Update app.py

app.py

@@ -1,21 +1,56 @@
 import streamlit as st
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import json
+import requests
+import os
 
 # Streamlit app configuration
 st.set_page_config(page_title="AI Chatbot", layout="centered")
 
+# Fix the model's configuration before loading
+def fix_model_config(model_name):
+    # Download the configuration file from the model repository
+    config_url = f"https://huggingface.co/{model_name}/resolve/main/config.json"
+    config_path = "config.json"
+    
+    if not os.path.exists(config_path):
+        response = requests.get(config_url)
+        response.raise_for_status()  # Raise an error if the request fails
+        with open(config_path, "w") as f:
+            f.write(response.text)
+    
+    # Load the configuration and modify rope_scaling if necessary
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    
+    if "rope_scaling" in config:
+        config["rope_scaling"] = {
+            "type": "linear",  # Replace the problematic rope_scaling type
+            "factor": config["rope_scaling"].get("factor", 1.0)
+        }
+    
+    # Save the modified configuration
+    with open(config_path, "w") as f:
+        json.dump(config, f)
+    
+    return config_path
+
 # Load the model pipeline
 @st.cache_resource
 def load_pipeline():
     model_name = "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2"
     
+    # Fix the model configuration
+    fixed_config_path = fix_model_config(model_name)
+    
     # Load tokenizer and model
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(
         model_name,
-        device_map="auto",  # Use GPU if available
-        rope_scaling=None   # Avoid issues with rope_scaling
+        config=fixed_config_path,
+        device_map="auto"  # Use GPU if available
     )
+    
     return pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 pipe = load_pipeline()