Update app.py

app.py

@@ -1,12 +1,17 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
 
 # Load the model and tokenizer from your Hugging Face Hub repository
 model_name = "abdulllah01/outputs"  # Replace with your Hugging Face repo name
 
-# Disable quantization explicitly if it's set in the config
+# Load the model configuration first and modify it if necessary
+config = AutoConfig.from_pretrained(model_name)
+if hasattr(config, 'quantization_config'):
+    config.quantization_config = None  # Disable any quantization settings
+
+# Load the model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map=None, quantization_config=None)
+model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
 
 # Streamlit interface
 st.title("Tech Support Chatbot")
@@ -16,7 +21,8 @@ st.write("Ask your technical support questions below:")
 user_input = st.text_input("Your question:", "")
 
 if user_input:
-    # Generate a response using the pipeline
-    response = model.generate(input_ids=tokenizer.encode(user_input, return_tensors="pt"), max_length=100)
+    # Generate a response using the model
+    inputs = tokenizer.encode(user_input, return_tensors="pt")
+    response = model.generate(inputs, max_length=100)
     answer = tokenizer.decode(response[0], skip_special_tokens=True)
     st.write("**Answer:**", answer)