Update app.py

app.py

@@ -1,41 +1,29 @@
-import requests
 import gradio as gr
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch.nn.functional as F
 
-# API endpoints (replace with actual API endpoints)
-ICD_API_URL = "https://icd10api.com/"
-CPT_API_URL = "https://api.ama-assn.org/cpt/codes"  # Example, requires AMA license
+# Mock ICD and CPT data (replace with actual API calls or datasets)
+def fetch_icd_codes(query):
+    # Mock ICD codes for demonstration
+    return [
+        {"code": "R50.9", "description": "Fever, unspecified"},
+        {"code": "A00", "description": "Cholera"},
+        {"code": "J06.9", "description": "Acute upper respiratory infection, unspecified"}
+    ]
+
+def fetch_cpt_codes(query):
+    # Mock CPT codes for demonstration
+    return [
+        {"code": "99213", "description": "Office or other outpatient visit"},
+        {"code": "87804", "description": "Infectious agent detection by immunoassay"},
+        {"code": "85025", "description": "Complete blood count (CBC)"}
+    ]
 
 # Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
 model = AutoModelForSequenceClassification.from_pretrained("emilyalsentzer/Bio_ClinicalBERT", num_labels=1000)  # Adjust num_labels as needed
 
-# Function to fetch ICD codes from API
-def fetch_icd_codes(query):
-    try:
-        response = requests.get(f"{ICD_API_URL}?desc={query}")
-        if response.status_code == 200:
-            return response.json()  # Adjust based on API response format
-        else:
-            return []
-    except Exception as e:
-        print(f"Error fetching ICD codes: {e}")
-        return []
-
-# Function to fetch CPT codes from API
-def fetch_cpt_codes(query):
-    try:
-        response = requests.get(f"{CPT_API_URL}?desc={query}")
-        if response.status_code == 200:
-            return response.json()  # Adjust based on API response format
-        else:
-            return []
-    except Exception as e:
-        print(f"Error fetching CPT codes: {e}")
-        return []
-
 # Prediction function
 def predict_codes(text):
     if not text.strip():
@@ -62,7 +50,7 @@ def predict_codes(text):
     # Get top 3 predictions
     top_k = torch.topk(probs, k=3)
     
-    # Fetch ICD and CPT codes using APIs
+    # Fetch ICD and CPT codes using mock functions
     icd_results = fetch_icd_codes(text)
     cpt_results = fetch_cpt_codes(text)
     
@@ -80,8 +68,15 @@ def predict_codes(text):
 # Create Gradio interface
 iface = gr.Interface(
     fn=predict_codes,
-    inputs=gr.Textbox(lines=5, placeholder="Enter medical summary here...", label="Medical Summary"),
-    outputs=gr.Textbox(label="Predicted Codes", lines=10),
+    inputs=gr.Textbox(
+        lines=5,
+        placeholder="Enter medical summary here...",
+        label="Medical Summary"
+    ),
+    outputs=gr.Textbox(
+        label="Predicted Codes",
+        lines=10
+    ),
     title="AutoRCM - Medical Code Predictor",
     description="Enter a medical summary to get recommended ICD-10 and CPT codes.",
     examples=[