Update app.py

app.py

@@ -8,11 +8,11 @@ import torch
 # Load PEFT adapter configuration
 peft_config = PeftConfig.from_pretrained("unica/CLiMA")
 
-# Load base model
 base_model = AutoModelForCausalLM.from_pretrained(
     peft_config.base_model_name_or_path,
     torch_dtype=torch.float16,
-    device_map="auto"
+    device_map="auto",
+    offload_folder="./offload"  # Add this line
 )
 
 # Load adapter weights
@@ -45,8 +45,8 @@ demo = gr.Interface(
     title="Causal Relation Extractor with MedLlama",
     description="Paste your clinical note or drug review, and specify two target entities. This AI agent extracts drug-condition or symptom causal relations using a fine-tuned LLM adapter model.",
     examples=[
-        ["Patient reported severe headaches after starting amitriptyline.", "amitriptyline", "headaches"],
-        ["Lisinopril helped reduce the patient's blood pressure but caused persistent cough.", "Lisinopril", "cough"],
+        ["Odynophagia: Was presumed due to mucositis from recent chemotherapy.", "chemotherapy", "mucositis"],
+        ["patient's wife noticed erythema on patient's face. On [**3-27**]the visiting nurse [**First Name (Titles) 8706**][**Last Name (Titles)11282**]of a rash on his arms as well. The patient was noted to be febrile and was admitted to the [**Company 191**] Firm. In the EW, patient's Dilantin was discontinued and he was given Tegretol instead.", "Dilantin", "erythema on patient's face"],
         ["After using Metformin, the patient experienced gastrointestinal discomfort.", "Metformin", "gastrointestinal discomfort"]
     ]
 )