Update ContentGradio.py

ContentGradio.py

@@ -1,14 +1,15 @@
 import gradio as gr
 import os
 
-
 class ContentAgentUI:
     def __init__(self):
-        # Set the path to the external CSS file
+        self.agent = None
+
+        # Optional: Adjust path if needed for hosted environments
         css_path = os.path.join(os.getcwd(), "ui", "styles.css")
-        
-        self.ca_gui = gr.Blocks(css=css_path)
-        #self.ca_gui = gr.Blocks()
+
+        self.ca_gui = gr.Blocks(css=css_path if os.path.exists(css_path) else None)
+
         self.sections = [
             self.create_header,
             self.create_user_guidance,
@@ -16,123 +17,81 @@ class ContentAgentUI:
             self.create_examples,
             self.create_footer,
         ]
-        
+
         for section in self.sections:
             section()
-        
+
         self.ca_gui.launch()
 
+    def call_agent(self, input_text):
+        try:
+            if self.agent:
+                return self.agent.get_response(input_text)
+            else:
+                return "Agent not loaded."
+        except Exception as e:
+            return f"Error: {str(e)}"
+
     def create_header(self):
-        agent_header = """
-        #Content Agent                
-        """
         with self.ca_gui:
-            gr.Markdown(agent_header)
+            gr.Markdown("# Content Agent")
 
     def create_user_guidance(self):
-        guidance = """
-        Please enter text below to get started. The AI Agent will try to determine whether the language is polite and uses the following classification:
-        - `polite`
-        - `somewhat polite`
-        - `neutral`
-        - `impolite`
-        App is running `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B` text generation model.
-        Uses Intel's Polite Guard NLP library.
-        Compute is GCP · Nvidia L4 · 4x GPUs · 96 GB
-        """
         with self.ca_gui:
-            gr.Markdown(guidance)
-            
+            gr.Markdown("""
+                Please enter text below to get started. The AI Agent will try to determine whether the language is polite and uses the following classification:
+
+                - `polite`
+                - `somewhat polite`
+                - `neutral`
+                - `impolite`
+
+                Technology:
+
+                - App is running `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B` text generation model.
+                - Agent uses Intel's Polite Guard NLP library tool
+                - Compute on GCP · Nvidia L4 · 4x GPUs · 96 GB
+            """)
+
     def create_main(self):
         with self.ca_gui:
             with gr.Row():
                 with gr.Column():
                     self.user_input = gr.Textbox(label="Your Input", placeholder="Enter something here...")
                     self.submit_button = gr.Button("Submit")
-                    self.output = gr.Textbox(label="Content feedback", interactive=False, lines=10, max_lines=20 )
-                    
-                    # Define the function to be called when the button is clicked or Enter is pressed
-                    self.submit_button.click(process_input, inputs=self.user_input, outputs=self.output)
-                    self.user_input.submit(process_input, inputs=self.user_input, outputs=self.output)
-
- 
-    # Function to generate predefined examples
-    def get_example():
-        # Define the path to the 'examples' directory
+                    self.output = gr.Textbox(label="Content feedback", interactive=False, lines=10, max_lines=20)
+
+                    # Use bound method with `self`
+                    self.submit_button.click(self.call_agent, inputs=self.user_input, outputs=self.output)
+                    self.user_input.submit(self.call_agent, inputs=self.user_input, outputs=self.output)
+
+    def get_example(self):
         example_root = os.path.join(os.path.dirname(__file__), "examples")
-        
-        # Get list of all example text file paths
-        example_files = [os.path.join(example_root, _) for _ in os.listdir(example_root) if _.endswith("txt")]
-        
-        # Read the content of each file (assuming they're plain text files)
         examples = []
-        
-        for file_path in example_files:
-            example_content = ""
-            with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
-                example_content = f.read()
-            
-            examples.append(example_content)  # Append the content to the list
-    
+
+        if os.path.exists(example_root):
+            example_files = [os.path.join(example_root, f) for f in os.listdir(example_root) if f.endswith(".txt")]
+
+            for file_path in example_files:
+                with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
+                    examples.append(f.read())
+
         return examples
 
     def create_examples(self):
-        # Fetch examples by calling get_example() here
-        examples = get_example()
-        print("examples")
-        print(examples)
-
-        example_radio = gr.Radio(choices=examples, label="Try one of these examples:")
-        
-        # When an example is selected, populate the input field
+        examples = self.get_example()
+
         with self.ca_gui:
-            example_radio.change(fn=lambda example: example, inputs=example_radio, outputs=self.user_input)
+            if examples:
+                example_radio = gr.Radio(choices=examples, label="Try one of these examples:")
+                example_radio.change(fn=lambda ex: ex, inputs=example_radio, outputs=self.user_input)
+            else:
+                gr.Markdown("*No examples found.*")
 
     def create_footer(self):
         with self.ca_gui:
             gr.Markdown("<div id='footer'>Thanks for trying it out!</div>")
 
-
-    
-    # Function for Main content (takes user input and returns a response)
-    def process_input(input_text):
-    #return f"You entered: {user_input}"
-
-    #def get_agent_response(input_text):
-        try:
-            # Pass the input to the agent
-            output = agent.get_response(input_text)
-            
-            # Return the agent's response
-            return output
-        except Exception as e:
-            # Handle any errors that occur
-            return f"Error: {str(e)}"
-    
-        self.user_input.change(
-            fn=get_agent_response,
-            inputs=self.user_input,
-            outputs=self.output
-        )
-
     def pass_through_agent(self, agent):
-        # Simulate the agent's response
-        agent_response = agent(self.user_input.value)
-        self.output.update(agent_response)
-
-        # Pass the input to the agent
-        output = agent.get_response(input_text)
-
-        # Update the output text box with the agent's response
-        self.submit_button.click(
-            fn=process_input,
-            inputs=self.user_input,
-            outputs=self.output
-        )
-    
-        self.user_input.submit(
-            fn=get_agent_response,
-            inputs=self.user_input,
-            outputs=self.output
-        )
-   
+        # Assign the agent for future use in `call_agent`
+        self.agent = agent