Update app.py

app.py

@@ -29,14 +29,16 @@ embedding_model = SentenceTransformer("all-MiniLM-L6-v2", cache_folder="/tmp/hug
 summarization_model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
 summarization_tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
 
+# ✅ Check if files exist before loading
+print("🔍 Available Files:", os.listdir("."))  # This will log available files
 
-# ✅ Load datasets
+# ✅ Load datasets with error handling
 try:
-   recommendations_df = pd.read_csv("treatment_recommendations.csv")
-   questions_df = pd.read_csv("symptom_questions.csv")
+    recommendations_df = pd.read_csv("treatment_recommendations.csv")
+    questions_df = pd.read_csv("symptom_questions.csv")
 except FileNotFoundError as e:
-    logging.error(f"Missing dataset file: {e}")
-    raise HTTPException(status_code=500, detail="Dataset files are missing.")
+    logging.error(f"❌ Missing dataset file: {e}")
+    raise HTTPException(status_code=500, detail=f"Dataset file not found: {str(e)}")
 
 # ✅ FAISS Index for disorder detection
 treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)