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CoSTA
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import requests
import gradio as gr
import soundfile as sf
import time



def speech_translation(audio, language):
    if audio is None:
        return "No audio input provided!", "No audio input provided!"

    # Convert audio to .wav format if not already
    if not audio.endswith(".wav"):
        wav_data, samplerate = sf.read(audio)
        sf.write("temp_audio.wav", wav_data, samplerate)
        audio_file = "temp_audio.wav" 
    else:
        audio_file = audio

    # ASR processing 
    files = {
        'file': open(audio_file, "rb"),
        'language': (None, language), 
        'vtt': (None, 'true'),
    }
    response = requests.post('https://asr.iitm.ac.in/ssl_asr/decode', files=files)

    print(response.json())
    try:
        asr_output = response.json()['transcript']
    except:
        asr_output = "Error in ASR processing"

    asr_output = asr_output.replace("।", "")
    asr_output = asr_output.replace(".", "")

    time.sleep(1)


    if language == "telugu":
        lang = "te"
    elif language == "hindi":
        lang = "hi"
    elif language == "marathi":
        lang = "mr"
    elif language == "bengali":
        lang = "bn"

    payload = {
        "pipelineTasks": [
            {
                "taskType": "translation",
                "config": {
                    "language": {
                        "sourceLanguage": lang,
                        "targetLanguage": "en",
                    },
                },

            }
        ],
        "pipelineRequestConfig": {
            "pipelineId" : "64392f96daac500b55c543cd"
                            
        }
    }
    headers = {
        "Content-Type": "application/json",
        "userID": "2aeef589f4584eb08aa0b9c49761aeb8",
        "ulcaApiKey": "02ed10445a-66b0-4061-9030-9b0b8b37a4f1"
    }

    response = requests.post('https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline', json=payload, headers=headers)


    if response.status_code == 200:
        response_data = response.json()
        print(response_data)
        service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
        # if lang=="te":
        #     service_id = "bhashini/iitm/asr-dravidian--gpu--t4"
        # else:
        #     service_id = "bhashini/iitm/asr-indoaryan--gpu--t4"

        
        # print("halfway")

        

        compute_payload = {
               "pipelineTasks": [
            {
                "taskType": "translation",
                "config": {
                    "language": {
                        "sourceLanguage": lang,
                        "targetLanguage": "en",
                    },
                },

            }
        ],
                "inputData": {"input": [{"source": asr_output}]},
            }
        
        callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
            
        headers2 = {
                "Content-Type": "application/json",
                response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]:
                    response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
            }

        compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
        # print(compute_response.json())

        if compute_response.status_code == 200:
            compute_response_data = compute_response.json()
            print(compute_response_data)
            translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
            print(
                
                    "Translation successful",
                    translated_content
            )
            
        else:
            print (
                    "status_code", compute_response.status_code)

    return translated_content

iface = gr.Interface(
    fn=speech_translation,
    inputs=[
        gr.Audio(type="filepath", label="Record your speech"),
        gr.Dropdown(["telugu", "hindi", "marathi", "bengali"], label="Select Language")
    ], 
    outputs=["text"],
    title="Speech Translation",
    description="Record your speech and get the English translation.",
)

iface.launch()