Update app.py

app.py

@@ -8,6 +8,9 @@ import pytz
 import math
 import re
 import requests
+from transformers import HfAgent # Your successful import
+from transformers.tools import Tool # Your successful import
+from transformers import pipeline # <<< --- MAKE SURE THIS IMPORT IS ADDED / PRESENT
 import traceback
 
 import sys
@@ -152,10 +155,19 @@ def safe_calculator(expression: str) -> str:
 class HfAgentWrapper:
     def __init__(self):
         print("Initializing HfAgentWrapper...")
-        model_repo = "google/gemma-1.1-7b-it"  # Correct Hugging Face Hub ID
-        print(f"Loading HfAgent with model: {model_repo}")
+        model_id_or_path = "google/gemma-1.1-7b-it"  # Correct Hugging Face Hub ID
 
         try:
+            print(f"Attempting to create LLM pipeline for model: {model_id_or_path} with trust_remote_code=True")
+            # 1. Create the pipeline first, with trust_remote_code=True
+            llm_pipeline = pipeline(
+                task="text-generation", 
+                model=model_id_or_path,
+                trust_remote_code=True
+                # Optional: device_map="auto" if accelerate is properly set up and you have >1 GPU or want specific mapping
+            )
+            print(f"Successfully created LLM pipeline for {model_id_or_path}.")
+
             # Ensure your tool functions have docstrings.
             if not get_current_time_in_timezone.__doc__: 
                 raise ValueError("Tool 'get_current_time_in_timezone' is missing a docstring.")
@@ -180,26 +192,25 @@ class HfAgentWrapper:
                 func=safe_calculator,
                 description=safe_calculator.__doc__
             )
-
             self.actual_tools_for_agent = [time_tool_obj, search_tool_obj, calculator_tool_obj]
-            
             tool_names_for_log = [tool.name for tool in self.actual_tools_for_agent]
             print(f"Prepared Tool objects with names: {tool_names_for_log} and descriptions.")
 
-            # Initialize HfAgent, now including trust_remote_code=True
+            # 2. Pass the pre-initialized pipeline object to HfAgent
+            # The first argument (llm_endpoint) of HfAgent can be a pipeline instance.
+            print(f"Initializing HfAgent with pre-initialized pipeline...")
             self.agent = HfAgent(
-                model_repo, 
-                additional_tools=self.actual_tools_for_agent,
-                trust_remote_code=True  # <<< --- ENSURE THIS IS ADDED CORRECTLY WITH A COMMA BEFORE IT
+                llm_pipeline,  # <<< --- PASS THE PIPELINE OBJECT HERE
+                additional_tools=self.actual_tools_for_agent
+                # Note: trust_remote_code=True is NOT passed here directly
             )
-            print(f"HfAgent successfully instantiated with model {model_repo}.")
+            print(f"HfAgent successfully instantiated with pre-initialized pipeline.")
 
-            # --- Robust toolbox inspection ---
+            # --- Robust toolbox inspection (still useful) ---
             print(f"Inspecting self.agent.toolbox...")
             if hasattr(self.agent, 'toolbox'):
                 toolbox_attr = self.agent.toolbox
                 print(f"Type of self.agent.toolbox: {type(toolbox_attr)}")
-
                 if isinstance(toolbox_attr, dict):
                     print(f"Toolbox is a dict. Keys: {list(toolbox_attr.keys())}")
                     tools_info_from_dict = {}
@@ -210,7 +221,6 @@ class HfAgentWrapper:
                         tools_info_from_dict[key] = {'name_attr': tool_name_in_dict, 'type': str(type(value))}
                     print(f"Sample of toolbox dict content: {tools_info_from_dict}")
                     print(f"Names of tools we passed to HfAgent: {[tool.name for tool in self.actual_tools_for_agent]}")
-
                 elif hasattr(toolbox_attr, 'tools') and callable(toolbox_attr.tools):
                     try:
                         tools_in_toolbox = toolbox_attr.tools()
@@ -221,14 +231,13 @@ class HfAgentWrapper:
                     print(f"Toolbox is of type {type(toolbox_attr)} but not a dict or recognized Toolbox object for this debug print.")
             else:
                 print("self.agent does not have a 'toolbox' attribute (this would be unexpected).")
-            # --- End of robust toolbox inspection ---
 
-        except Exception as e: # This is the main except block for the __init__ try
-            print(f"CRITICAL ERROR: Failed to initialize HfAgent: {e}")
+        except Exception as e:
+            print(f"CRITICAL ERROR: Failed to initialize HfAgent or Pipeline: {e}")
             # import traceback # Already imported at the top of your file
-            print("Full traceback of HfAgent initialization error:")
+            print("Full traceback of HfAgent/Pipeline initialization error:")
             traceback.print_exc() 
-            raise RuntimeError(f"HfAgent initialization failed: {e}") from e
+            raise RuntimeError(f"HfAgent/Pipeline initialization failed: {e}") from e
 
     def __call__(self, question: str) -> str:
         print(f"\n--- HfAgentWrapper received question (first 100 chars): {question[:100]}... ---")