text to video

app.py

@@ -115,7 +115,8 @@ with gr.Blocks() as demo:
             openai_api_key = gr.Textbox(
                 show_label=False,
                 placeholder="Set your OpenAI API key here and press Enter",
-                lines=1
+                lines=1,
+                type="password"
             ).style(container=False)
         with gr.Column(scale=0.15, min_width=0):
             btn1 = gr.Button("Submit").style(full_height=True)
@@ -125,7 +126,8 @@ with gr.Blocks() as demo:
             hugging_face_token = gr.Textbox(
                 show_label=False,
                 placeholder="Set your Hugging Face Token here and press Enter",
-                lines=1
+                lines=1,
+                type="password"
             ).style(container=False)
         with gr.Column(scale=0.15, min_width=0):
             btn3 = gr.Button("Submit").style(full_height=True)

awesome_chat.py

@@ -152,6 +152,8 @@ def send_request(data):
     
     response = requests.post(endpoint, json=data, headers=HEADER, proxies=PROXY)
     logger.debug(response.text.strip())
+    if "choices" not in response.json():
+        return response.json()
     if use_completion:
         return response.json()["choices"][0]["text"].strip()
     else:
@@ -576,14 +578,14 @@ def model_inference(model_id, data, hosted_on, task, huggingfacetoken=None):
         HUGGINGFACE_HEADERS = None
     if hosted_on == "unknown":
         r = status(model_id)
-        logger.debug("Local Server Status: " + str(r.json()))
-        if r.status_code == 200 and "loaded" in r.json() and r.json()["loaded"]:
+        logger.debug("Local Server Status: " + str(r))
+        if "loaded" in r and r["loaded"]:
             hosted_on = "local"
         else:
             huggingfaceStatusUrl = f"https://api-inference.huggingface.co/status/{model_id}"
             r = requests.get(huggingfaceStatusUrl, headers=HUGGINGFACE_HEADERS, proxies=PROXY)
             logger.debug("Huggingface Status: " + str(r.json()))
-            if r.status_code == 200 and "loaded" in r.json() and r.json()["loaded"]:
+            if "loaded" in r and r["loaded"]:
                 hosted_on = "huggingface"
     try:
         if hosted_on == "local":
@@ -603,7 +605,7 @@ def get_model_status(model_id, url, headers, queue = None):
         r = requests.get(url, headers=headers, proxies=PROXY)
     else:
         r = status(model_id)
-    if r.status_code == 200 and "loaded" in r.json() and r.json()["loaded"]:
+    if "loaded" in r and r["loaded"]:
         if queue:
             queue.put((model_id, True, endpoint_type))
         return True
@@ -836,6 +838,9 @@ def chat_huggingface(messages, openaikey = None, huggingfacetoken = None, return
     task_str = parse_task(context, input, openaikey).strip()
     logger.info(task_str)
 
+    if "error" in task_str:
+        return {"message": "You exceeded your current quota, please check your plan and billing details."}
+
     if task_str == "[]":  # using LLM response for empty task
         record_case(success=False, **{"input": input, "task": [], "reason": "task parsing fail: empty", "op": "chitchat"})
         response = chitchat(messages, openaikey)

config.gradio.yaml

@@ -8,7 +8,7 @@ log_file: logs/debug.log
 model: text-davinci-003 # text-davinci-003
 use_completion: true
 inference_mode: hybrid # local, huggingface or hybrid
-local_deployment: minimal # minimal, standard or full
+local_deployment: full # minimal, standard or full
 num_candidate_models: 5
 max_description_length: 100
 proxy: 

models_server.py

@@ -78,9 +78,9 @@ def load_pipes(local_deployment):
     if local_deployment in ["full"]:
         other_pipes = {
             "nlpconnect/vit-gpt2-image-captioning":{
-                "model": VisionEncoderDecoderModel.from_pretrained(f"nlpconnect/vit-gpt2-image-captioning"),
-                "feature_extractor": ViTImageProcessor.from_pretrained(f"nlpconnect/vit-gpt2-image-captioning"),
-                "tokenizer": AutoTokenizer.from_pretrained(f"nlpconnect/vit-gpt2-image-captioning"),
+                "model": VisionEncoderDecoderModel.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
+                "feature_extractor": ViTImageProcessor.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
+                "tokenizer": AutoTokenizer.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
                 "device": "cuda:0"
             },
             # "Salesforce/blip-image-captioning-large": {
@@ -89,7 +89,7 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "damo-vilab/text-to-video-ms-1.7b": {
-                "model": DiffusionPipeline.from_pretrained(f"damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"),
+                "model": DiffusionPipeline.from_pretrained(f"{local_models}damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"),
                 "device": "cuda:0"
             },
             # "facebook/maskformer-swin-large-ade": {
@@ -112,11 +112,11 @@ def load_pipes(local_deployment):
                 "device": "cuda:0"
             },
             "espnet/kan-bayashi_ljspeech_vits": {
-                "model": Text2Speech.from_pretrained(f"espnet/kan-bayashi_ljspeech_vits"),
+                "model": Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits"),
                 "device": "cuda:0"
             },
             "lambdalabs/sd-image-variations-diffusers": {
-                "model": DiffusionPipeline.from_pretrained(f"lambdalabs/sd-image-variations-diffusers"), #torch_dtype=torch.float16
+                "model": DiffusionPipeline.from_pretrained(f"{local_models}lambdalabs/sd-image-variations-diffusers"), #torch_dtype=torch.float16
                 "device": "cuda:0"
             },
             # "CompVis/stable-diffusion-v1-4": {
@@ -128,7 +128,7 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "runwayml/stable-diffusion-v1-5": {
-                "model": DiffusionPipeline.from_pretrained(f"runwayml/stable-diffusion-v1-5"),
+                "model": DiffusionPipeline.from_pretrained(f"{local_models}runwayml/stable-diffusion-v1-5"),
                 "device": "cuda:0"
             },
             # "microsoft/speecht5_tts":{
@@ -143,10 +143,10 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "microsoft/speecht5_vc":{
-                "processor": SpeechT5Processor.from_pretrained(f"microsoft/speecht5_vc"),
-                "model": SpeechT5ForSpeechToSpeech.from_pretrained(f"microsoft/speecht5_vc"),
-                "vocoder": SpeechT5HifiGan.from_pretrained(f"microsoft/speecht5_hifigan"),
-                "embeddings_dataset": load_dataset(f"Matthijs/cmu-arctic-xvectors", split="validation"),
+                "processor": SpeechT5Processor.from_pretrained(f"{local_models}microsoft/speecht5_vc"),
+                "model": SpeechT5ForSpeechToSpeech.from_pretrained(f"{local_models}microsoft/speecht5_vc"),
+                "vocoder": SpeechT5HifiGan.from_pretrained(f"{local_models}microsoft/speecht5_hifigan"),
+                "embeddings_dataset": load_dataset(f"{local_models}Matthijs/cmu-arctic-xvectors", split="validation"),
                 "device": "cuda:0"
             },
             # "julien-c/wine-quality": {
@@ -158,13 +158,13 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "facebook/maskformer-swin-base-coco": {
-                "feature_extractor": MaskFormerFeatureExtractor.from_pretrained(f"facebook/maskformer-swin-base-coco"),
-                "model": MaskFormerForInstanceSegmentation.from_pretrained(f"facebook/maskformer-swin-base-coco"),
+                "feature_extractor": MaskFormerFeatureExtractor.from_pretrained(f"{local_models}facebook/maskformer-swin-base-coco"),
+                "model": MaskFormerForInstanceSegmentation.from_pretrained(f"{local_models}facebook/maskformer-swin-base-coco"),
                 "device": "cuda:0"
             },
             "Intel/dpt-hybrid-midas": {
-                "model": DPTForDepthEstimation.from_pretrained(f"Intel/dpt-hybrid-midas", low_cpu_mem_usage=True),
-                "feature_extractor": DPTFeatureExtractor.from_pretrained(f"Intel/dpt-hybrid-midas"),
+                "model": DPTForDepthEstimation.from_pretrained(f"{local_models}Intel/dpt-hybrid-midas", low_cpu_mem_usage=True),
+                "feature_extractor": DPTFeatureExtractor.from_pretrained(f"{local_models}Intel/dpt-hybrid-midas"),
                 "device": "cuda:0"
             }
         }
@@ -176,15 +176,15 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "openai/whisper-base": {
-                "model": pipeline(task="automatic-speech-recognition", model=f"openai/whisper-base"), 
+                "model": pipeline(task="automatic-speech-recognition", model=f"{local_models}openai/whisper-base"), 
                 "device": "cuda:0"
             },
             "microsoft/speecht5_asr": {
-                "model": pipeline(task="automatic-speech-recognition", model=f"microsoft/speecht5_asr"), 
+                "model": pipeline(task="automatic-speech-recognition", model=f"{local_models}microsoft/speecht5_asr"), 
                 "device": "cuda:0"
             },
             "Intel/dpt-large": {
-                "model": pipeline(task="depth-estimation", model=f"Intel/dpt-large"), 
+                "model": pipeline(task="depth-estimation", model=f"{local_models}Intel/dpt-large"), 
                 "device": "cuda:0"
             },
             # "microsoft/beit-base-patch16-224-pt22k-ft22k": {
@@ -192,11 +192,11 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "facebook/detr-resnet-50-panoptic": {
-                "model": pipeline(task="image-segmentation", model=f"facebook/detr-resnet-50-panoptic"), 
+                "model": pipeline(task="image-segmentation", model=f"{local_models}facebook/detr-resnet-50-panoptic"), 
                 "device": "cuda:0"
             },
             "facebook/detr-resnet-101": {
-                "model": pipeline(task="object-detection", model=f"facebook/detr-resnet-101"), 
+                "model": pipeline(task="object-detection", model=f"{local_models}facebook/detr-resnet-101"), 
                 "device": "cuda:0"
             },
             # "openai/clip-vit-large-patch14": {
@@ -204,7 +204,7 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "google/owlvit-base-patch32": {
-                "model": pipeline(task="zero-shot-object-detection", model=f"google/owlvit-base-patch32"), 
+                "model": pipeline(task="zero-shot-object-detection", model=f"{local_models}google/owlvit-base-patch32"), 
                 "device": "cuda:0"
             },
             # "microsoft/DialoGPT-medium": {
@@ -248,15 +248,15 @@ def load_pipes(local_deployment):
             #     "device": "cuda:0"
             # },
             "impira/layoutlm-document-qa": {
-                "model": pipeline(task="document-question-answering", model=f"impira/layoutlm-document-qa"), 
+                "model": pipeline(task="document-question-answering", model=f"{local_models}impira/layoutlm-document-qa"), 
                 "device": "cuda:0"
             },
             "ydshieh/vit-gpt2-coco-en": {
-                "model": pipeline(task="image-to-text", model=f"ydshieh/vit-gpt2-coco-en"), 
+                "model": pipeline(task="image-to-text", model=f"{local_models}ydshieh/vit-gpt2-coco-en"), 
                 "device": "cuda:0"
             },
             "dandelin/vilt-b32-finetuned-vqa": {
-                "model": pipeline(task="visual-question-answering", model=f"dandelin/vilt-b32-finetuned-vqa"), 
+                "model": pipeline(task="visual-question-answering", model=f"{local_models}dandelin/vilt-b32-finetuned-vqa"), 
                 "device": "cuda:0"
             }
         }