Update app.py

app.py

@@ -151,22 +151,34 @@ def add_code_to_workspace(project_name, code, file_name):
     st.session_state.workspace_projects[project_name]['files'].append(file_name)
     return f"Code added to '{file_name}' in project '{project_name}'."
 
-def chat_interface(input_text):
-    # Placeholder for chat interface logic
-    return f"Chatbot response: {input_text}"
+def chat_interface_with_agent(input_text, agent_name):
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
 
-st.title("AI Agent Creator")
+    model_name = "gpt2"
+    try:
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except EnvironmentError as e:
+        return f"Error loading model: {e}"
 
-sidebar = st.sidebar
-sidebar.title("Navigation")
-app_mode = sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
+    # Combine the agent prompt with user input
+    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
 
-if app_mode == "AI Agent Creator":
-    st.header("Create an AI Agent from Text")
+    # Truncate input text to avoid exceeding the model's maximum length
+    max_input_length = 900
+    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+    if input_ids.shape[1] > max_input_length:
+        input_ids = input_ids[:, :max_input_length]
 
-    subheader = st.subheader
-    agent_name = subheader("Enter agent name:")
-    text_input = subheader("Enter skills (one per line):")
+    # Adjust max_new_tokens if needed
+    max_new_tokens = 50  # Reduce if necessary
 
-    if st.button("Create Agent"):
-        agent_prompt = create_agent_from_text(agent_name, text_input)
+    # Generate chatbot response
+    outputs = model.generate(
+        input_ids, max_new_tokens=max_new_tokens, num_return_sequences=1, do_sample=True
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response