ai: Update Next-Gen logic.

app.py

@@ -16,4 +16,4 @@ if __name__ == "__main__":
 
     # Call the 'launch' method on the 'app' object to start the user interface.
     # This typically opens the UI window or begins the event loop, making the application interactive.
-    app.queue(default_concurrency_limit=2).launch(show_api=False)
+    app.queue(default_concurrency_limit=2).launch(show_api=False, quiet=True)

src/client/chat_handler.py

@@ -16,51 +16,51 @@ from src.tools.deep_search import SearchTools  # Import SearchTools class for de
 import gradio as gr  # Import Gradio library for UI and request handling
 
 # Define an asynchronous function 'respond' to process user messages and generate AI responses
-# This version uses the "messages" style for chat history, where history is a list of dicts with "role" and "content" keys,
-# supporting content as strings, dicts with "path" keys, or Gradio components.
+# This function handles various types of user inputs including text, commands, and file uploads
+# It supports multiple AI models and generation modes with customizable parameters
 async def respond(
     message,  # Incoming user message, can be a string or a dictionary containing text and files
-    history: List[Any],  # List containing conversation history as pairs of user and assistant messages (tuples style)
-    model_label,  # Label/key to select the AI model from the available models
-    temperature,  # Sampling temperature controlling randomness of AI response generation
-    top_k,  # Number of highest probability tokens to keep for sampling
-    min_p,  # Minimum probability threshold for token sampling
-    top_p,  # Cumulative probability threshold for nucleus sampling
-    repetition_penalty,  # Penalty factor to reduce repetitive tokens in generated text
-    thinking,  # Boolean flag indicating if AI should operate in "thinking" mode
-    image_gen,  # Boolean flag to enable image generation commands
-    audio_gen,  # Boolean flag to enable audio generation commands
-    search_gen,  # Boolean flag to enable deep search commands
+    history: List[Any],  # List containing conversation history as pairs of user and assistant messages
+    model_label,  # Label/key to select the specific AI model from available models configuration
+    temperature,  # Sampling temperature parameter controlling randomness of AI response generation (0.0 to 2.0)
+    top_k,  # Number of highest probability tokens to keep for sampling during text generation
+    min_p,  # Minimum probability threshold for token sampling to filter low probability tokens
+    top_p,  # Cumulative probability threshold for nucleus sampling technique
+    repetition_penalty,  # Penalty factor to reduce repetitive tokens in generated text output
+    thinking,  # Boolean flag indicating if AI should operate in "thinking" mode with deeper reasoning
+    image_gen,  # Boolean flag to enable image generation commands using /image prefix
+    audio_gen,  # Boolean flag to enable audio generation commands using /audio prefix
+    search_gen,  # Boolean flag to enable deep search commands using /dp prefix
     request: gr.Request  # Gradio request object to access session information such as session hash
 ):
-    # Select the AI model based on the provided label, if label not found, fallback to the first model in the config
+    # Select the AI model based on the provided label, fallback to first model if label not found
     selected_model = model.get(model_label, list(model.values())[0])
     
-    # Instantiate SearchTools to enable deep search capabilities if requested
+    # Instantiate SearchTools class to enable deep search capabilities when requested by user
     search_tools = SearchTools()
     
-    # Retrieve session ID from the Gradio request's session hash, generate a new UUID if none exists
+    # Retrieve session ID from the Gradio request's session hash, generate new UUID if none exists
     session_id = request.session_hash or str(uuid.uuid4())
 
-    # Initialize an empty conversation history for this session if it does not already exist
+    # Initialize an empty conversation history list for this session if it does not already exist
     if session_id not in session:
         session[session_id] = []
 
     # Determine the mode string based on the 'thinking' flag, affects AI response generation behavior
     mode = "/think" if thinking else "/no_think"
 
-    # Initialize variables for user input text and any attached files
+    # Initialize variables for storing user input text and any attached files
     input = ""
     files = None
 
-    # Check if the incoming message is a dictionary (which may contain text and files)
+    # Check if the incoming message is a dictionary which may contain both text and file attachments
     if isinstance(message, dict):
         # Extract the text content from the message dictionary, default to empty string if missing
         input = message.get("text", "")
-        # Extract the first file from the files list if present, otherwise, set files to None
+        # Extract the first file from the files list if present, otherwise set files to None
         files = message.get("files")[0] if message.get("files") else None
     else:
-        # If the message is a simple string, assign it directly to input
+        # If the message is a simple string, assign it directly to input variable
         input = message
 
     # Strip leading and trailing whitespace from the input for clean processing
@@ -68,7 +68,7 @@ async def respond(
     # Convert the stripped input to lowercase for case-insensitive command detection
     lowered_input = stripped_input.lower()
 
-    # If the input is empty after stripping, yield an empty list and exit the function early
+    # If the input is empty after stripping whitespace, yield an empty list and exit function early
     if not stripped_input:
         yield []
         return
@@ -78,15 +78,14 @@ async def respond(
         yield []
         return
 
-    # Prepare a new conversation history list formatted with roles and content for AI model consumption
-    # Here we convert the old "tuples" style history (list of [user_msg, assistant_msg]) into "messages" style:
-    # a flat list of dicts with "role" and "content" keys.
+    # Convert conversation history from tuples style to messages style format for AI model consumption
+    # Transform list of [user_msg, assistant_msg] pairs into flat list of role-content dictionaries
     new_history = []
     for entry in history:
         # Ensure the entry is a list with exactly two elements: user message and assistant message
         if isinstance(entry, list) and len(entry) == 2:
             user_msg, assistant_msg = entry
-            # Append the user message with role 'user' to the new history if not None
+            # Append the user message with role 'user' to the new history if message is not None
             if user_msg is not None:
                 new_history.append({"role": "user", "content": user_msg})
             # Append the assistant message with role 'assistant' if it exists and is not None
@@ -96,68 +95,100 @@ async def respond(
     # Update the global session dictionary with the newly formatted conversation history for this session
     session[session_id] = new_history
 
-    # Handle audio generation command if enabled and input starts with '/audio'
+    # Handle audio generation command if enabled and input starts with '/audio' prefix
     if audio_gen and lowered_input.startswith("/audio"):
         # Extract the audio instruction text after the '/audio' command prefix and strip whitespace
         audio_instruction = input[6:].strip()
-        # If no instruction text is provided, yield empty and exit early
+        # If no instruction text is provided after the command, yield empty and exit early
         if not audio_instruction:
             yield []
             return
         try:
             # Asynchronously create audio content based on the instruction using AudioGeneration class
             audio = await AudioGeneration.create_audio(audio_instruction)
-            # Serialize the audio data and instruction into a JSON formatted string
+            # Serialize the audio data and instruction into a JSON formatted string for processing
             audio_generation_content = json.dumps({
                 "audio": audio,
                 "audio_instruction": audio_instruction
             })
-            # Construct the conversation history including the audio generation result and detailed instructions
+            # Construct the conversation history including the audio generation result and formatting instructions
             audio_generation_result = (
                 new_history
                 + [
                     {
                         "role": "system",
                         "content": (
-                            f"Audio generation result:\n\n{audio_generation_content}\n\n\n"
-                             "Show the audio using the following HTML audio tag format, where '{audio_link}' is the URL of the generated audio:\n\n"
-                             "<audio controls src='{audio_link}' style='width:100%; max-width:100%;'></audio>\n\n"
-                             "Please replace '{audio_link}' with the actual audio URL provided in the context.\n\n"
-                             "Then, describe the generated audio based on the above information.\n\n\n"
-                             "Use the same language as the previous user input or user request.\n"
-                             "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
-                             "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                            "Audio generation result:\n\n" + audio_generation_content + "\n\n\n"
+                            "Show the audio using the following HTML audio tag format, where '{audio_link}' is the URL of the generated audio:\n\n"
+                            "<audio controls src='{audio_link}' style='width:100%; max-width:100%;'></audio>\n\n"
+                            "Please replace '{audio_link}' with the actual audio URL provided in the context.\n\n"
+                            "Then, describe the generated audio based on the above information.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
                         )
                     }
                 ]
             )
 
-            # Use async generator to get descriptive text about the generated audio
+            # Use async generator to get descriptive text about the generated audio from AI
             async for audio_description in jarvis(
                 session_id=session_id,
                 model=selected_model,
                 history=audio_generation_result,
                 user_message=input,
-                mode="/no_think",  # Use no_think mode to avoid extra processing
-                temperature=0.7,  # Fixed temperature for audio description generation
-                top_k=20,  # Limit token sampling to top 20 tokens
-                min_p=0,  # Minimum probability threshold
-                top_p=0.8,  # Nucleus sampling threshold
+                mode="/no_think",  # Use non-reasoning mode to avoid extra processing overhead
+                temperature=0.7,  # Fixed temperature for consistent audio description generation
+                top_k=20,  # Limit token sampling to top 20 most probable tokens
+                min_p=0,  # Minimum probability threshold set to zero
+                top_p=0.8,  # Nucleus sampling threshold for quality control
                 repetition_penalty=1  # No repetition penalty for this step
             ):
-                # Yield the audio description wrapped in a tool role for UI display
-                yield [{"role": "tool", "content": f'{audio_description}'}]
+                # Yield the audio description wrapped in a tool role for proper UI display
+                yield [{"role": "tool", "content": audio_description}]
             return
         except Exception:
-            # If audio generation fails, yield an error message and exit
-            yield [{"role": "tool", "content": "Audio generation failed. Please wait 15 seconds before trying again."}]
+            # If audio generation fails, let AI generate a contextual error message
+            generation_failed = (
+                new_history
+                + [
+                    {
+                        "role": "system",
+                        "content": (
+                            "Audio generation failed for the user's request. The user tried to generate audio with the instruction: '"
+                            + audio_instruction + "'\n\n\n"
+                            "Please explain to the user that audio generation failed and suggest they wait 15 seconds before trying again.\n"
+                            "Be helpful and empathetic in your response.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                        )
+                    }
+                ]
+            )
+            
+            # Use AI to generate a contextual error message
+            async for error_response in jarvis(
+                session_id=session_id,
+                model=selected_model,
+                history=generation_failed,
+                user_message=input,
+                mode="/no_think",  # Use non-reasoning mode for error handling
+                temperature=0.7,  # Fixed temperature for more consistent error messages
+                top_k=20,  # Limit token sampling
+                min_p=0,  # Minimum probability threshold
+                top_p=0.8,  # Nucleus sampling threshold
+                repetition_penalty=1  # No repetition penalty
+            ):
+                # Yield the AI-generated error response wrapped in tool role
+                yield [{"role": "tool", "content": error_response}]
             return
 
-    # Handle image generation command if enabled and input starts with '/image'
+    # Handle image generation command if enabled and input starts with '/image' prefix
     if image_gen and lowered_input.startswith("/image"):
         # Extract the image generation instruction after the '/image' command prefix and strip whitespace
         generate_image_instruction = input[6:].strip()
-        # If no instruction text is provided, yield empty and exit early
+        # If no instruction text is provided after the command, yield empty and exit early
         if not generate_image_instruction:
             yield []
             return
@@ -165,58 +196,90 @@ async def respond(
             # Asynchronously create image content based on the instruction using ImageGeneration class
             image = await ImageGeneration.create_image(generate_image_instruction)
 
-            # Serialize the image data and instruction into a JSON formatted string
+            # Serialize the image data and instruction into a JSON formatted string for processing
             image_generation_content = json.dumps({
                 "image": image,
                 "generate_image_instruction": generate_image_instruction
             })
 
-            # Construct the conversation history including the image generation result and detailed instructions
+            # Construct the conversation history including the image generation result and formatting instructions
             image_generation_result = (
                 new_history
                 + [
                     {
                         "role": "system",
                         "content": (
-                            f"Image generation result:\n\n{image_generation_content}\n\n\n"
-                             "Show the generated image using the following markdown syntax format, where '{image_link}' is the URL of the image:\n\n"
-                             "![Generated Image]({image_link})\n\n"
-                             "Please replace '{image_link}' with the actual image URL provided in the context.\n\n"
-                             "Then, describe the generated image based on the above information.\n\n\n"
-                             "Use the same language as the previous user input or user request.\n"
-                             "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
-                             "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                            "Image generation result:\n\n" + image_generation_content + "\n\n\n"
+                            "Show the generated image using the following markdown syntax format, where '{image_link}' is the URL of the image:\n\n"
+                            "![Generated Image]({image_link})\n\n"
+                            "Please replace '{image_link}' with the actual image URL provided in the context.\n\n"
+                            "Then, describe the generated image based on the above information.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
                         )
                     }
                 ]
             )
 
-            # Use async generator to get descriptive text about the generated image
+            # Use async generator to get descriptive text about the generated image from AI
             async for image_description in jarvis(
                 session_id=session_id,
                 model=selected_model,
                 history=image_generation_result,
                 user_message=input,
-                mode="/no_think",  # Use no_think mode to avoid extra processing
-                temperature=0.7,  # Fixed temperature for image description generation
-                top_k=20,  # Limit token sampling to top 20 tokens
-                min_p=0,  # Minimum probability threshold
-                top_p=0.8,  # Nucleus sampling threshold
+                mode="/no_think",  # Use non-reasoning mode to avoid extra processing overhead
+                temperature=0.7,  # Fixed temperature for consistent image description generation
+                top_k=20,  # Limit token sampling to top 20 most probable tokens
+                min_p=0,  # Minimum probability threshold set to zero
+                top_p=0.8,  # Nucleus sampling threshold for quality control
                 repetition_penalty=1 # No repetition penalty for this step
             ):
-                # Yield the image description wrapped in a tool role for UI display
-                yield [{"role": "tool", "content": f"{image_description}"}]
+                # Yield the image description wrapped in a tool role for proper UI display
+                yield [{"role": "tool", "content": image_description}]
             return
         except Exception:
-            # If image generation fails, yield an error message and exit
-            yield [{"role": "tool", "content": "Image generation failed. Please wait 15 seconds before trying again."}]
+            # If image generation fails, let AI generate a contextual error message
+            generation_failed = (
+                new_history
+                + [
+                    {
+                        "role": "system",
+                        "content": (
+                            "Image generation failed for the user's request. The user tried to generate an image with the instruction: '"
+                            + generate_image_instruction + "'\n\n\n"
+                            "Please explain to the user that image generation failed and suggest they wait 15 seconds before trying again.\n"
+                            "Be helpful and empathetic in your response.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                        )
+                    }
+                ]
+            )
+            
+            # Use AI to generate a contextual error message
+            async for error_response in jarvis(
+                session_id=session_id,
+                model=selected_model,
+                history=generation_failed,
+                user_message=input,
+                mode="/no_think",  # Use non-reasoning mode for error handling
+                temperature=0.7,  # Fixed temperature for more consistent error messages
+                top_k=20,  # Limit token sampling
+                min_p=0,  # Minimum probability threshold
+                top_p=0.8,  # Nucleus sampling threshold
+                repetition_penalty=1  # No repetition penalty
+            ):
+                # Yield the AI-generated error response wrapped in tool role
+                yield [{"role": "tool", "content": error_response}]
             return
 
-    # Handle deep search command if enabled and input starts with '/dp'
+    # Handle deep search command if enabled and input starts with '/dp' prefix
     if search_gen and lowered_input.startswith("/dp"):
         # Extract the search query after the '/dp' command prefix and strip whitespace
         search_query = input[3:].strip()
-        # If no search query is provided, yield empty and exit early
+        # If no search query is provided after the command, yield empty and exit early
         if not search_query:
             yield []
             return
@@ -238,15 +301,15 @@ async def respond(
                     {
                         "role": "system",
                         "content": (
-                            f"Deep search results for query: '{search_query}':\n\n{search_content}\n\n\n"
-                             "Please analyze these search results and provide a comprehensive summary of the information.\n"
-                             "Identify the most relevant information related to the query.\n"
-                             "Format your response in a clear, structured way with appropriate headings and bullet points if needed.\n"
-                             "If the search results don't provide sufficient information, acknowledge this limitation.\n"
-                             "Please provide links or URLs from each of your search results.\n\n"
-                             "Use the same language as the previous user input or user request.\n"
-                             "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
-                             "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                            "Deep search results for query: '" + search_query + "':\n\n\n" + search_content + "\n\n\n"
+                            "Please analyze these search results and provide a comprehensive summary of the information.\n"
+                            "Identify the most relevant information related to the query.\n"
+                            "Format your response in a clear, structured way with appropriate headings and bullet points if needed.\n"
+                            "If the search results don't provide sufficient information, acknowledge this limitation.\n"
+                            "Please provide links or URLs from each of your search results.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
                         )
                     }
                 ]
@@ -265,20 +328,51 @@ async def respond(
                 top_p=top_p,
                 repetition_penalty=repetition_penalty
             ):
-                # Yield the search summary wrapped in a tool role for UI display
-                yield [{"role": "tool", "content": f"{search_response}"}]
+                # Yield the search summary wrapped in a tool role for proper UI display
+                yield [{"role": "tool", "content": search_response}]
             return
             
         except Exception as e:
-            # If deep search fails, yield an error message and exit
-            yield [{"role": "tool", "content": "Search failed, please try again later."}]
+            # If deep search fails, let AI generate a contextual error message
+            generation_failed = (
+                new_history
+                + [
+                    {
+                        "role": "system",
+                        "content": (
+                            "Deep search failed for the user's query: '" + search_query + "'\n\n\n"
+                            "Please explain to the user that the search operation failed and suggest they try again later.\n"
+                            "Be helpful and empathetic in your response. You can also suggest alternative approaches or workarounds.\n\n\n"
+                            "Use the same language as the previous user input or user request.\n"
+                            "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n"
+                            "If it is in English, explain in English. This also applies to other languages.\n\n\n"
+                        )
+                    }
+                ]
+            )
+            
+            # Use AI to generate a contextual error message
+            async for error_response in jarvis(
+                session_id=session_id,
+                model=selected_model,
+                history=generation_failed,
+                user_message=input,
+                mode="/no_think",  # Use non-reasoning mode for error handling
+                temperature=0.7,  # Fixed temperature for more consistent error messages
+                top_k=20,  # Limit token sampling
+                min_p=0,  # Minimum probability threshold
+                top_p=0.8,  # Nucleus sampling threshold
+                repetition_penalty=1  # No repetition penalty
+            ):
+                # Yield the AI-generated error response wrapped in tool role
+                yield [{"role": "tool", "content": error_response}]
             return
 
-    # For all other inputs that do not match special commands, use the jarvis function to generate a response
+    # For all other inputs that do not match special commands, use the jarvis function to generate a normal response
     async for response in jarvis(
         session_id=session_id,
         model=selected_model,
-        history=new_history,  # Pass the conversation history in "messages" style format
+        history=new_history,  # Pass the conversation history
         user_message=input,
         mode=mode,  # Use the mode determined by the thinking flag
         files=files,  # Pass any attached files along with the message
@@ -288,5 +382,5 @@ async def respond(
         top_p=top_p,
         repetition_penalty=repetition_penalty
     ):
-        # Yield each chunk of the response as it is generated
+        # Yield each chunk of the response as it is generated by the AI model
         yield response

src/ui/interface.py

@@ -176,7 +176,8 @@ def ui():
             multimodal=False, # Disable support for multimodal inputs such as images or audio files
             fill_height=True, # Duplicate from Blocks to Chat Interface
             fill_width=True,  # Duplicate from Blocks to Chat Interface
-            head=meta_tags    # Duplicate from Blocks to Chat Interface
+            head=meta_tags,   # Duplicate from Blocks to Chat Interface
+            show_progress="full" # Progress animation
         )
     # Return the complete Gradio app object for launching or embedding
     return app