Update app.py

app.py

@@ -21,25 +21,28 @@ task_instructions = {
 }
 
 
-# Preprocessing user input
+# Enhanced text preprocessing function
 def preprocess_text(text):
     """
-    Clean and validate the user's input text.
+    Clean and validate the user's input text with better error handling and language detection.
     """
     try:
         # Detect input language
         language = langdetect.detect(text)
         if language != "en":
             return f"Input language detected as {language}. Please provide input in English."
-    except Exception:
+    except langdetect.lang_detect_exception.LangDetectException:
         return "Unable to detect language. Please provide valid text input."
+    except Exception as e:
+        return f"An error occurred while processing the text: {str(e)}"
+
     return text.strip()
 
 
-# Respond function for handling user input and generating a response
+# Enhanced respond function with better error handling and structured flow
 def respond(task, message, history, system_message, max_tokens, temperature, top_p):
     """
-    Handle user messages and generate responses using the NLP model.
+    Handle user messages and generate responses using the NLP model with improved error handling and response flow.
     """
     # Apply task-specific instructions
     system_message = f"{system_message} Task: {task_instructions.get(task, 'General NLP task')}"
@@ -47,8 +50,7 @@ def respond(task, message, history, system_message, max_tokens, temperature, top
     # Preprocess the user's input
     message = preprocess_text(message)
     if message.startswith("Input language detected") or message.startswith("Unable to detect"):
-        yield message
-        return
+        return message  # Early exit on language issues
 
     # Prepare conversation history
     messages = [{"role": "system", "content": system_message}]
@@ -59,10 +61,10 @@ def respond(task, message, history, system_message, max_tokens, temperature, top
             messages.append({"role": "assistant", "content": assistant_message})
 
     messages.append({"role": "user", "content": message})
-    response = ""
 
-    # Stream response from the Hugging Face model
+    # Stream response from the Hugging Face model with improved error handling
     try:
+        response = ""
         for chunk in client.chat_completion(
             messages=messages,
             max_tokens=max_tokens,
@@ -77,33 +79,37 @@ def respond(task, message, history, system_message, max_tokens, temperature, top
         yield f"Error generating response: {str(e)}"
 
 
-# Save conversation history to a JSON file
-def save_history(history):
-    with open("chat_history.json", "w") as f:
-        json.dump(history, f)
-    return "Chat history saved successfully."
+# Improved chat history management functions with better file handling
+def save_history(history, filename="chat_history.json"):
+    try:
+        with open(filename, "w") as f:
+            json.dump(history, f)
+        return "Chat history saved successfully."
+    except Exception as e:
+        return f"Error saving chat history: {str(e)}"
 
 
-# Load conversation history from a JSON file
-def load_history():
+def load_history(filename="chat_history.json"):
     try:
-        with open("chat_history.json", "r") as f:
+        with open(filename, "r") as f:
             history = json.load(f)
         return history
     except FileNotFoundError:
         return []
+    except json.JSONDecodeError:
+        return []  # Handle case where the file is empty or corrupt
 
 
-# Gradio app interface
+# Refactor the Gradio interface to be more organized and responsive
 def create_interface():
     """
-    Create the Gradio interface for the chatbot.
+    Create and enhance the Gradio interface for the chatbot with improved layout and feedback.
     """
     with gr.Blocks() as demo:
         gr.Markdown("## 🧠 NLPToolkit Agent\nAn advanced assistant for NLP tasks, powered by Hugging Face.")
 
+        # Organize task selection and parameters in a better layout
         with gr.Row():
-            # Task selection dropdown
             task = gr.Dropdown(
                 choices=["Summarization", "Sentiment Analysis", "Text Classification", "Entity Recognition"],
                 value="Summarization",
@@ -111,36 +117,32 @@ def create_interface():
             )
 
         with gr.Row():
-            # User input and system message
             user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
             system_message = gr.Textbox(value=default_system_message, label="System Message")
 
         with gr.Row():
-            # Chat history and assistant response
             chat_history = gr.State(value=[])
             assistant_response = gr.Textbox(label="Assistant Response", interactive=False)
 
         with gr.Row():
-            # Parameter sliders
             max_tokens = gr.Slider(1, 2048, value=512, label="Max Tokens")
             temperature = gr.Slider(0.1, 4.0, value=0.7, label="Temperature")
             top_p = gr.Slider(0.1, 1.0, value=0.95, label="Top-p (Nucleus Sampling)")
 
         with gr.Row():
-            # Buttons for save/load functionality
             save_button = gr.Button("Save Chat History")
             load_button = gr.Button("Load Chat History")
 
         with gr.Row():
-            # Submit button
             submit_button = gr.Button("Generate Response")
 
-        # Connect functionalities
+        # Connect button actions and ensure smooth flow
         submit_button.click(
             fn=respond,
             inputs=[task, user_input, chat_history, system_message, max_tokens, temperature, top_p],
             outputs=assistant_response
         )
+
         save_button.click(fn=save_history, inputs=chat_history, outputs=None)
         load_button.click(fn=load_history, inputs=None, outputs=chat_history)
 
@@ -149,7 +151,7 @@ def create_interface():
     return demo
 
 
-# Run the app
+# Run the enhanced Gradio app
 if __name__ == "__main__":
     demo = create_interface()
     demo.launch()