Omit Text generatos cause it takes ages

app.py

@@ -38,7 +38,7 @@ show_pages(
             Page("pages/Text to Image.py", "Text to Image",":lower_left_paintbrush:"),
             Page("pages/Text Classification.py",'Text Classification',":book:"),
             Page("pages/Image to text.py","Image to Text",":camera:"),
-            Page("pages/Text Generation.py", "Text Generation", ":printer:"),
+            #Page("pages/Text Generation.py", "Text Generation", ":printer:"),
         ]
     )
 

pages/Text Generation.py

@@ -1,32 +1,32 @@
-import streamlit as st
-from PIL import Image
-import base64
-import transformers
+# import streamlit as st
+# from PIL import Image
+# import base64
+# import transformers
 
 
 
-model_name = 'Intel/neural-chat-7b-v3-1'
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+# model_name = 'Intel/neural-chat-7b-v3-1'
+# model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+# tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 
-def generate_response(system_input, user_input):
+# def generate_response(system_input, user_input):
 
-    # Format the input using the provided template
-    prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"
+#     # Format the input using the provided template
+#     prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"
 
-    # Tokenize and encode the prompt
-    inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)
+#     # Tokenize and encode the prompt
+#     inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)
 
-    # Generate a response
-    outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+#     # Generate a response
+#     outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
+#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    # Extract only the assistant's response
-    return response.split("### Assistant:\n")[-1]
+#     # Extract only the assistant's response
+#     return response.split("### Assistant:\n")[-1]
 
 
-# Example usage
-system_input = "You are a employee in the customer succes department of a company called Retraced that works in sustainability and traceability"
-prompt = st.text_input(str("Insert here you prompt?"))
-response = generate_response(system_input, prompt)
-st.write(response)
+# # Example usage
+# system_input = "You are a employee in the customer succes department of a company called Retraced that works in sustainability and traceability"
+# prompt = st.text_input(str("Insert here you prompt?"))
+# response = generate_response(system_input, prompt)
+# st.write(response)