update scripts

src/backend/model_operations.py

@@ -29,6 +29,7 @@ import src.envs as envs
 # import scipy
 from scipy.spatial.distance import jensenshannon
 import numpy as np
+import spacy_transformers
 
 
 
@@ -45,7 +46,7 @@ logging.basicConfig(level=logging.INFO,
 try:
     nlp1 = spacy.load("en_core_web_trf")
 except OSError:
-    print("Downloading language model for the spaCy 'en_core_web_sm'...")
+    print("Downloading language model for the spaCy 'en_core_web_trf'...")
     spacy.cli.download("en_core_web_trf")
     nlp = spacy.load("en_core_web_trf")
 
@@ -454,10 +455,10 @@ class SummaryGenerator:
                 # result = response['choices'][0]['message']['content']
                 # print(result)
                 from huggingface_hub import InferenceClient
-
+                print("token_for_request:",envs.TOKEN)
                 client = InferenceClient(self.model_id,api_key=envs.TOKEN,headers={"X-use-cache": "false"})
                 messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}]
-                outputs = client.chat_completion(messages, max_tokens=50)
+                # outputs = client.chat_completion(messages, max_tokens=50)
                 result = None
                 while result is None:
                     outputs = client.chat_completion(messages, max_tokens=50)

src/envs.py

@@ -6,6 +6,7 @@ from huggingface_hub import HfApi
 # replace this with our token
 # TOKEN = os.environ.get("HF_TOKEN", None)
 TOKEN = os.getenv("H4_TOKEN")
+print("token:", TOKEN)
 # print(TOKEN)
 # OWNER = "vectara"
 # REPO_ID = f"{OWNER}/Humanlike"