Update app.py

app.py

@@ -4,27 +4,32 @@ from transformers import pipeline
 
 print("Booting up PredictiX Inference API...")
 
-# 1. Load Ticket Categorization Model (From local folder)
+# Grab the secret token you added in the Space Settings
+hf_token = os.environ.get("HF_TOKEN")
+
+# 1. Load Ticket Categorization Model
 try:
     cat_path = "./distilbert_category_model"
     model_id = cat_path if os.path.exists(cat_path) else "Dinusha-Ekanayake/predictix-ticket_categorization_model"
-    categorizer = pipeline("text-classification", model=model_id, top_k=None)
+    categorizer = pipeline("text-classification", model=model_id, top_k=None, token=hf_token)
 except Exception as e:
     categorizer = None
     print(f"Failed to load categorizer: {e}")
 
-# 2. Load Ticket Summarization Model (From local folder)
+# 2. Load Ticket Summarization Model
 try:
     sum_path = "./predictix-ticket_summarization_model"
     model_id = sum_path if os.path.exists(sum_path) else "Dinusha-Ekanayake/predictix-ticket_summarization_model"
-    ticket_summarizer = pipeline("summarization", model=model_id)
+    # Auto-detect task to avoid "Unknown task" error!
+    ticket_summarizer = pipeline(model=model_id, token=hf_token)
 except Exception as e:
     ticket_summarizer = None
     print(f"Failed to load ticket summarizer: {e}")
 
-# 3. Load Asset Summarization Model (From Hub)
+# 3. Load Asset Summarization Model (Gated Repo)
 try:
-    asset_summarizer = pipeline("summarization", model="Dinusha-Ekanayake/predictix-asset_summarization_model")
+    # Auto-detect task and pass token to bypass the gate!
+    asset_summarizer = pipeline(model="Dinusha-Ekanayake/predictix-asset_summarization_model", token=hf_token)
 except Exception as e:
     asset_summarizer = None
     print(f"Failed to load asset summarizer: {e}")
@@ -37,17 +42,16 @@ def categorize(text):
 def summarize_ticket(text):
     if not ticket_summarizer: return {"error": "Ticket Summarization model not loaded."}
     res = ticket_summarizer(text, min_length=15, max_length=150)
-    return {"summary": res[0]["summary_text"]}
+    return {"summary": res[0].get("summary_text", res[0].get("generated_text", str(res[0])))}
 
 def summarize_asset(text):
     if not asset_summarizer: return {"error": "Asset Summarization model not loaded."}
     res = asset_summarizer(text, min_length=20, max_length=150)
-    return {"summary": res[0]["summary_text"]}
+    return {"summary": res[0].get("summary_text", res[0].get("generated_text", str(res[0])))}
 
 # --- Server API Interface ---
 with gr.Blocks(title="PredictiX API") as demo:
     gr.Markdown("# PredictiX Internal Inference Server 🚀")
-    gr.Markdown("This space holds the models in RAM and exposes them via REST API to the PredictiX backend.")
     
     with gr.Tab("Ticket Categorization"):
         cat_in = gr.Textbox(label="Ticket Title & Description")