Update app.py

app.py

@@ -86,9 +86,6 @@ class AIAgent:
             "READ_PROMPT": self.read_prompt,
             "TASK_PROMPT": self.task_prompt,
             "UNDERSTAND_TEST_RESULTS_PROMPT": self.understand_test_results_prompt,
-            "EXECUTE_COMMAND": self.execute_command,  # Add command execution
-            "PYTHON_INTERPRET": self.python_interpret,  # Add Python interpretation
-            "NLP": self.nlp,  # Add NLP capabilities
         }
         self.task_history: List[Dict[str, str]] = []
         self.current_task: Optional[str] = None
@@ -102,7 +99,6 @@ class AIAgent:
             "bigscience/T0_3B",
         ]  # Add more as needed
         self.selected_model = "gpt2"  # Default model
-        self.nlp_pipeline = None  # Initialize NLP pipeline later
 
     # --- Search Functionality ---
     def search(self, query: str) -> List[str]:
@@ -255,12 +251,6 @@ class AIAgent:
                 return "Enter the new task to start."
             elif action == "UNDERSTAND_TEST_RESULTS_PROMPT":
                 return "Enter your question about the test results."
-            elif action == "EXECUTE_COMMAND":
-                return "Enter the command to execute."
-            elif action == "PYTHON_INTERPRET":
-                return "Enter the Python code to interpret."
-            elif action == "NLP":
-                return "Enter the text for NLP analysis."
             else:
                 raise InvalidActionError("Please provide a valid action.")
         except InvalidActionError as e:
@@ -324,39 +314,6 @@ class AIAgent:
         """Provides a prompt to understand the test results."""
         return "What do you want to know about the test results?"
 
-    # --- Command Execution Functionality ---
-    def execute_command(self, command: str) -> str:
-        """Executes the provided command in the terminal."""
-        try:
-            process = subprocess.run(
-                command.split(), capture_output=True, text=True
-            )
-            return f"Command output:\n{process.stdout}"
-        except subprocess.CalledProcessError as e:
-            return f"Error executing command: {e}"
-
-    # --- Python Interpretation Functionality ---
-    def python_interpret(self, code: str) -> str:
-        """Interprets the provided Python code."""
-        try:
-            exec(code)
-            return "Python code executed successfully."
-        except Exception as e:
-            return f"Error interpreting Python code: {e}"
-
-    # --- NLP Functionality ---
-    def nlp(self, text: str) -> str:
-        """Performs NLP analysis on the provided text."""
-        try:
-            if not self.nlp_pipeline:
-                self.nlp_pipeline = pipeline(
-                    "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-5-lit"
-                )  # Example NLP pipeline
-            analysis = self.nlp_pipeline(text)
-            return f"NLP Analysis: {analysis}"
-        except Exception as e:
-            return f"Error performing NLP analysis: {e}"
-
     # --- Input Handling Functionality ---
     def handle_input(self, input_str: str):
         """Handles user input and executes the corresponding action."""
@@ -448,7 +405,6 @@ if __name__ == "__main__":
     # --- Tabbed Navigation ---
     tabs = st.tabs(["Agent Generation", "Chat App"])
 
-    # --- Agent Generation Tab ---
     with tabs[0]:
         st.title("AI Agent Generation")
         st.sidebar.title("Agent Settings")
@@ -479,9 +435,7 @@ if __name__ == "__main__":
 
         # --- Model Dropdown ---
         selected_model = st.sidebar.selectbox(
-            "Model",
-            agent.available_models,
-            index=agent.available_models.index(agent.selected_model),
+            "Model", agent.available_models, index=agent.available_models.index(agent.selected_model)
         )
         agent.selected_model = selected_model
 
@@ -510,45 +464,39 @@ if __name__ == "__main__":
             except WorkspaceExplorerError as e:
                 st.error(f"Error exploring workspace: {e}")
 
-    # --- Chat App Tab ---
     with tabs[1]:
         st.title("Chat App")
+        st.sidebar.title("Chat Settings")
+
+        # --- Model Dropdown ---
+        selected_chat_model = st.sidebar.selectbox(
+            "Model", agent.available_models, index=agent.available_models.index(agent.selected_model)
+        )
+        agent.selected_model = selected_chat_model
 
         # --- Chat History ---
         chat_history = st.empty()
-        chat_history.text("Chat History:")
+        chat_input = st.text_input("Enter your message:")
 
-        # --- Input Field ---
-        user_input = st.text_input("Enter your message:")
-
-        # --- Send Message ---
+        # --- Chat Input Handling ---
         if st.button("Send"):
-            if user_input:
-                # --- Display User Message ---
-                chat_history.text(f"You: {user_input}")
+            if chat_input:
+                user_message = chat_input
+                chat_history.markdown(f"**You:** {user_message}")
+                chat_input.empty()
 
-                # --- Process User Input ---
+                # --- Generate Response ---
                 try:
-                    # --- Extract Command and Arguments ---
-                    action, *args = user_input.split()
-                    if action in agent.tools:
-                        if args:
-                            output = agent.tools[action](" ".join(args))
-                        else:
-                            output = agent.tools[action]()
-                        # --- Display Agent Response ---
-                        chat_history.text(f"Agent: {output}")
-                    else:
-                        # --- Treat as regular chat message ---
-                        output = agent.code_generation(user_input)
-                        # --- Display Agent Response ---
-                        chat_history.text(f"Agent: {output}")
+                    if not agent.code_generator:
+                        agent.code_generator = pipeline(
+                            "text-generation", model=agent.selected_model
+                        )
+                    response = agent.code_generator(
+                        user_message, max_length=500, num_return_sequences=1
+                    )[0]["generated_text"]
+                    chat_history.markdown(f"**Agent:** {response}")
                 except Exception as e:
-                    # --- Display Error Message ---
-                    chat_history.text(f"Agent: Error: {e}")
-
-                # --- Clear Input Field ---
-                user_input = ""
+                    chat_history.markdown(f"**Agent:** Error: {e}")
 
 # --- Gradio Integration ---
 def gradio_interface(input_text):