Update app.py

app.py

@@ -1,4 +1,50 @@
 import streamlit as st
+from transformers import BertModel, BertTokenizer
+from transformers import HfAgent, load_tool
+
+# Load tools
+controlnet_transformer = load_tool("huggingface-tools/text-to-image")
+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')
+
+# Create the Streamlit app
+st.title("Hugging Face Agent")
+
+# Input field for the user's message
+message_input = st.text_input("Enter your message:", "")
+
+# Checkboxes for the tools to be used by the agent
+tool_checkboxes = [st.checkbox(f"Use {tool}") for tool in tools]
+
+# Submit button
+submit_button = st.button("Submit")
+
+# Define the callback function to handle the form submission
+def handle_submission():
+    # Get the user's message and the selected tools
+    message = message_input
+    selected_tools = [tool for tool, checkbox in zip(tools, tool_checkboxes) if checkbox]
+
+    # 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.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'])
+
+    # Display the agent's response
+    response = outputs.logits[0].item()
+    st.text(f"{response:.4f}")
+
+# Add the callback function to the Streamlit app
+submit_button = st.button("Submit", on_click=handle_submission)
+
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)