Update app.py

app.py

@@ -47,7 +47,6 @@ class AgentMemory:
 
     def add_short_term(self, data: Dict[str, Any]) -> None:
         self.short_term.append(data)
-        # Keep only the last 10 entries
         if len(self.short_term) > 10:
             self.short_term.pop(0)
 
@@ -73,7 +72,6 @@ class AgentHub:
         self.global_memory = AgentMemory()
         self.session_id = str(uuid.uuid4())
 
-        # Initialize NLP components
         try:
             self.tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
             self.model = AutoModelForCausalLM.from_pretrained("distilgpt2")
@@ -173,7 +171,6 @@ class IntelligentAgent:
 # ---------------------------
 # Specialized Agent Implementations
 # ---------------------------
-
 class WebResearchAgent(IntelligentAgent):
     def __init__(self, hub: AgentHub):
         super().__init__("web_research", hub)
@@ -297,7 +294,6 @@ class TextProcessingAgent(IntelligentAgent):
         else:
             chunk_size = 5
             chunking_strategy = "sentence_blocks"
-        chunks = []
         if chunking_strategy == "character_blocks":
             chunks = [task[i:i+chunk_size] for i in range(0, len(task), chunk_size)]
         elif chunking_strategy == "word_blocks":
@@ -1007,7 +1003,7 @@ def create_gradio_interface():
             return {"error": str(e)}
     with gr.Blocks(title="SmolAgents Toolbelt") as interface:
         gr.Markdown("# SmolAgents Toolbelt")
-        gr.Markdown("A collection of specialized agents for various tasks with improved, evolved logic :contentReference[oaicite:2]{index=2}.")
+        gr.Markdown("A collection of specialized agents for various tasks with evolved logic :contentReference[oaicite:0]{index=0}.")
         with gr.Tabs():
             with gr.Tab("Single Agent"):
                 agent_type = gr.Dropdown(
@@ -1025,8 +1021,10 @@ def create_gradio_interface():
                 chain_input = gr.Textbox(label="Input", placeholder="Enter your request for the chain...")
                 chain_sequence = gr.Textbox(label="Agent Sequence", placeholder="Comma-separated agent names (e.g., text_processing,data_analysis)")
                 chain_output = gr.JSON(label="Chain Output")
+                # Use a hidden state component for request type instead of a literal string
+                chain_type = gr.State("chain")
                 chain_btn = gr.Button("Process Chain")
-                chain_btn.click(fn=process_request, inputs=["chain", chain_input, chain_sequence], outputs=chain_output)
+                chain_btn.click(fn=process_request, inputs=[chain_type, chain_input, chain_sequence], outputs=chain_output)
             with gr.Tab("Help"):
                 gr.Markdown("""
                 ## Available Agents
@@ -1040,7 +1038,7 @@ def create_gradio_interface():
                 - **File Management Agent**: Handles file creation, reading, listing, and deletion.
                 
                 ### Usage
-                1. Select an agent (or choose 'chain' for a sequence).
+                1. Select an agent (or choose 'Chain of Thought' for a sequence).
                 2. Enter your request.
                 3. For chains, provide a comma-separated list of agent IDs.
                 """)