Update app.py

app.py

@@ -9,8 +9,8 @@ upscaler = load_tool("diffusers/latent-upscaler-tool")
 tools = [controlnet_transformer, upscaler ]
 
 # Define the model and tokenizer
-model = BertModel.from_pretrained('bert-base-uncased')
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+#model = BertModel.from_pretrained('bert-base-uncased')
+#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 
 # Create the Streamlit app
 st.title("Hugging Face Agent")
@@ -33,15 +33,15 @@ def handle_submission():
     # Initialize the agent with the selected tools
     agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools)
 
-    agent.config.tokenizer = tokenizer
+ #   agent.config.tokenizer = tokenizer
     agent.config.tools = selected_tools
 
     # Process the user's message
-    inputs = tokenizer.encode_plus(message, add_special_tokens=True, return_tensors="pt")
-    outputs = agent(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+   # inputs = tokenizer.encode_plus(message, add_special_tokens=True, return_tensors="pt")
+   # outputs = agent(inputs['input_ids'], attention_mask=inputs['attention_mask'])
 
     # Display the agent's response
-    response = outputs.logits[0].item()
+    response = agent.run(inputs['input_ids'])
     st.text(f"{response:.4f}")
 
 # Add the callback function to the Streamlit app