Update main.py

main.py

@@ -13,8 +13,12 @@ os.environ["HF_HOME"] = "/app/huggingface"
 app = FastAPI()
 
 # --- Load Clinical Trials CSV (for metadata lookup) ---
-csv_path = "clinical_trials.csv"  # Ensure this file is uploaded
-df_trials = pd.read_csv(csv_path)
+csv_path = "ctg-studies.csv"  # Ensure this file is uploaded
+if os.path.exists(csv_path):
+    df_trials = pd.read_csv(csv_path)
+    print("✅ CSV File Loaded Successfully!")
+else:
+    raise FileNotFoundError(f"❌ CSV File Not Found: {csv_path}. Upload it first.")
 
 # --- Load FAISS Index ---
 dimension = 768
@@ -58,12 +62,13 @@ async def retrieve_trial(request: QueryRequest):
 
     matched_trials = []
     for idx, dist in zip(indices[0], distances[0]):
-        nct_id = df_trials.iloc[idx]["NCT_ID"]  # Get NCT_ID using FAISS index mapping
-        trial_data = get_trial_info(nct_id)  # Fetch complete trial details
-        
-        if trial_data:
-            trial_data["similarity"] = round(100 / (1 + dist), 2)  # Convert similarity
-            matched_trials.append(trial_data)
+        if idx < len(df_trials):  # Ensure index is within bounds
+            nct_id = df_trials.iloc[idx]["NCT_ID"]  # Get NCT_ID using FAISS index mapping
+            trial_data = get_trial_info(nct_id)  # Fetch complete trial details
+            
+            if trial_data:
+                trial_data["similarity"] = round(100 / (1 + dist), 2)  # Convert similarity
+                matched_trials.append(trial_data)
 
     return {"matched_trials": matched_trials}