Update app.py

app.py

@@ -1,4 +1,31 @@
-import time
+import os
+import subprocess
+
+def install_packages():
+    packages = [
+        "torch",
+        "transformers",
+        "huggingface-hub",
+        "gradio",
+        "accelerate",
+        "onnxruntime",
+        "onnxruntime-tools",
+        "optimum",
+    ]
+    for package in packages:
+        result = subprocess.run(f'pip install {package}', shell=True)
+        if result.returncode != 0:
+            print(f"Failed to install {package}")
+        else:
+            print(f"Successfully installed {package}")
+
+install_packages()
+
+import gradio as gr
+from huggingface_hub import login
+from optimum.onnxruntime import ORTModelForSeq2SeqLM
+from transformers import AutoTokenizer, pipeline
+
 from transformers import AutoTokenizer, pipeline
 from optimum.onnxruntime import ORTModelForSequenceClassification
 import gradio as gr
@@ -6,6 +33,12 @@ import gradio as gr
 
 # with gr.Blocks(css=css) as demo:#     box1 = gr.Textbox(value="Good Job", elem_classes="feedback")#     box2 = gr.Textbox(value="Failure", elem_id="warning", elem_classes="feedback")
 model_id = "HassamAliCADI/SentimentOnx"
+hf_token = os.environ.get("NLP")
+
+if hf_token:
+    login(hf_token)
+else:
+    print("NLP token not found.")
 
 model = ORTModelForSequenceClassification.from_pretrained(model_id)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
@@ -15,9 +48,9 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer)
 
 def classify_text(text):
-    start_time = time.time()
+  #  start_time = time.time()
     results = pipe(text)
-    end_time = time.time()
+ #   end_time = time.time()
 
 # with gr.Blocks(css=".gradio-container {background-color: red}") as demo:# with gr.Blocks(css=".gradio-container {background: url('file=clouds.jpg')}") as demo:# css = """