Parsing GCP PredictionResponse type data

Question

Below is the response from GCP Natural Language custom model for classification. I have made a dictionary that has the gcs_path (file name) as key and payload is the prediction from the model, stored as the value. Trying to extract (parse) the gcs_path (key) for the class (display_name) which is HIC. Therefore for the below response, trying to get gs://talem_mapfre_ocr/invoice-doc0.pdf as the output. I have tried to convert this to json format, but the payload format is not json serializable. Any leads would be appreciated.

{'gs://talem_mapfre_ocr/invoice-doc0.pdf': payload {
      annotation_spec_id: "2184948407204839424"
      classification {
        score: 0.9999998807907104
      }
      display_name: "HIC"
    }
    payload {
      annotation_spec_id: "6796634425632227328"
      classification {
        score: 1.0604375688672008e-07
      }
      display_name: "Not_HIC"
    }
    preprocessed_input {
      document {
        document_text {
        }
        page_count: 1
      }
    }
    , 'gs://talem_mapfre_ocr/invoice-doc1.pdf': payload {
      annotation_spec_id: "6796634425632227328"
      classification {
        score: 1.0
      }
      display_name: "Not_HIC"
    }
    payload {
      annotation_spec_id: "2184948407204839424"
      classification {
        score: 2.7873853178801136e-16
      }
      display_name: "HIC"
    }
    preprocessed_input {
      document {
        document_text {
        }
        page_count: 1
      }
    }
    , 'gs://talem_mapfre_ocr/invoice-doc2.pdf': payload {
      annotation_spec_id: "6796634425632227328"
      classification {
        score: 1.0
      }
      display_name: "Not_HIC"
    }
    payload {
      annotation_spec_id: "2184948407204839424"
      classification {
        score: 8.088715808263203e-10
      }
      display_name: "HIC"
    }
    preprocessed_input {
      document {
        document_text {
        }
        page_count: 1
      }
    }
    , 'gs://talem_mapfre_ocr/invoice-doc3.pdf': payload {
      annotation_spec_id: "6796634425632227328"
      classification {
        score: 1.0
      }
      display_name: "Not_HIC"
    }
    payload {
      annotation_spec_id: "2184948407204839424"
      classification {
        score: 1.4505006917797658e-10
      }
      display_name: "HIC"
    }
    preprocessed_input {
      document {
        document_text {
        }
        page_count: 1
      }
    }
    , 'gs://talem_mapfre_ocr/single_file_for_splitting.pdf': payload {
      annotation_spec_id: "6796634425632227328"
      classification {
        score: 0.999998927116394
      }
      display_name: "Not_HIC"
    }
    payload {
      annotation_spec_id: "2184948407204839424"
      classification {
        score: 1.0924828757197247e-06
      }
      display_name: "HIC"
    }
    preprocessed_input {
      document {
        document_text {
        }
        page_count: 4
      }
    }
    }

Here is the code for getting the result (response):

def pdf_payload(file_path):
    return {'document': {'input_config': {'gcs_source': {'input_uris': [file_path] } } } }

def get_prediction(file_path, model_name):
    options = ClientOptions(api_endpoint='automl.googleapis.com')
    prediction_client = automl_v1.PredictionServiceClient(client_options=options)

    #payload = inline_text_payload(file_path)
    # Uncomment the following line (and comment the above line) if want to predict on PDFs.
    payload = pdf_payload(file_path)

    params = {}

    request = prediction_client.predict(name=model_name, payload=payload)

    return request  # waits until request is returned


pdf_single_pages = glob.glob("./*.pdf")
list_of_pages = []
for single_page in pdf_single_pages:
     path_split = PurePath(single_page).parts
     pagename, ext = os.path.splitext(path_split[-1])
     pdf = (pagename+'.pdf')
     list_of_pages.append(pdf)
     

for i in list_of_pages:
    new_path = os.path.join(file_path,i)
    gcs_paths.append(new_path)
    result = get_prediction(new_path, model_name)
    json_results.append(result)
 
 
dict_json = {k: v for k, v in zip(gcs_paths, json_results)}