add curl examples

curl_example.md

@@ -0,0 +1,15 @@
+
+API Usage (based on CURL)
+```
+EVENT_ID=$(curl -s -X POST https://jony7chu-cispa-citizen-defake.hf.space/gradio_api/call/detect_api \
+  -H "Content-Type: application/json" \
+  -d '{
+    "data": [
+      {"path": "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "meta": {"_type": "gradio.FileData"}},
+      "API_KEY"
+    ]
+  }' | grep -o '"event_id":"[^"]*"' | cut -d'"' -f4) && \
+curl -N "https://jony7chu-cispa-citizen-defake.hf.space/gradio_api/call/detect_api/$EVENT_ID"
+```
+
+    

defake/blipmodels/med.py

@@ -947,6 +947,26 @@ class BertLMHeadModel(BertPreTrainedModel):
             "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
             "is_decoder": True,
         }
+    
+    # Fix the transformer conflicts (not working)
+    # def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, **model_kwargs):
+    #     input_shape = input_ids.shape
+    #     # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+    #     if attention_mask is None:
+    #         attention_mask = input_ids.new_ones(input_shape)
+
+    #     # cut decoder_input_ids if past is used
+    #     if past is not None:
+    #         input_ids = input_ids[:, -1:]
+
+    #     return {
+    #         "input_ids": input_ids, 
+    #         "attention_mask": attention_mask, 
+    #         "past_key_values": past,
+    #         "encoder_hidden_states": encoder_hidden_states,
+    #         "encoder_attention_mask": encoder_attention_mask,
+    #         "is_decoder": True,
+    #     }
 
     def _reorder_cache(self, past, beam_idx):
         reordered_past = ()

defake/test_api.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 import torch.nn as nn
 import argparse
 
-from blipmodels import blip_decoder
+from .blipmodels import blip_decoder
 
 class NeuralNet(nn.Module):
     def __init__(self, input_size, hidden_size_list, num_classes):