Update app.py

app.py

@@ -10,14 +10,13 @@ import pandas as pd
 import nltk
 from nltk.sentiment import SentimentIntensityAnalyzer
 from textblob import TextBlob
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM, PretrainedConfig
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM, LlamaConfig
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 
 # --- Monkey Patch for Gradio Client JSON Schema Bug ---
 import gradio_client.utils as client_utils
 
-# Patch get_type to check for non-dict types.
 original_get_type = client_utils.get_type
 def patched_get_type(schema):
     if not isinstance(schema, dict):
@@ -25,7 +24,6 @@ def patched_get_type(schema):
     return original_get_type(schema)
 client_utils.get_type = patched_get_type
 
-# Additionally, patch _json_schema_to_python_type to handle boolean schemas.
 if not hasattr(client_utils, "_original_json_schema_to_python_type"):
     client_utils._original_json_schema_to_python_type = client_utils._json_schema_to_python_type
 
@@ -111,8 +109,8 @@ class LLMResponder:
             self.llm = Llama(model_path="path/to/your/gguf/file.gguf", n_ctx=1024)
             self.backend = "llama_cpp"
         else:
-            # Create a dummy config to bypass the missing model_type in config.json.
-            dummy_config = PretrainedConfig.from_dict({"model_type": "llama"})
+            # Create a dummy config using LlamaConfig so that the model loads even if its config is missing a model_type key.
+            dummy_config = LlamaConfig.from_dict({"model_type": "llama"})
             self.llm_tokenizer = AutoTokenizer.from_pretrained(model_name, config=dummy_config, trust_remote_code=True)
             self.llm_model = AutoModelForCausalLM.from_pretrained(model_name, config=dummy_config, trust_remote_code=True)
             self.backend = "transformers"