Unable to pass image data to gemini multimodal live api

Google AI Studio
,
1

I am currently testing gemini 2.0 multimodal live api and using following code to send image data to the api.

import asyncio
from google import genai
from google.genai.live import types
import base64
import os

client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'), http_options={'api_version': 'v1alpha'})
model_id = "gemini-2.0-flash-exp"
config = {"response_modalities": ["TEXT"]}

async def send_data_to_gemini(base64_image_data):
    async with client.aio.live.connect(model='gemini-2.0-flash-exp', config=config) as session:
        # Decode the base64 image data
        image_bytes = base64.b64decode(base64_image_data)

        # Create content part for the image
        # content1 = types.LiveClientRealtimeInput(
        #     media_chunks=[
        #         {
        #             'mime_type': 'image/jpeg',
        #             'data': image_bytes
        #         }
        #     ]
        # )
        print('Sending image data to gemini...')
        # await session.send(input=content1)
        await session.send(input={'mime_type': 'image/jpeg', 'data': image_bytes})

        print('Sending text to gemini ..')
        await session.send(input='what is the heading in this image', end_of_turn=True)

        # Receive response
        print('Receiving response from Gemini...')
        turn = session.receive()
        response_text = []

        async for chunk in turn:
            if chunk.text is not None:
                response_text.append(chunk.text)

        print('Response:', ''.join(response_text))

# Main execution
if __name__ == "__main__":
    image_data = '/9j/4AAQSkZJRgABAQAAAQABAAD/4gHYSUNDX1BST0ZJTEUAAQEAAAHIAAAAAAQwAABtbnRyUkdCIFhZWiAH4AABAAEAAAAAAABhY3NwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAA9tYAAQAAAADTLQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlkZXNjAAAA8AAAACRyWFlaAAABFAAAABRnWFlaAAABKAAAABRiWFlaAAABPAAAABR3dHB0AAABUAAAABRyVFJDAAABZAAAAChnVFJDAAABZAAAAChiVFJDAAABZAAAAChjcHJ0AAABjAAAADxtbHVjAAAAAAAAAAEAAAAMZW5VUwAAAAgAAAAcAHMAUgBHAEJYWVogAAAAAAAAb6IAADj1AAADkFhZWiAAAAAAAABimQAAt4UAABjaWFlaIAAAAAAAACSgAAAPhAAAts9YWVogAAAAAAAA9tYAAQAAAADTLXBhcmEAAAAAAAQAAAACZmYAAPKnAAANWQAAE9AAAApbAAAAAAAAAABtbHVjAAAAAAAAAAEAAAAMZW5VUwAAACAAAAAcAEcAbwBvAGcAbABlACAASQBuAGMALgAgADIAMAAxADb/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/2wBDAQMDAwQDBAgEBAgQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/wAARCADUAeADASIAAhEBAxEB/8QAHQABAAIDAQEBAQAAAAAAAAAAAAQGAwUHAgEICf/EAE8QAAEEAQMDAQQGBgUHCAsAAAEAAgMEBQYREgcTITEUIkFSIzIzN1GRCBVCdoG0FiRhcbEXQ2KSwtHSVnJ1gpahwdNERlNXY2SDk5Sjs//EABoBAQEAAwEBAAAAAAAAAAAAAAABAgQFAwb/xAA4EQEAAgECAwQHBgQHAAAAAAAAARECAwQFITESQWHhExQVIlFxoQaBkcHR8BYysfEjQkNSU3Li/9oADAMBAAIRAxEAPwD+qaIiAiIgIiICIiAiIgIiICIiAiIgKj6y0BndVZqvkqmtJMVDVEhhZBVc+WGV1O1XEsb3ScGSD2tz9+2d+2wHfbdXhaa9hslNmYchRzLalXtvFqsK/I2JPc7cnMOBaW8dttiHBxB8bIK9mNA6pymOnpV+ol3HzPy9rJR2KzJg5kb68rIICHTkFsUr4pS0bRv7PExgPcV4xegtZUcljZ73UaS/Rox1+7Ulpy7zyxxRRulMhnJ3Pbc7iQ5odK5xDnhrhObpDVTpMdPJrt7JooIGZJ8FBrPbZIy5xe3k93aDi/y33tgxrWlo5B3qlo/UEUEXt2sn2L0ODfjGZBtJjbDLUhDpbbeTnM94siIiLS1pjHqDsg083SzNOtWbFTXdur7Q23s9rJpZY3yVuxBMx0s7wJog6Q8+Pv8AJrSA2NgE/UuhdS5nkMTru1ie/Vjq2HMbYe4gFpe+Paw1sTyGAB4G4DpNy4uaW/YtFaxkLHX+p18kiV8oq0ooR3DLA6Phy58WNjili4uLi4Tl3IPaHKZR0vqaobvtOubN0WWQshE1VrfZ9pXunLTG5pJexzWAk7xlm43BLUGgu9LdR5D2T23qFPNHUyDciIZa8skTnMvVLEIc10537bK0kQ3O30ok25tc6RQ6W6mpUZ4v8oczrs+WdkpbLK00XeZ+qhRjil42A+Ti5scxcXglzBsGuAeNpY0Tqq7Fnq1/Xz7NfLY25Sr15MZE6KrLNNM5szmknuhkL4Igw7NcIXOO5kO0m7pzV0mWjs1dXOFOXKCxPCYgx0NNtKSNsMZ8h59qLJiXbbjdp3a0NIQchoLVtu5hbNXqZkaceOjx7LkEcBcy77O57pSeUhc0zc2ciS4/RNBLgXh2JnT7VpxenqtjqPZkv4eKaO9cFeVhyQkikaGva2fdrQ90bt+ReO37r2udzElmjNbfqoU5ep1v21oj43Y8fGDybUEbi5ji5pDrAdPtsPDu39UArI/RWoo6+afQ1n2cnkaNSjVyL6AklrNg5nk4cw2Ul0srh4bty88viCpobNVsZapz69y9uxNjHUYZZ3ua2Od27n2SI3NeXl53AEjQxgDGcRuTGo6A1PUzWQvS6/sWMbdbbEeLkqudDAZo4Wt2LpSSGvjlfx9PpeLeADuc2fReUu6hxuayOpfaocVmJsrVrvqbGJr6ctYQtcH/AA773ciCT6bDclYZtKa5k7ZHUJ7HviminMVFrGtc6BrY5Y2uLzuyZrpOLnEOEpa4lrGNQRcPoLV+Ozrslleo1/K1JLEDxXcH1+1DEyTdmzXlr3SSmJ7zs0e69rQI3CNsaXplqxs9z2DqhkK1S3bgnZX7UrjBG0zd+Nj+8HEy97fm4uLXNBG4bG1k/OdPsvnaWNpWdZ2wKIa6RzotzYkZdq2IpXcXNaHtbVczcAeZnOAb9UzX6NyN10D8zqaTIGrm5srAJKrGtiiMUjIq7Q0jcR82vD3bu5N3G3jiFcyHS3XN+tJXj6uZGmWQ2GVZK8M4fE+WZr+Ty6y7uhsfdiaHeWte1wcHsDztdQ6A1HmsFYxFfqPlKT53ZEmw2McgLLy6NvuuadoWkxN2I9wk+JAyVmwi0xqhs4dLrqxLD7ZFOYjUa0mEQcJIeTXA+/L9Ly9W+WN2b4GqvdPdUZXTNvTuX6gSZAX8PJjLUljHM4yyyuPdmLGPbtuwlgYDs0fE+hD5menOqL2ebkcT1MyuKx5fI59KNr5SN60ULOD3ylreDo3ygFjmufIS9r9luMBpTL4mtTjvaxv3H1r9y7KRvwsNmfI5sL+66V4ZEHgMDXgAMbv6LzqLTusMrFkW4bXRw808sbqMsePbN7Kxoj5Nc17+MpcWzeSG7CX0JY1w18HTm/8Aq/K4i/qye1j8jkIrMdR1YCOGsLLZpax97lIJfpWOc4ndshBBA2IV/NdIdd57TV3TOQ6rGSvkMRdx05OPsHnLZsdx0p/rfItji+ijj5bgb8nPaXRm2U9GZWC1kZrOqH2YLduKzXqurkRVgyZknEDn5O7XHl4JdI4u5gRtZ8qaU1dHXpwXdfTWTBPUknkbSEbrEccIbMw7P93uy8pN27cQ7iAQAVIv6c1JcZXDNZWYTFi5acphrsY6W27gG2gTuGlobJswtc3eTcg8Qgq+L6WavxtOrUm6qZS06KOqZp5H2i+SeN0rp5NnWXbNmD42mPfgwRngAXBzNlY6e6hNfIRUuo+Xilnpy1KRldJIyo5zHcJSBI10r2uEPkvBLIiCecs0r4+rtG65y1l4xN/DSwuxYoCXINc+UPdDZZI/gWPj2c6aBxaAOQg4u3DhwyW9N67nyFHKQVsJFYpzidzjfld3nCGWMcj2A4/ag7cttm7bDfcBCk6Xa47LxU6t5GKxJVZC6Z8U8n0vtUUj5Qw2eDS6GIRBoADS57h9d7HTYOneqq+Tjut6jXnV25CvadXeLLx2I2xl9cF1gjaSVsjy5wds14jA4t2M7RuB1zjaYranzdOV7KtGMvqjfv2Y3vdasOBYzgZ+Td2jcN2JHkneNX0Vr5pottdVLr21XWe+W0IN7gkiAh3936Psy8nNDd+bQGyF/lB5h6dZ6q7LCpraWGLJQZdjYo4ZmMhmt2BNBMA2ccXQ7ygmPg6TublzS0KVh9Famxeq5c5LruxYxk1q5ZOKNd3baJo4AxjXukdsGPilf6bbzbNDAHczNHaujbTeNfvfZrU7NWWZ1AAWnyS1jHYlYx7WmVkcD2bgAEzOIDGjgd5Sx2Xgzlm7ZyveovrtbBABsWymWRz3O/sDOy1u231Xkj3hsG2REQEREBERAREQEREBERAREQEREBVHrAAek2td9/GnskfB2/8ARpFblUer33Ta1/d3Jfy0iC3IiICIiAiIgIiICIiAiIgIiICIiAqHrDTXUXKZ+xk9M6lr0IGYS5SpAyFvbuTOhLJnxmJ7X9vsuIcSd+5x4gBxkviwm5VE8tY2IxLDGyWRhcN2McXBriPgCWOAP+ifwQUz+jfUhsEdX+m0jzXuyTtskwNfYrh0fbhlZ7MWjdvca5zCCHbPG4d22TOnmF1fgcLTxeqct+sH06dep3XSBznOiiYHSE8eTy+Qy+852/BkW7eReTZIL9WxLahjeeVOQRy8mloBLGv3BPqNnjyPG+49QVlE8BdxEzCS3ntyH1fx/u/tQZEWMzwgkGZgLdgd3Dxv6f4hRreaxdKtFcsXYhDNLBDG8O5BzppGxxbbfM97QD/agmoiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgKo9Xvum1r+7uS/lpFblUer33Ta1/d3Jfy0iC3IiICIiAiIgIiICIiAiIgIiICIiAtdf09hco6ycjj4rIuRRQWGSbuZLHG5zmMc30IBkf42877HcLYqp3uqGkcdrkdPLNm3+t204r85ZTldWrQymVsbppw3txc3QSNaHOBJ2A9RuGxzek8ZmMfeqBghmu4ubFd73iBDICNnNDhz4k7jc7jd2xHI748XojTuNhqj9WVpbFfGNxDp+0GmSqA0dsj04btBDTuBu7bbc75cjrTSWJj7uR1Fj67BZiqEvnaNppJhAyM+frGVwYB8x2Wd+p9ORu4yZ2g083M2Nhg94EAj1+Bc38wgiWdC6Wu7e24v2kgUm7zTSPJFSZs1fcl3ktka1258uI97ceFgrdONFU46sVXBxxNpQ1a8JbI/cRVnROgaTy3cGOgiLd99tnfO7l61Nr7TWlcZUyl6xYtNyFr2KlBj60lyxanDXvdHFFEHPe5rIpXOAB2bG8nYNK94HX+jdTYOjqPDaipT0MjCyevL3Q3k1zGvG4PkHi9pIPkcggsCLXHUeABc05qjuzYu+nb43jMo38/+zBf/AM0E+ijUdY6cvVXXG5KOCNstmL+s/QuPYlfFI4B2xLOUTyHehaOQJHlBukUarksfdkkhp3YJ3xfaNjeHFvvvZ529Peje3+9jh8FJQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBVHq9902tf3dyX8tIrcqj1e+6bWv7u5L+WkQW5ERAREQEREBERAREQEREBERAREQFyHrP0DpdTJ7epa12Z2cOGs4arWmnjrU3RTxSxOMsrIHzniJ3SsbyLBKyN3EEbrrypmqM5rfG5e+cHiGXKFWnQdEwwSF8tiW25kvFzdwWshHJ2w3buD53QVDDfowaHxRp3H5fNPyUVfHMuWIbDYmXrNO1TtstyRcS0zOnpNc553cRNK0nYt46an+hP0Ro0LNCKvm5Rbo36Ekti/wB9/bt7c3N7jSGuaBswtA2BO/I+V1DAao1Hdy4xuc0zJRj3libYY2R0cr2RV38mktHFhdLM0ctiTCSBstDitX9SbeDx8t3SLq9xuH9qvOfDKHe3Mhry9hsYafEhfMzcElpZtsXDigkZHotp63isZjsdnM3iJsPqDI6ko3qM0IsQ27z7brABkjewxn26doaW7gcfO43VQf8Aoe9ITUhowOz1aKLG1McRXv8Abc98FmrY9rLg3cWXupV2vkaRuxmwDfVWnVWt+oON0zNPjNEWJcxLhW2YooIZLAgvPrzuETtmgODZo42HY77PBO24Wztay1VRxGVyU2jLM82PFNsdSsyV8s75OPf4e4A9rC47Fu+4ad9kFWn/AEWuk02lb+kY8fdr1MhLVe+aGcCzHHBjY8a2FspaXds1GPjc07/bzEEFw2j5X9E/pRm8tdzOS/XUk16pk6jmC+WRMbfmyMs7msa0N5g5a41hdvxaWgftF1y1HqzV+NtXoMLpB96OvVbJBK8SgSzd/g5mzGOOwYWu3G5PLwNmuIiW9Y68qY9stfRMl21Nqd+Jjj2fGI8eZHBtt54nZoaAdxuCCPIJLQErpz0m0v0w9ufp+W/PNkI68U812YSSObEHkeQ1vl0s1iZx+MliV3gEAXRUjL6w1jTFJ+L0dLfM4xz5Ig2WNzY55OE3vOYGtdF9YtcRs3cu4+N5FnVGsHfq8YvRclgXK8NmR8sphETS491jg8BzXtZw2a4Auc/b0a8tC3oqDY1pryPLiKHQ/cxrZ4YZJd5hMA6eRkjg3t8XBkcRfuHbO5x7bhwK8xaw15FgdO2To+WxdvRTPyofFLF7J22ct+Aa4kuOzQ31JPjfY7B0BFQKmt9dm5jKGQ0FIx10h9ixD3XQVY3mMR8iWAl27pA5oHu8OR93dw81dea2nZzl0DZiEVGpYn5Mm3M0leWWWOMcN39sthZ+Jc8t25DYh0FFR83rLWWPzlDFY/RUtuCeWt7TbAl7UURilfMQQw7ua5kbWg7bmTyRsVgta21rDp+nkW6JtPu2GzPmrsild2XMLCyIgN5HkHO97bYFh23JaCF/RUTC621jbkrVsvoazTmnge97mtlfFDM2pDNwc7h6GSSWMOHqY/xOwwY3VfUh0bYp9HMnl7FqV7nGSHeX2t8ULG8mcePDg8kkHju7b0QdCRVHKZ/WMWUpY+lp1nYmgpy2Z/pH9p77UccsYIbxPGJ0jt9/HHcjbyoR1nrVmoYce7Rcr8dJHYZJaa2X6GZl4QR7gs95j4nCTkPQNJ+r7yC9oqLPrHW0eeyVWvo+Kzi6EoYLMUkpklGxL+LeGzi0Fh2BPL3mt3c3ivlDWetJrFCK3o0tZZZ/WXxufIKkrrL4gxxa0tcGhreZaTsXctuALgF7RarS+TyWY09jsjmcW/G5CxUglt1HA/QTPja58YJAJ4lxbvt6graoCIiAiIgIiICIiAiIgIiICIiAiIgIiICqPV77pta/u7kv5aRW5VHq9902tf3dyX8tIgtyIiAiIgIiICIiAiIgIiICIiAiIgIijTSvbIQ13hBJRQu/L8yd+X5laS2u1tidUZrT8uP0fqaPA5F8kZbcfUFjZgcC9oaSNi4eOXnb12K22PltTUK016sa9mSFjpoS4OMby0cm7tJB2O43BIWLvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUULvy/Mnfl+ZKLTUWouZc0zxLJpX9qSbhEzk4tZtvsPifeGw9TupEdszRsmilD2PaHNcPIIPkEJRaeiqWrOomndE6WyOs9QZLs4rFtcbEzWF2zhJ2+IA9XF+zQPiSFy3SH6ZnSTWedx2AxtjKxzZS5Fj68s1J7YjYkO0cZd6AuPpulFu/ovELi6MOcdyV7UUREQEREBERAREQEREBERAREQFUer33Ta1/d3Jfy0ityqPV77pta/u7kv5aRBbkREBERAREQEREBERAREQEREBERAUOx9qf4KYodj7U/wVhJY0RFkxEREBERAREQEREBERAREQEREBERARFptX4G3qfTtzB0NRZHBT2QzhkMe5rbEBa8O3aXAjzx2O48gkINJrbL5nT2Yx2YqQ0ZapjdUIntCIgyyx8jsfXbjHsfQAvLtthvYdOYiTA4WtiZLstoVw5rHybcmxlxLI/HqGNLWA/ENCoettLaoh07QM+ta1i1FbqUHT3cLFZbILFyGLl2ydmu4SPYdjsd2uO3HY78ad6gwzY6WPXsM7YZ4DdikxrWNnha49wNLXHg4t4AeoBafm8QUzrzozVnVLpFqLRmDeIrFtkDK1d9c8ZX152yEGT4czGADtsNh+J2/N3Tn9GXrTV6k6fysWmv1Bo+nqalnJKNm6x7ovZpPUgeXO48w0/g/dfr6700km9qlp651PBNMZ5Im+2t7MT5I+A2YGfVB3cBv6uJ/DazYDDtwOJhxLb1q4IXSOM9qTnLIXvc8lzvid3H8B/YPRKW28r/Yt/j/AIrIsdf7Fv8AH/FZFjLKBERAREQEREBERAREQEREBERAVR6vfdNrX93cl/LSK3Ln3XuzPW6Tal7EpZ3cXcift+001pdwgvk9mtWANmxFEHHYF7w3c/xWVa/L1btqOJtOvj5S127hcYXAD/R2+K2CAiIgIiICIiAiIgIiICIiAiIgKHY+1P8ABTFDsfan+CsJLGiIsmIiIgIioms9aa803qjH08L02s57Azsi9rvVLDRNA9zpOQER+sGsY12+/kuDfUggL2i5RN1H6r47V13HzdKbeSwjXE1bNU8JeJhhe0uLjxOzjYjIHndjPmXvC9VOoWXnZ7Z0U1FiY5J21QLEkTi0OZzM7iD4Y3YN4+pcdvgpY6Plc1icHXFrL5CCrG47NMjti47b7NHqTsCdh8AtJ/lO0GP/AFjr/wCo/wD3KpdUoK9jU2DntRB9itQsOgLv82XvjDyB6Ani0b+oG4+J3qtnIU6gf7TbjYY283NLve2O+3j1O/F2348T+C7my4RG70Y1pzq/BxN7xidprToxhdV3urHqhoIeTqWsP+q//cvn+VLp/wCn9KKn5O/3LjtfKWslM0UsZIKu/mzY9wOG8ZHBvq4OY9+zvGxZsR5X1lHKksnlv12v4NL4WVwY+fBwOzj72xcWn8fd2+JWx7Biemc/h5tb2/lHXD6+TsQ6n6CPkalrH/qv/wBy+/5TdCf8o6/+q/8A3LjbbmYqTiO7jGTQve7aao7fhvKxsYcx3k+65znOHgBhU2pfq3WB9WwHgtDtvRwBG43B8j+KscAxnl6T6eaT9oMo/wBP6+Tt2H1Fg9QMfJhsnBbEYBeI3e80EkAlp8gHi7Y7edj+C2K4v0+hrs6p1LIbxnlwOQjcQdubRYpkcvm4knbf05O2+sV2Z0jGeXva3yB5O3knYfmfC4u923qmtOld07ey3PrejGrVW9Ii+EgfHztutVtNbLqbTcE0tebUGNjlru4SsdbjDo3bb7OG/g+R4P4qLJrvRER2l1jhGH/SyEQ/2lx3ROAxB0dhZLVGvdsT46rJYtWIWvlsyGJvKSRxG7nH1JK2b9IaTkO8ml8Q8/i6lEf9lac7rwfR4/Z+csYn0n08246ra4rWsNhodF6p0xZlOosS7ICbJRbx0W2mOllZ72xczZr9j6ta4euyvDddaIf9TWOEd/dkIj/tLln9C9Hf8k8N/wDgRf8ACvbdIaTZ9TS+Ib/dSiH+yp634Mv4en/k+nm6q3V2lHjdmpsU7+65Gf8AxXp+q9LxRmWXUmLYwEAudbjAG/p53X5ey+ndNaY0VntaV9PuuWa9+/8AQRgBu3t8jN9gPDWDydvg0rWdGKeM6r6QtZLM0nsZDekqxzQENistjO3PYt23Dgd9vA8fHdbEZ5TpTrV7sTX9P1j9xLlZbTSw3uHD5z/xMse1HKaqPHpHTp5P2rX+xb/H/FZFROhL5H9GdFCWeWd0eDqRdyV5fI8MjDQXOPlziB5J8k7lXtZdWlVchERAREQEREBERAREQEREBERAXOf0gvum1D/0fb/lpV0Zc5/SC+6bUP8A0fb/AJaVB0ZERAREQEREBERAREQEREBERAREQFwz9JHrFqHpvHXxemKjmWrNOXIWL3s3tEkVaOWKN7asHpYsbSl4jJ+rG7wdxt3Na2/Sp2rMctqpDM+u8SQukjDjG7b6zSfQ/wBoVhJcPx/6Wei8tkLYxum89JisNBfkzt6SvwOLkqxyySRSR+S47RNALSQTPDx5cjx9N/Siwd6OvksPpTUliCapNJFjn4qSO9al9pxteLgCdmt7uQdGeXqYpCDxjcT0l3TPQDsrVzf9E8eLtOIQQytj47M2I2IHh3gke8CrF7LV7on9mi7jWhgfwHINB3Dd/wANxvt+KvNFO6VdRG9SsRfzLIPZhBYqtFV8L45qrbGPqXGxTB3rK1ttvLj4Hp6gqg9F+vVfWeptQYnL6zxOUowV8dYx9+CnJUhlsWbEsMtaAyAd1kbnUmBxPPnYLXeQF3CGvXrmQ168cRlcHyFjA0vdxDdzt6ni1o3/AAAHwUZuFwzIooWYik2OB3OJorsAY7m2Tdo28Hm1rvH7TQfUBBxXoH1G1VqmpXymRzdnUEd3TLNRZamys0SYXI2ZDKzGxuG3NzWumiMbyHM9mi32Mi8R9RrWuL+T1Fh9G9TsW+N0ePh4YdhEctZ8rmyhr5B6mw9r2+Q5rQN9tl3KpQoY8Stx9GvVE8rp5RDE1nckd9Z7th5cfiT5KkIj8+2sjrLI6GyejLVHqjBLcvyyRZJmLZLZFMkFsDnPl8eBxcW/DcD1UTRYo9HaU+Xw2heqeUhxOnfYIa+TqiVzu1JLO53dMpIL3P2Pg7cRt6bL9GoiuIam1LNqnLYDLz4DJ4Z8+KtOdTyMQjmjImiHkAkbH1B+I8+Fx7UGk8TBqh2sdNdRo6N+a4zIZEXLntEMkEJY0wsZvtG0842bjyA/YeXFd96owE6txln4DHTM/OVh/wDBchzHRTppm3yyWdNQxST2I7Mr4HOjMjmEkA7H6p38j0Pj8AvrOH6WepssIwiJ5z1mY7+6ofKb7PRw3mpOrlMTyqoiYqud3MeFNZ7L1UZZhtXepWnGsqVZobcMVYNBf2N+57ztmuDjG/Y+A3x6OWfF383/AEpr3ch1AxV2rlKE1fCU44wwSTv2lD5C07uIbC8DYegk+K2/+TvSte9as2cabDchKbD5JZC4xzbsDQ0fgdh/YAwA+Fjh6R6Dr5KHK1cR2X167a8McchEcQaZCHtb8H7TSjkPg8hb2OnqzP8A6lztSdPGL6xPSaj93+/FUtHZPWclWSq/q3gbzcayOu6SSAF8swkY0F5cQ4tkPNvIeCXM4ncFWo6cv56nYq6o1HW79mtJE11JrYpGd0vY2RjgeX2bixu/9/qs9/RWjZshSfX09E+3ju/JAYYwwRmbuElz/TbmH7DzxdxOw8LKzp1ps5StlZsdX7tOcWK7WNP0cgcdngk778eHj0BBIHlTHDUvs12ojxn81yjGu1M9m/CL+n9nyjrrBdEsxiM7rrMZC9VqYbKQGxHUMs8pM9RzGlrBt4Y0guOw93cncratp9M8dSqYvE9a9aU6lYFrmmG1K8tHa4Na90RLOHZZx29N3fiViwWl9Su1k5mm8xDPeuYnIBoyzDPXhi7lUcGR+m/puTuD6ldyYzX3BvcsYHlsN9opvX/WXynFc8vW5jLGY5R493xfZcK2+n6pjlhq4zFz8Yn8K6c6+5xSlf6fUcD+opOuWrJebpRLY/V9oSOil35taRF7juJ91w8tJe4eXeGkNY9LtL9R8Zv1A1NqLLailv1a7p8RZ7NeOSSF0UBcWARRR7cGeD5eS4jwu2GPXXws4Mf/AEpf+Ja3K47qdZuY+fH5rAVYq8xdbBpve6aHbzG0k+7uQ07j8FzpzmO6XSw20ZTU6mMfj+ig6LaW6OwTT6jGVR/+pqq1ytq7TNy3kZ+oGNjguXnO7WTf9HG2SaXsRM3I4e6IWePrbykeQ3a1aMcXaOwTj6nGVSf/ALTVr9T9NdI6wuuyOcxxmtGOrE2USEFja9kWIuPwBEoBJ23I8ei5nfzfc5YTlpx2evzpSze6u1L8ORp6v03ktOZfMNAvhhc6tBJHHBHFE1m4fvOXHf4Hy7xurFWuZ2xiYMjc1fiubc/BFedQeXxAbsh9kZuSWEz8A4H5nem6z43pPpfH2chI912zWuyOdHSlnPs9ZrmcC2Ng2DRs6QD8A8j8FAqdCOntKQSV6t9pbMLA2uvAD+4ZS7YHbd0ji8k+SSrMw8o0tXv/AKy1FKjr7MZG5Q09qLDwacZaZPFJDL3rFqNwbLIwD6reby8Oc7fdsgLV0uthadKFtWi+WvCzfjHC4MaNzudgBt6krUVun2naWoq+p6TbVa1WfYc2OGcthcJooYnNdGPBaG14i0fskbj1Ksw9Uyyvo9NHQ7HPLr+X5L900rQU9DYqnVjDIYI3xRtHo1rZHAD8grOq/oJnb0nRZv6d3/8Ao5WBdKOj4PU/nn5iIirAREQEREBERAREQEREBERAVE65Y99/pNqrhIGezYe9YO49Q2tL4/71e1Uer33Ta1/d3Jfy0iC3IiICIiAiIgIiICIiAiIgIiICIiAodj7U/wAFMUOx9qf4KwksaIiyYiIiAiIgIiIKVr7RWZ1Jk8VlcNk4IfYmTwWq00ZLbEcnAtc1w8texzPHqC2R+434kaZvTbPn61ioP4krpyLd0OI7nbYej08qj7mlr8O225z9JqY3PzlynI9J8xlKFnG2blYQ2onQycHOa7i4bHYjyDsfVV6v0jzGc0pXwWntdQRMxcn6vluV2cpTJWJjex2/1XB7AT/a38Cu7rjOBvay0jjMrb0ho2tk4LGczTvZYLDY5ppBkHxidwd9YNYxrNgR4AJ33AHluOKbmZvOesVNRzr7nS4dwnQnTmNKonHKJxiZjs3Uxfvcrrz6Q2w6ZahjHGO5QDfJ2DXf70HTXUQ+tapH+7ktpi9VdRslkrdR+jqEMFeKNzLLbvdhll5vbLCHDYh8Z4b7t8lkg8EjabezeucbiLeQlwuPmsRPcYq7ZuHcB4mOJrnHZzySYgfALyCAB4PpHHN1Ecspr5eTXy+zGhOVZdm/+8d/3oGjun2VwuqX6kymSgeyKjJTq1oGEbd18b5HSEnyQYWBu23hzt9/CvyoWo73VPI0NKHS9Cri5rc0E2efac1xqxFnvxMYQeR5H1BG3EevLxa8M7LRUS/UNiA2TJsTG0MjHo0BgPnYu3I3JPvAblamrus9zqTlqXM/FsYcPx2ehHYyxrnyibnr++/8myXw+fC+oo8nDqukerOksfR0zjNLY/UVfF1IKrMnLmG1H2uDA3m6Ltu4uO3nY7b7kbDwsLH9YpMlLimdLsYZ4YY53j+kjNuD3Pa3/M/jG5d3Wnhj46uty7faY6u3f8eMs3/EtbLQwiY5O1ocV3U45R2ukcuUfGHHMrZ6x4iCOxZ6VY5zZbMFVoZqRhPOWRsbf8z6cnDc/AbqV7L1s/8AdXiv+0zP/IV96oZXUOJxuLu4DG1L0NbLV7GXZO8tfFjo+T5po/gZGFrCAfB2IVxhmjsRtlidya4bg7bf9x9FfV9N5+2t5/uj8IcTFTrXt91mK/7TM/8AJWSHH9ZZpWRS9OcTWa9waZnagbIIgf2i0RAu29dgRv8Aiu1or6vp/BJ41vJ/zfSGLSuMnw2nMdi7dltmxWrsjnna3iJZdvfeG/Dk7c7fDdbVY6/2Lf4/4rIvTo5t3zkREQEREBERAREQEREBERAREQFUer33Ta1/d3Jfy0ityqPV77pta/u7kv5aRBbkREBERAREQEREBERAREQEREBERAUOx9qf4KYodj7U/wAFYSWNERZMRERAREQERaWnrPTGQu5ejUzEMj8C0HIybOEFf6+4dMR2y5vbdzaHEs294N3G4bpFHfeqs9mJl5Ntu4QuY0va48C/fcAgDZp8nYeg33IB12V1fp3CYHKanymSbBisLC+xdtGN5ZHE2MSueNgebQxwO7dx6j1BCDcrkHTfTsGtMcNdUtQ5mlIMlqGvUEbu21tefJyuPuEevuAkkbhwHyN2vlTqFpK9rK5oCnkZps5j2h1uBlOcxwbxxyBr5uHaDiyWNwaX8tng7LS1OqGkIsxhtO6cpTzwZfP5HBusxQGGvBerx3JrLTyALj3KkzSWgtLtzyPx8s8IzyiZ6f2bm33U7fSyxw/mmY5+FZRMVXfcfVM0X02raKtXbFTUWXtx5F8k9mCxMDG+zI7lJOAB7rnOLidvHkePAUXO9LBnaYrSaxzlZznRSyvrytYJJoi10MoZtxa5jo43AtA34NB3CsWU1lpTC3osZlNRY+ven9nMdV1hvfc2ewytE8Rg8uDppY4w7bjyeBuoWv8AqRorpdhWah15nGYrHyTGBkroZJeTxFJKQGxtc7xHFI8nbYNY4nYBJ0cJiqWOIbmM51Iy5z4R+jV5npXFnakFG/qvLywQlpLJXMlDyywJmE8gfIDRHv8AFo2O6x3Ok/6yr1aeU13qa3Xrhgkifc2bPwPJpft6nue/v6nYN+qAFl0f1g0trTUeS0vQq5Sndx1h9dov1eyLRZDBM50Q3LthFarO99rDtK3xuCBeVPQ6fwZe0t1y9/pz6R+iu6a01ksBkbr59Q3sjRlgrsrx253Svjkbz7jiXH9rdn92ysSIvTHGMYqGrq6uWtl28+otbJIxmo4Iv25qUrv4Mkj/AONbJVPKDNU+oFPN3JaNfTNTB22zWZJAx8Vl08J2eXHbgWN3B8bFjt/ULHPlEfOHptoiZyuYj3Z6/Lp82u1LXvP0RqnEsNK1Vv1stBHPTkLTXfJ3DxlLnEbhz3BzgRxI9PPiy6ZxdvGV7hulplt3p7Pg7+653jc/3bbD4N4t3O25rdrph0o1Xfs5y1icdlH5APM/03cimD+BO7QeJG8YcP7S4/tFbyjU0foevemjtU8dHctSXrctiyG8pXndznOefHwAHoAAB6LNrLAixVrVa7Xjt07EU8ErQ6OWJ4cx7T6EEeCFlVEyv9i3+P8Aisix1/sW/wAf8VkWEs4EREBERAREQEREBERAREQEREBVHq9902tf3dyX8tIrcqj1e+6bWv7u5L+WkQW5ERAREQEREBERAREQEREBERAREQFDsfan+CmKHY+1P8FYSWNERZMRERAREQFxnUn6KPTDP5K5n68mYoZm1dF9t1l58xgmFmWzsyOUujEZlnkc6Ljwd7oc0hjduzIoOR4b9GDpfi8bapWK+Qtz5DEyYW5YFt9fuVZAwPYyOEtjhBbHG3aNrRs0AeFa6fSbROP0xqXRtTHztxGrHWDka5tSOHGauyu9kZJ+iYIo2Na1mwbt4AVxRBWMT09wWI1O/V0U+QnyD4JoOVi2+QbSisJCQT5JFOvtv9Xi7bbm7fTZDoV04yOLfiXYmxXjlztvUkklW5LDM/IWROJZHSMcHEEWZWhu+wBAA2aF0BEHI8h+i/0vyjcfUyEOTsUKMFetJVmvSSe2w17T7VeGxI4mSWGOWRzhE53D6oIPFu1m1P0b0DrHpjF0f1BjJ7GmYKlWlHXFqRsrYq/ERDug89xwbud9z539SrsiUKJiOjekcR1EyPU+Ns8ucyVh9h87yA5vOvFXMYcAHGLt14foyS3lG123IAq9oiAiIqC0+BNLM4yrqJ1Jomy1KGR/cAc4ROBkbETt5De44D+8rcLW4KST2aenO+Iy07U0JZENhHHyLoW7fj2XRfmgh5nI5/FF7cHpGPIxsa0t43Gwk7slJGxadtnMib6+e7v+zsdk17bc8tS1inhrGg9yRrHRP328N87/AJgei95GjUydKWhepxWq87SyWGUbse38CPivtGpBRrNqVasVaGPcMjj+qAfP4ePJKgi3sPBJ/XaEMEGRggdDWnLTswEtdwPEglhcxnJoI3AUujcr5GlXyFSTnBaiZNE75mOAIP5FY8hd9midFXdC+7JG91aB7+PccNh8NzxBc3cgHYFZaVSChTgo1YxHDWibDGwejWtAAH5BBsq/2Lf4/wCKyLHX+xb/AB/xWRYyzgREQEREBERAREQEREBERAREQFUer33Ta1/d3Jfy0ityqPV77pta/u7kv5aRBbkREBERAREQEREBERAREQEREBERAUSdjjKSGk/wUtEEDg/5HfknB/yO/JT0VtKQOD/kd+ScH/I78lPRLKQOD/kd+ScH/I78lPRLKQOD/kd+ScH/ACO/JT0SykDg/wCR35Jwf8jvyU9EspA4P+R35Jwf8jvyU9EspA4P+R35Jwf8jvyU9EspA4P+R35Jwf8AI78lPRLKQOD/AJHfknB/yO/JT0SykDg/5HfkqvkND18rq12bs0K7K7qJqWeMXv3w5wJjl+BjbwYR433LhvxLg67ollKWOmGgG2GW26IxAnj+pIKTOTf7jstlb0pgL9Q0LuBqT1nDiYpIA5hH4bEKxIllNNjcNj8PWZTxWNhqQRjZkcMQa1o8DwB6eg/JSuD/AJHfkp6JZTHACImgjY+f8VkRFFEREBERAREQEREBERAREQEREBVHq9902tf3dyX8tIrcqj1e+6bWv7u5L+WkQW5ERAREQEREBERAREQEREBERAREQFU9W6RymYsQ38LqDJULTLdKR/byM0cXZjsxPlb2huw8o2yDy33i7i73SVbFX85b1iyxNBgcdV7TRC6KxK0S8ie6ZWGPux7AcYgH8j5k+qQ0lBCpN6pNxznZB+m3335xpa2HvNhixHeaCNyC59gwhzv2W83AfVHmPncd1Fy2JsVK0+MgnfRybYy21Yh/rDw5lLd8Wzg0MfvIWkOa8NLPIBUCC91odmKJtYTEtrR2pobBjftFNUdaiaybYyF8czYGzP4Dm3ctG/vntbDN2ercdW1+oaOnn3H2y2m2cSGuyqDIA6V4e1/cIETiGsIBcW+8N3gJ7W6+kzUcUoxUWIbC4ySMne6y+b2iMtAb2w1sfZEoPku3LRv4Lj5xNfqA3KVXZi3iRjme29+OvzdIdpmtpgOePIMHJ0m537u3Elh2ETE3upNzN2nZjD1KWKbaBotYW98w9mbl3yJXNJ7hg24bftb+PKwY7K9V57bpMlp7H1qbrzGtjYGmdlQV283F3fLXPdYLgPADY27kOcQg9vh6wvkDI7mmYow2t9IWzPdybx7+7dgCHnlx2I4jffkSCzYyQ9QP1HW7VvEfrqKF0czi14qTS78Wy8dubW7fSdsO/wBDmftBGnyPUhl+zFHhaD6jReMMrWgvOwZ7IOJmbuSe7z3LQdmbFvkrDgbHVKOw2nnKGMlrNghBtiTaR0gqDuntg7ebG3EbgFvc3LeLBIEqPH68mpz1MvkMbZbM9kbjWdLUeIjJIyRzZGEuY4Q9mQbeTIJW8mtc0s1dKp1sZA43snpd8opWHRsjjmDfaxFG2Br3EEuiLxK55GzvLdvHgQcnnet0d4Y3FYHCPlswX568lmtKIIzHO1sDJpWTO4kxODjs3d7jsAxrXuburF3qjJUyE1DE4iKf214oRWQTvV9kPDulkpHP2rblsdhFvsC5BNjx+sDmq8ti1SONr5Avaxk8wk9m9kc33vg95ne7dryWBjWEAPG61UsHWdmPaatzS8l5tGs/aZs3addMjvaWktAPYEZHb29/kByO24dly0vU+TJ4OXGQUYqLJIZcizttMj29myJYzvLs3dxqFvEu4uDyXPaNne7V7qi3C4ySpicS/Jva92Sa9pEce0jQBC3ve8eBeRycA7iNzHvsA1dbB9YoJJ+5n8PNDXyhmote+XuSVHNm5id4bxc/eVga0M4N7TDsdtjZY6mroM7NPFLQdjLGSMr2ySyukFT2JjAGA7tY/wBpZvxbs3gS767jvF9q6ktyUHKjh34423MmLWvFgQGWQNc1pk4btY2Ek8ve7ryGgxhknqKPXFmrpi1esezWw7bO16ccIhIdXeSW9wvcA2YRgcX7kF2+/jYPU1bqDLUpFmQxcNhlva01jSWyVhcY4ODi07P9lY9pHEAvl3BaGjfWQRdZx7f7TY0sf6kw0O2Zhtc9mAeJt2neI2HOI48XBkbdy4vIbl05a6oQ6eadRUKFjK1sdIeEbBGy1aEjhEC/vO4bxtby90jd/IEbdsTIpeosmRsFzcTHSbMWxRvqO5uYXlod3G2CDxHGTyxvIe57h94Bifi9dvycNmS/QfDWubxnvzML6/sMjQZGM2Y6T2qTcgjj2w07c2t2jYip1bkzNX+kl3T36qZDDLJ7AZmT+0tjiEjTyBa+JzjOQPdcOMfkglom0LPUJ2LE2Qq4/wBtNioDFHX7YbEZwLHnvvBIhJ4nceW77HfgNPRt9Z48Pjm26GHffibL7bvX3bMB3u1xIsjg8hkAd4c3eQuG2xY0Nxbi6kTRZB9C3hoH8y7Htmje4FvY3DZtv/jgD3f80Xft7OEOKPrALuPfLPpn2P2OM5CP6Yym5zcXiF/EAQhvBoDmlxG+7gRyM29ktfRZy5BR0/UmxUcUYrzukaJXyduw57g3ue80OFWMNPAkulJcGhpMOabqdLh7HdqVhciuvihNJ8dd89Y1dmycZe8yNzbLtyOTvo4+Wzie04NzawmRyEWK9rycrLFCWOSaSCxLE2xxDeXJkbmg8iD7ruTRv6H0WvqxdRq9pz7cmJsVGsleyKKR7JOTZJO1GXOY7cPi7HJ3gtf3frBzeGG9b6oNy1b9X08W7HOyYZYbLU+lZR9/dzXizs554s2JaPEm5Zu0g+NN2ep08ToNWU6sAdja5E1COMTMuFkvfA5yvYQ0thLDxAJedxsEGXLUepRyN21hMliuyZLIpxWi/i2I0oxEHBrfJFtr3E7k9t52P7IxZLD9RclLgXy5ShEaUveyPsk81eOcnH2YnMDAC4t9plikAc/w2Np+s3d3vGWeos2Vls5CgK2Odj43MrOdC+VtoGd0reTTsRs+o1hPg9qfkG7sLoeRudW8VFWhwuLqZuSR9qWxLdMdfiOwHwRNDH+6DMTGXbPIa0u947cgmYmr1XNXHfr/ACWB9p7ML7/sLHtZ3/a2ulZHzDj2xX5saT7xcdzt6i5Kiz/5UpHPHbpCGHUFh8YYwB8uLFSR8Id9L7z/AGvttOzmAsADgAXE2TTkmpJKROp2VG2wIh/VoixhPZjMhAMjzt3TIB5GwAHn6xDbIiICIiAiIgIiICIiAiIgIiICIiAqj1e+6bWv7u5L+WkVuVR6vfdNrX93cl/LSILciIgIiICIiAiIgIiICIiAiIgIiICoOrtfZjAZuxjadam+KJsBaZWPLvfgtyHfZw+Ndm39hd/ZsRBTdV9XNYUellXVNGSpBkMu6GISNg5Cp3cL7WXRNcSCWyjcdzmNiQQV6w/W7VeQ11q7TE2PxLauA1DiMTWe2KQSPhtWTFI55MmxcGjdpAAB9QfREQba91Y1FVizb46WOJxuZyOOi3jk96OvQs2GF3v+XF8DASNhxLtgDsR46d9S9T5jTmn5cs+tasXdGy52xO6Hg99lj2NHhhDQ0hxJAaPPoR6IiCtaO/SA1lqHUVHEXcZhmQ2YoXvdFDKHAvv0q52JkI+pZkPp9YN+AIODM9etcOdTxlWHF1HZLE27BsxV3GWCVmOu2GujD3OZ4krR+HtcNi4EHfwRBdNM9U9Q5rVlPA2qeOZXsZvKY1zo43h4irVo5WEEvI5FzyCdttttgPVVxnXbVzsRpm+cdiO5ms1icdYHZl4titX7deQs+k8ODKzC0ncAl24IIAIgl2uteqoMLdyTMfijLXjtOaDFJxJibkS3f6T/AOSi3/5z/TcbQrfW7WOLwcl3sY2zMMdPkQ6aB3h36ylgbHsxzfcbG1oH7XjcuJ33IgvWpdT52j1Jx+mad7tUbWIORe0RMLhJFerRFoJB917LLg74+4wtLTy5VzR3WHU2ode4vS12jjGVL2Pq2pHxRSCQPko99wBLyNufgeD4/t8oiDU1uuGsJNX5HEvq4s1q9O9LGzsv3D6+ffj2nfnueUOxd8ObQRxG7TYOpXVfUWjTk/1ZSx0vsRtCPvxyO37VWvM3fi8ftTOB/sA9PJJEGuxvWjVNzJYqnLQxQZehxckhbFJuDZNQSbe/8PaH7evo3ffzvkZ1k1O606A0MXxGSvU9+1Jvwhu34Gn6/qW1IyfhuXeACACIIs3W7Vceiq+pG4/E+0ywVJXMMUnb3ldcDth3N9v6szbz8Xeu42r2m+vuu7uSoC43GSxZHRNfUTojXIbFaflfZnNYQ4Hh2pANnFx3jYd/rciINz1V63aw0hpoZTD1sYJxRzF36WF7gXUstSqxs25/VfFYk5/HfYtLdlZb/UbUWHweqro9ltTYWTnWdPEfIkyVqAMcGFu4bHEwN22PjdxcSiINbmuqeqma20np+q6pXq5LPGnaLId3yQjG17HHdxO30lhx3AB2Ywb/AFuUPUvWTVuJ6df09qxY/wBqhxOZsmo6FxrSS18hXgic4cu54Y9+4DwCXE/AbEQbKXq9qVmG1XkRRxncwWIy9+uDFJxfJVv268Yf7/lpZXYXbbEuLtiBsB8s9XtSw6xn082jjDWjt1IA4xSc+MuZfRcd+e2/aaHDx9fz5HuoiDryIiAiIgIiICIiAiIgIiICIiAiIgKo9YAT0m1rs4jbT2S9Pj/VpERB/9k='
    asyncio.run(send_data_to_gemini(image_data))

I am not sure what is the issue but it keeps on telling me that it is unable to see any image. can someone tell me what is wrong with this code?

3

Hello,
Emin değilim fakat aşağıdaki gibi deneyebilirsen belki düzeltme sağlanabilir.

1. Asenkron Fonksiyonun Çalıştırılması

async def send_data_to_gemini fonksiyonu asenkron bir fonksiyon, ancak if __name__ == "__main__": bloğunda asenkron fonksiyon doğrudan çağrılıyor. Asenkron fonksiyonlar await veya asyncio.run() gibi yöntemlerle çağrılmalıdır.

2. Base64 Görüntü Verisinin Tanımlanması

image_data değişkeninizin içinde bir Base64 kodu var, ancak doğru bir JPEG görüntü içermeyebilir. Eğer bu kod eksikse veya hatalı bir Base64 dizesi ise, base64.b64decode(base64_image_data) işlemi hata verecektir. Bu kodun doğruluğunu kontrol edin.

3. session.receive() Fonksiyonu Eksik Bekleme

turn = session.receive() satırında, await eksik. Asenkron bir metod çağrısında bekleme yapılması gerekiyor.

Kodunuzu gözden geçirirken birkaç potansiyel hata ve eksiklik fark ettim:

1. Asenkron Fonksiyonun Çalıştırılması

async def send_data_to_gemini fonksiyonu asenkron bir fonksiyon, ancak if __name__ == "__main__": bloğunda asenkron fonksiyon doğrudan çağrılıyor. Asenkron fonksiyonlar await veya asyncio.run() gibi yöntemlerle çağrılmalıdır.

Düzeltme:

python

KopyalaDüzenle

if __name__ == "__main__":
    image_data = '/9j/4AAQSkZJRg...'  # Base64 görüntü verisi burada olmalı
    asyncio.run(send_data_to_gemini(image_data))

2. Base64 Görüntü Verisinin Tanımlanması

image_data değişkeninizin içinde bir Base64 kodu var, ancak doğru bir JPEG görüntü içermeyebilir. Eğer bu kod eksikse veya hatalı bir Base64 dizesi ise, base64.b64decode(base64_image_data) işlemi hata verecektir. Bu kodun doğruluğunu kontrol edin.

3. session.receive() Fonksiyonu Eksik Bekleme

turn = session.receive() satırında, await eksik. Asenkron bir metod çağrısında bekleme yapılması gerekiyor.

Düzeltme:

python

KopyalaDüzenle

turn = await session.receive()

4. Yanıt Parçalarının Birleştirilmesi

Yanıt parçalarını birleştirme işlemi doğru yapılmamış. Düzgün bir şekilde birleştirmek için bir liste yerine doğrudan string ile çalışabilirsiniz.

5. Yorumlu Kodlar

Kod içinde yorumlanmış satırlar (content1 gibi) var. Eğer bu kısım kullanılmayacaksa tamamen kaldırabilirsiniz, yoksa hangi yöntemin kullanılacağını netleştirmeniz iyi olur.

6. HTTP Seçeneklerinde Hata

http_options={'api_version': 'v1alpha'} kısmı doğru gibi görünüyor, ancak bu yapılandırmanın kullanılabilir bir seçenek olduğunu belgelemeden kontrol etmelisiniz.

5

Thanks for your reply. It will be really helpful if you can post your reply in english.