Docs, TTS, answer streaming, web search and perplexity clone

.github/workflows/python-app.yml

@@ -33,8 +33,10 @@ jobs:
     - name: Test ChromaDB lib functions with pytest
       run: |
         pwd 
-        cd ./Tests/ChromaDB
-        pytest test_chromadb.py
+        ls
+        ls ./Config_Files
+        cd ./Tests/ChromaDB/
+        pytest ./test_chromadb.py
 
     - name: Test RAG lib functions with pytest
       run: |
@@ -59,3 +61,8 @@ jobs:
         pwd
         cd ./Tests/Utils
         pytest test_utils.py
+
+    - name: Test tldw runs
+      run: |
+        pwd
+        python summarize.py -h

App_Function_Libraries/Audio/models/pyannote_diarization_config.yaml

@@ -0,0 +1,13 @@
+pipeline:
+  params:
+    clustering: AgglomerativeClustering
+    embedding: /FULL/PATH/TO/SCRIPT/tldw/App_Function_Libraries/models/pyannote_model_wespeaker-voxceleb-resnet34-LM.bin #models/pyannote_model_wespeaker-voxceleb-resnet34-LM.bin
+    segmentation: /FULL/PATH/TO/SCRIPT/tldw/App_Function_Libraries/models/pyannote_model_segmentation-3.0.bin #models/pyannote_model_segmentation-3.0.bin
+
+params:
+  segmentation:
+    min_duration_off: 0.0
+  clustering:
+    method: centroid
+    min_cluster_size: 12
+    threshold: 0.7045654963945799

App_Function_Libraries/Benchmarks_Evaluations/Confabulation_check.py

@@ -59,15 +59,20 @@ def simplified_geval(transcript: str, summary: str, api_name: str, api_key: str,
 
 Overall Assessment: [Your overall assessment of the summary's quality]
 """
-
+    # FIXME - Add g_eval_model to config.txt
+    # g_eval_model = loaded_config[][]
     try:
         result = chat_api_call(
             api_name,
             api_key,
             prompt,
             "",
             temp=temp,
-            system_message="You are a helpful AI assistant tasked with evaluating summaries."
+            system_message="You are a helpful AI assistant tasked with evaluating summaries.",
+            streaming=False,
+            minp=None,
+            maxp=None,
+            model=None
         )
     except Exception as e:
         return detailed_api_error(api_name, e)

App_Function_Libraries/Benchmarks_Evaluations/ms_g_eval.py

@@ -329,7 +329,7 @@ def geval_summarization(
                 temp = 0.7
                 logging.info(f"Debug - geval_summarization Function - API Endpoint: {api_endpoint}")
                 try:
-                    response = chat_api_call(api_endpoint, api_key, prompt_with_src_and_gen, "", temp, system_message)
+                    response = chat_api_call(api_endpoint, api_key, prompt_with_src_and_gen, "", temp, system_message, streaming=False, minp=None, maxp=None, model=None)
                 except Exception as e:
                     raise ValueError(f"Unsupported API endpoint: {api_endpoint}")
     except RetryError:

App_Function_Libraries/Character_Chat/Character_Chat_Lib.py

@@ -87,13 +87,25 @@ def extract_character_id(choice: str) -> int:
     """Extract the character ID from the dropdown selection string."""
     log_counter("extract_character_id_attempt")
     try:
-        character_id = int(choice.split('(ID: ')[1].rstrip(')'))
+        logging.debug(f"Choice received: {choice}")  # Debugging line
+        if not choice:
+            raise ValueError("No choice provided.")
+
+        if '(ID: ' not in choice or ')' not in choice:
+            raise ValueError(f"Invalid choice format: {choice}")
+
+        # Extract the ID part
+        id_part = choice.split('(ID: ')[1]
+        character_id = int(id_part.rstrip(')'))
+
+        logging.debug(f"Extracted character ID: {character_id}")  # Debugging line
         log_counter("extract_character_id_success")
         return character_id
     except Exception as e:
         log_counter("extract_character_id_error", labels={"error": str(e)})
         raise
 
+
 def load_character_wrapper(character_id: int, user_name: str) -> Tuple[Dict[str, Any], List[Tuple[Optional[str], str]], Optional[Image.Image]]:
     """Wrapper function to load character and image using the extracted ID."""
     log_counter("load_character_wrapper_attempt")

App_Function_Libraries/Chat/Chat_Functions.py

@@ -30,16 +30,23 @@
 # Functions:
 
 def approximate_token_count(history):
-    total_text = ''
-    for user_msg, bot_msg in history:
-        if user_msg:
-            total_text += user_msg + ' '
-        if bot_msg:
-            total_text += bot_msg + ' '
-    total_tokens = len(total_text.split())
-    return total_tokens
-
-def chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_message=None):
+    try:
+        total_text = ''
+        for user_msg, bot_msg in history:
+            if user_msg:
+                total_text += user_msg + ' '
+            if bot_msg:
+                total_text += bot_msg + ' '
+        total_tokens = len(total_text.split())
+        return total_tokens
+    except Exception as e:
+        logging.error(f"Error calculating token count: {str(e)}")
+        return 0
+
+
+# FIXME - add model parameter
+def chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_message, streaming, minp=None, maxp=None, model=None):
+    logging.info(f"Debug - Chat API Call - API Endpoint: {api_endpoint}")
     log_counter("chat_api_call_attempt", labels={"api_endpoint": api_endpoint})
     start_time = time.time()
     if not api_key:
@@ -50,18 +57,22 @@ def chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_messag
         logging.info(f"Debug - Chat API Call - API Key: {api_key}")
         logging.info(f"Debug - Chat chat_api_call - API Endpoint: {api_endpoint}")
         if api_endpoint.lower() == 'openai':
-            response = chat_with_openai(api_key, input_data, prompt, temp, system_message)
+            response = chat_with_openai(api_key, input_data, prompt, temp, system_message, streaming, minp, maxp, model)
 
         elif api_endpoint.lower() == 'anthropic':
             # Retrieve the model from config
             loaded_config_data = load_and_log_configs()
-            model = loaded_config_data['models']['anthropic'] if loaded_config_data else None
+            if not model:
+                model = loaded_config_data['anthropic_api']['model']
             response = chat_with_anthropic(
                 api_key=api_key,
                 input_data=input_data,
                 model=model,
                 custom_prompt_arg=prompt,
-                system_prompt=system_message
+                max_retries=3,
+                retry_delay=5,
+                system_prompt=system_message,
+                streaming=streaming,
             )
 
         elif api_endpoint.lower() == "cohere":
@@ -133,7 +144,7 @@ def chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_messag
 
 
 def chat(message, history, media_content, selected_parts, api_endpoint, api_key, prompt, temperature,
-         system_message=None):
+         system_message=None, streaming=False, minp=None, maxp=None, model=None):
     log_counter("chat_attempt", labels={"api_endpoint": api_endpoint})
     start_time = time.time()
     try:
@@ -172,12 +183,15 @@ def chat(message, history, media_content, selected_parts, api_endpoint, api_key,
         logging.debug(f"Debug - Chat Function - Prompt: {prompt}")
 
         # Use the existing API request code based on the selected endpoint
-        response = chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_message)
+        response = chat_api_call(api_endpoint, api_key, input_data, prompt, temp, system_message, streaming, minp=None, maxp=None, model=None)
 
-        chat_duration = time.time() - start_time
-        log_histogram("chat_duration", chat_duration, labels={"api_endpoint": api_endpoint})
-        log_counter("chat_success", labels={"api_endpoint": api_endpoint})
-        return response
+        if streaming:
+            return response
+        else:
+            chat_duration = time.time() - start_time
+            log_histogram("chat_duration", chat_duration, labels={"api_endpoint": api_endpoint})
+            log_counter("chat_success", labels={"api_endpoint": api_endpoint})
+            return response
     except Exception as e:
         log_counter("chat_error", labels={"api_endpoint": api_endpoint, "error": str(e)})
         logging.error(f"Error in chat function: {str(e)}")
@@ -374,7 +388,6 @@ def update_chat_content(selected_item, use_content, use_summary, use_prompt, ite
 
 CHARACTERS_FILE = Path('.', 'Helper_Scripts', 'Character_Cards', 'Characters.json')
 
-
 def save_character(character_data):
     log_counter("save_character_attempt")
     start_time = time.time()
@@ -435,7 +448,6 @@ def load_characters():
         return {}
 
 
-
 def get_character_names():
     log_counter("get_character_names_attempt")
     start_time = time.time()

App_Function_Libraries/Chat/Chat_Pipeline.py

@@ -0,0 +1,19 @@
+# Chat_Pipeline.py
+#
+# Description: This file contains functions related to the prompt modification pipeline, available as a means of
+#   complex prompt modification/replacement without modification of original intent/messaging.
+#
+# Imports
+import os
+#
+# 3rd-party Libraries
+#
+# Local Imports
+#
+#######################################################################################################################
+#
+# Functions:
+
+#
+# End of Chat_Pipeline.py
+#######################################################################################################################

App_Function_Libraries/DB/Character_Chat_DB.py

@@ -355,17 +355,20 @@ def add_character_card(card_data: Dict[str, Any]) -> Optional[int]:
 
 def get_character_cards() -> List[Dict]:
     """Retrieve all character cards from the database."""
-    logging.debug(f"Fetching characters from DB: {chat_DB_PATH}")
-    conn = sqlite3.connect(chat_DB_PATH)
-    cursor = conn.cursor()
-    cursor.execute("SELECT * FROM CharacterCards")
-    rows = cursor.fetchall()
-    columns = [description[0] for description in cursor.description]
-    conn.close()
-    characters = [dict(zip(columns, row)) for row in rows]
-    #logging.debug(f"Characters fetched from DB: {characters}")
-    return characters
-
+    try:
+        logging.debug(f"Fetching characters from DB: {chat_DB_PATH}")
+        conn = sqlite3.connect(chat_DB_PATH)
+        cursor = conn.cursor()
+        cursor.execute("SELECT * FROM CharacterCards")
+        rows = cursor.fetchall()
+        columns = [description[0] for description in cursor.description]
+        conn.close()
+        characters = [dict(zip(columns, row)) for row in rows]
+        logging.debug(f"Characters fetched from DB: {characters}")
+        return characters
+    except Exception as e:
+        logging.error(f"Error fetching character cards: {e}")
+        return []
 
 def get_character_card_by_id(character_id: Union[int, Dict[str, Any]]) -> Optional[Dict[str, Any]]:
     """

App_Function_Libraries/Gradio_Related.py

@@ -65,6 +65,7 @@
     create_utilities_yt_video_tab
 from App_Function_Libraries.Gradio_UI.Video_transcription_tab import create_video_transcription_tab
 from App_Function_Libraries.Gradio_UI.View_tab import create_manage_items_tab
+from App_Function_Libraries.Gradio_UI.WebSearch_tab import create_websearch_tab
 from App_Function_Libraries.Gradio_UI.Website_scraping_tab import create_website_scraping_tab
 from App_Function_Libraries.Gradio_UI.Workflows_tab import chat_workflows_tab
 from App_Function_Libraries.Gradio_UI.View_DB_Items_tab import create_view_all_mediadb_with_versions_tab, \
@@ -403,6 +404,10 @@ def launch_ui(share_public=None, server_mode=False):
         padding: 10px;
         margin-top: 10px;
     }
+    .scrollable-textbox textarea {
+    height: 600px !important;
+    overflow-y: auto !important;
+    }
     """
 
     config = load_and_log_configs()
@@ -464,6 +469,8 @@ def launch_ui(share_public=None, server_mode=False):
                 create_chat_interface_four()
                 chat_workflows_tab()
 
+            with gr.TabItem("Web Search & Review", id="websearch group", visible=True):
+                create_websearch_tab()
             with gr.TabItem("Character Chat", id="character chat group", visible=True):
                 create_character_card_interaction_tab()
                 create_character_chat_mgmt_tab()