added code to track transcription process running in background

ASR_Server.py

@@ -91,6 +91,15 @@ def generateTranscript(ASR_model, csv_path, output_dir="./"):
     import soundfile as sf
     from transformers import pipeline
 
+    global job_status
+    job_status.update({
+        "running": True,
+        "model": ASR_model,
+        "completed": 0,
+        "message": "Starting transcription..."
+    })
+
+
     # Load dataset without decoding audio (required!)
     dataset = load_dataset("satyamr196/asr_fairness_audio", split="train")
     # dataset = dataset.with_format("python", decode_audio=False)
@@ -103,6 +112,9 @@ def generateTranscript(ASR_model, csv_path, output_dir="./"):
         return
 
     # Load CSV
+    total = len(df)
+    job_status["total"] = total
+
     df = pd.read_csv(csv_path)
     print(f"CSV Loaded with {len(df)} rows")
 
@@ -157,6 +169,9 @@ def generateTranscript(ASR_model, csv_path, output_dir="./"):
             transcripts.append("")
             rtfx_score.append(0)
 
+        job_status["completed"] = idx + 1
+        job_status["message"] = f"Processing {idx + 1}/{total}"
+
     # Save results
     df["transcript"] = transcripts
     df["rtfx"] = rtfx_score
@@ -169,6 +184,8 @@ def generateTranscript(ASR_model, csv_path, output_dir="./"):
         print(f"Created directory: {output_dir}")
         
     df.to_csv(output_csv_path, index=False)
+    job_status["running"] = False
+    job_status["message"] = "Transcription completed."
 
     print(f"\n📄 Transcripts saved to: {output_csv_path}")
 
@@ -207,5 +224,9 @@ def asr_models():
     print("Transcription started in background")
     return jsonify({"asr_models": models})
 
+@app.route("/job_status")
+def get_status():
+    return jsonify(job_status)
+
 # if __name__ == "__main__":
 #     app.run(debug=True)