer,
-    WhisperFeatureExtractor,
-    GenerationConfig
-)
-
-print("🔧 Loading ASR components...")
-
-# Load generation config and remove forced_decoder_ids
-gen_config = GenerationConfig.from_pretrained("amedcj/whisper-kurmanji")
-gen_config.forced_decoder_ids = None
-
-# Load model and set generation config directly
-model = WhisperForConditionalGeneration.from_pretrained("amedcj/whisper-kurmanji")
-model.generation_config = gen_config
-
-# Load tokenizer and feature extractor
-tokenizer = AutoTokenizer.from_pretrained("amedcj/whisper-kurmanji")
-feature_extractor = WhisperFeatureExtractor.from_pretrained("amedcj/whisper-kurmanji")
-
-# Create the ASR pipeline
-asr = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=tokenizer,
-    feature_extractor=feature_extractor,
-    device=-1  # CPU
-)
-
-def transcribe(audio_path):
-    print("📥 Transcription triggered")
-    if audio_path is None:
-        return "Please upload an audio file."
-
-    array, sampling_rate = librosa.load(audio_path, sr=None)
-    result = asr({"array": array, "sampling_rate": sampling_rate})
-    return result["text"]
-
-
-# Gradio Interface using Blocks with a Submit button (compatible with HF Spaces)
-with gr.Blocks() as demo:
-    gr.Markdown("## 🗣️ Kurdish ASR Demo")
-
-    audio_input = gr.Audio(type="filepath", label="🎤 Upload Kurdish Audio")
-    submit_btn = gr.Button("Submit")
-    output_text = gr.Textbox(label="📝 Transcription", interactive=False)
-
-    submit_btn.click(fn=transcribe, inputs=audio_input, outputs=output_text)
-
-print("🚀 Launching Gradio app...")
-demo.launch()
+import subprocess
+import sys
+
+# Force upgrade gradio
+subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "gradio>=4.44.0"])
+
+import gradio as gr
+import numpy as np
+
+from transformers import (
+    pipeline,
+    WhisperForConditionalGeneration,
+    AutoTokenizer,
+    WhisperFeatureExtractor,
+    GenerationConfig
+)
+
+print("🔧 Loading ASR components...")
+
+# Load generation config and remove forced_decoder_ids
+gen_config = GenerationConfig.from_pretrained("amedcj/whisper-kurmanji")
+gen_config.forced_decoder_ids = None
+
+# Load model and set generation config directly
+model = WhisperForConditionalGeneration.from_pretrained("amedcj/whisper-kurmanji")
+model.generation_config = gen_config
+
+# Load tokenizer and feature extractor
+tokenizer = AutoTokenizer.from_pretrained("amedcj/whisper-kurmanji")
+feature_extractor = WhisperFeatureExtractor.from_pretrained("amedcj/whisper-kurmanji")
+
+# Create the ASR pipeline
+asr = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=tokenizer,
+    feature_extractor=feature_extractor,
+    device=-1  # CPU
+)
+
+def transcribe(audio_path):
+    print("📥 Transcription triggered")
+    if audio_path is None:
+        return "Please upload an audio file."
+
+    array, sampling_rate = librosa.load(audio_path, sr=None)
+    result = asr({"array": array, "sampling_rate": sampling_rate})
+    return result["text"]
+
+
+# Gradio Interface using Blocks with a Submit button (compatible with HF Spaces)
+with gr.Blocks() as demo:
+    gr.Markdown("## 🗣️ Kurdish ASR Demo")
+
+    audio_input = gr.Audio(type="filepath", label="🎤 Upload Kurdish Audio")
+    submit_btn = gr.Button("Submit")
+    output_text = gr.Textbox(label="📝 Transcription", interactive=False)
+
+    submit_btn.click(fn=transcribe, inputs=audio_input, outputs=output_text)
+
+print("🚀 Launching Gradio app...")
+demo.launch()