Update app.py

app.py

@@ -19,8 +19,59 @@ def deepgram_transcribe(audio_file):
     return transcription
 
 def assemblyai_transcribe(audio_file):
-    # Replace with actual Assembly AI transcription code
-    transcription = "This is a dummy transcription from Assembly AI API"
+    import assemblyai as aai
+
+    # Replace with your API key
+    aai.settings.api_key = "96206c6070cf4157b84f4f8eb66b5903"
+    
+    # URL of the file to transcribe
+    #FILE_URL = "https://assemblyaiusercontent.com/playground/ECw2Ncu7btO.mp3"
+    #FILE_URL = "C:/lakshmi/AI usecases/tamil_audio1.mp3"
+    
+    # You can also transcribe a local file by passing in a file path
+    # FILE_URL = './path/to/file.mp3'
+    
+    # You can set additional parameters for the transcription
+    config = aai.TranscriptionConfig(
+      speech_model=aai.SpeechModel.nano,
+      
+      language_detection=True
+    )
+    
+    transcriber = aai.Transcriber(config=config)
+    transcript = transcriber.transcribe(audio_file)
+    
+    if transcript.status == aai.TranscriptStatus.error:
+        print(transcript.error)
+    else:
+        print(transcript.text)
+    # Load a pre-trained sentiment analysis model for Tamil
+    #test_script = "இந்த செய்தி மிகவும் சோகம் மிகுந்தது.இந்த செய்தி நன்றாக உள்ளது."
+    """from transformers import pipeline
+    sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+    result = sentiment_analyzer(transcript.text)
+    print(result)
+    
+    lines = test_script.split('.')  # Split the transcript into lines
+    sentiment_results = []
+    
+    for line in lines:
+        line = line.strip()  # Remove leading/trailing whitespace
+        if line:  # Only analyze non-empty lines
+            sentiment = sentiment_analyzer(line)
+            sentiment_results.append((line, sentiment))
+    print(sentiment_results)
+    # Write the Tamil text to a file
+    with open("tamil_text1.txt", "w", encoding="utf-8") as file:
+        file.write(transcript.text)
+    
+    # Write the sentiment analysis results to a file 
+    
+    # Write the list of dictionaries in a human-readable format
+    with open("tamil_result.txt", 'w', encoding='utf-8') as file:
+        for result in sentiment_results:
+            file.write(f"Label: {result[0]}, Score: {result[1]}\n")
+    """
     return transcription
 
 # Sentiment analysis function