Update app.py

app.py

@@ -226,41 +226,64 @@ final_answer = FinalAnswerTool()
 # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
 # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='sarvamai/sarvam-m',# it is possible that this model may be overloaded
-)
-
 
+# Define Models
+primary_model = HfApiModel(
+    max_tokens=2096,
+    temperature=0.5,
+    model_id='sarvamai/sarvam-m',  # Primary model, possibly overloaded
+)
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+backup_model = HfApiModel(
+    max_tokens=2096,
+    temperature=0.5,
+    model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',  # Backup model
+)
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
-    
-    
-agent = CodeAgent(
-    model=model,
-    tools=[final_answer,
-           get_horoscope,
+# Function to Build an Agent with Any Model
+def build_agent(model_to_use):
+    return CodeAgent(
+        model=model_to_use,
+        tools=[
+            final_answer,
+            get_horoscope,
             get_date_panchang,
             get_holidays,
             get_panchang_field,
             get_festivals_today,
             get_current_time_in_timezone,
             my_custom_tool,
-            image_generation_tool], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-    
-)
+            image_generation_tool
+        ],
+        max_steps=6,
+        verbosity_level=1,
+        grammar=None,
+        planning_interval=None,
+        name=None,
+        description=None,
+        prompt_templates=prompt_templates
+    )
+
+# Instantiate Primary Agent
+agent = build_agent(primary_model)
+
+# Fallback-Handled Runner Function
+def agent_runner(user_input):
+    try:
+        result = agent.run(user_input)
+        if result is None or result.strip() == "":
+            raise ValueError("Primary model returned empty response.")
+        return result
+    except Exception as e:
+        print(f"Primary model failed: {e}")
+        print("Switching to backup model...")
 
+        backup_agent = build_agent(backup_model)
+        try:
+            result = backup_agent.run(user_input)
+            return result or "Backup model also failed."
+        except Exception as e2:
+            return f"Backup model failed as well: {e2}"
 
-GradioUI(agent).launch()
+# Launch Gradio with Fallback Logic
+GradioUI(agent_runner).launch()