Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+from ner import perform_ner
+from intent import perform_intent_classification
+ 
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+ 
+def transcribe(stream, new_chunk):
+    transcription = ""
+    sentence_buffer = ""
+    results = []
+    sr, y = new_chunk
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+ 
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y
+    print(transcriber({"sampling_rate": sr, "raw": stream})["text"])
+    transcription=transcriber({"sampling_rate": sr, "raw": stream})["text"]
+    # Check for sentence boundaries
+    sentence_boundary = "." in transcription or "?" in transcription
+ 
+    if sentence_boundary:
+        sentence = sentence_buffer + transcription.split(transcription[-1])[0]
+        print("Sentence Buffer :",sentence_buffer)
+        print("Sentence :",sentence)
+        ner_result = perform_ner(sentence)
+        intent_result = perform_intent_classification(sentence)
+        print("NER Result (sentence):", ner_result)
+        print("Intent Result (sentence):", intent_result)
+    sentence_buffer = transcription[-1]  # Start a new sentence buffer
+    transcription = ""  # Reset transcription for the new sentence
+    return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"], ner_result, intent_result
+ 
+demo = gr.Interface(
+    transcribe,["state", gr.Audio(sources=["microphone"], streaming=True),
+           ],
+           ["state", gr.Text(label="Transcribe"), gr.Text(label="NER"), gr.Text(label="Intent")],
+    live=True,
+)
+demo.launch(share=True)