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-rw-r--r--g4f/Provider/DeepInfra.py2
-rw-r--r--g4f/client/async_client.py5
-rw-r--r--g4f/client/client.py7
3 files changed, 9 insertions, 5 deletions
diff --git a/g4f/Provider/DeepInfra.py b/g4f/Provider/DeepInfra.py
index 763b960a..f3e31962 100644
--- a/g4f/Provider/DeepInfra.py
+++ b/g4f/Provider/DeepInfra.py
@@ -11,7 +11,7 @@ class DeepInfra(Openai):
needs_auth = True
supports_stream = True
supports_message_history = True
- default_model = "meta-llama/Meta-Llama-3-70b-instruct"
+ default_model = "meta-llama/Meta-Llama-3-70B-Instruct"
default_vision_model = "llava-hf/llava-1.5-7b-hf"
model_aliases = {
'dbrx-instruct': 'databricks/dbrx-instruct',
diff --git a/g4f/client/async_client.py b/g4f/client/async_client.py
index a97b4d7a..dbfa6b70 100644
--- a/g4f/client/async_client.py
+++ b/g4f/client/async_client.py
@@ -184,9 +184,10 @@ async def iter_image_response(
return ImagesResponse([Image(None, image, chunk.alt) for image in chunk.get_list()], int(time.time()))
def create_image(provider: ProviderType, prompt: str, model: str = "", **kwargs) -> AsyncIterator:
- prompt = f"create a image with: {prompt}"
- if provider.__name__ == "You":
+ if isinstance(provider, type) and provider.__name__ == "You":
kwargs["chat_mode"] = "create"
+ else:
+ prompt = f"create a image with: {prompt}"
return provider.create_async_generator(
model,
[{"role": "user", "content": prompt}],
diff --git a/g4f/client/client.py b/g4f/client/client.py
index 1b090981..acf53c70 100644
--- a/g4f/client/client.py
+++ b/g4f/client/client.py
@@ -125,9 +125,12 @@ def iter_image_response(response: Iterator) -> Union[ImagesResponse, None]:
return ImagesResponse([Image(image) for image in chunk.get_list()])
def create_image(client: Client, provider: ProviderType, prompt: str, model: str = "", **kwargs) -> Iterator:
- prompt = f"create a image with: {prompt}"
- if provider.__name__ == "You":
+
+
+ if isinstance(provider, type) and provider.__name__ == "You":
kwargs["chat_mode"] = "create"
+ else:
+ prompt = f"create a image with: {prompt}"
return provider.create_completion(
model,
[{"role": "user", "content": prompt}],