Merge pull request #136 from cagostino/chris/installation_target_testing

adjusting to have multiple installation methods so users can install …

Author: cagostino

npcsh/conversation.py

@@ -7,9 +7,8 @@
 from typing import Any, Dict, Generator, List
 import os
 import anthropic
-import ollama  # Add to setup.py if missing
+
 from openai import OpenAI
-from diffusers import StableDiffusionPipeline
 from google.generativeai import types
 import google.generativeai as genai
 from .npc_sysenv import get_system_message
@@ -34,6 +33,7 @@ def get_ollama_conversation(
     Returns:
         List[Dict[str, str]]: The list of messages in the conversation.
     """
+    import ollama
 
     messages_copy = messages.copy()
     if messages_copy[0]["role"] != "system":

npcsh/embeddings.py

@@ -12,7 +12,6 @@
     NPCSH_EMBEDDING_PROVIDER,
     chroma_client,
 )
-import ollama
 from openai import OpenAI
 import anthropic
 
@@ -21,6 +20,8 @@ def get_ollama_embeddings(
     texts: List[str], model: str = "nomic-embed-text"
 ) -> List[List[float]]:
     """Generate embeddings using Ollama."""
+    import ollama
+
     embeddings = []
     for text in texts:
         response = ollama.embeddings(model=model, prompt=text)

npcsh/image_gen.py

@@ -10,7 +10,6 @@
 import os
 
 from openai import OpenAI
-from diffusers import StableDiffusionPipeline
 
 
 def generate_image_openai(
@@ -66,6 +65,8 @@ def generate_image_hf_diffusion(
         PIL.Image: The generated image.
     """
     # Load the Stable Diffusion pipeline
+    from diffusers import StableDiffusionPipeline
+
     pipe = StableDiffusionPipeline.from_pretrained(model)
     pipe = pipe.to(device)
 

npcsh/knowledge_graph.py

@@ -3,7 +3,11 @@
 import datetime
 
 import numpy as np
-import kuzu
+
+try:
+    import kuzu
+except ModuleNotFoundError:
+    print("kuzu not installed")
 from typing import Optional, Dict, List, Union, Tuple
 
 
@@ -581,7 +585,10 @@ def visualize_graph(conn):
     plt.show()
 
 
-import chromadb
+try:
+    import chromadb
+except ModuleNotFoundError:
+    print("chromadb not installed")
 import numpy as np
 import os
 import datetime

npcsh/llm_funcs.py

@@ -4,33 +4,17 @@
 import os
 import json
 import PIL
-from PIL import Image
 
 import sqlite3
 from datetime import datetime
 from typing import List, Dict, Any, Optional, Union, Generator
-import typing_extensions as typing
-from pydantic import BaseModel, Field
-
-import base64
-import re
-import io
 
 
 from jinja2 import Environment, FileSystemLoader, Template, Undefined
 
 import pandas as pd
 import numpy as np
 
-# chroma
-import chromadb
-from chromadb import Client
-
-# llm providers
-import anthropic
-import ollama  # Add to setup.py if missing
-from openai import OpenAI
-from diffusers import StableDiffusionPipeline
 from google.generativeai import types
 import google.generativeai as genai
 
@@ -54,7 +38,6 @@
     NPCSH_API_URL,
     NPCSH_VISION_MODEL,
     NPCSH_VISION_PROVIDER,
-    chroma_client,
     available_reasoning_models,
     available_chat_models,
 )
@@ -150,25 +133,30 @@ def generate_image(
     #    image = generate_image_openai_like(prompt, model, npc.api_url, openai_api_key)
     elif provider == "diffusers":
         image = generate_image_hf_diffusion(prompt, model)
+    else:
+        image = None
     # save image
     # check if image is a PIL image
     if isinstance(image, PIL.Image.Image):
         image.save(filename)
         return filename
 
-    elif image is not None:
-        # image is at a private url
-        response = requests.get(image.data[0].url)
-        with open(filename, "wb") as file:
-            file.write(response.content)
-        from PIL import Image
+    else:
+        try:
+            # image is at a private url
+            response = requests.get(image.data[0].url)
+            with open(filename, "wb") as file:
+                file.write(response.content)
+            from PIL import Image
 
-        img = Image.open(filename)
-        img.show()
-        # console = Console()
-        # console.print(Image.from_path(filename))
+            img = Image.open(filename)
+            img.show()
+            # console = Console()
+            # console.print(Image.from_path(filename))
+            return filename
 
-        return filename
+        except AttributeError as e:
+            print(f"Error saving image: {e}")
 
 
 def get_embeddings(
@@ -511,7 +499,6 @@ def execute_llm_question(
         # messages.append({"role": "assistant", "content": output})
 
     else:
-
         response = get_conversation(
             messages,
             model=model,
@@ -1030,7 +1017,6 @@ def check_llm_command(
         return {"messages": messages, "output": output}
 
     elif action == "answer_question":
-
         if ENTER_REASONING_FLOW:
             print("entering reasoning flow")
             result = enter_reasoning_human_in_the_loop(
@@ -1208,7 +1194,6 @@ def handle_tool_call(
     # print(npc)
     print("handling tool call")
     if not npc:
-
         print(
             f"No tools available for NPC '{npc.name}' or tools_dict is empty. Available tools: {available_tools}"
         )
@@ -1320,7 +1305,6 @@ def handle_tool_call(
     print("Executing tool with input values:", input_values)
 
     try:
-
         tool_output = tool.execute(
             input_values,
             npc.all_tools_dict,
@@ -1335,7 +1319,6 @@ def handle_tool_call(
         if "Error" in tool_output:
             raise Exception(tool_output)
     except Exception as e:
-
         # diagnose_problem = get_llm_response(
         ##    f"""a problem has occurred.
         #                                    Please provide a diagnosis of the problem and a suggested #fix.

npcsh/npc_sysenv.py

@@ -3,7 +3,6 @@
 from typing import Any
 import os
 import io
-import chromadb
 import sqlite3
 from dotenv import load_dotenv
 from PIL import Image
@@ -222,7 +221,12 @@ def get_system_message(npc: Any) -> str:
 
 EMBEDDINGS_DB_PATH = os.path.expanduser("~/npcsh_chroma.db")
 
-chroma_client = chromadb.PersistentClient(path=EMBEDDINGS_DB_PATH)
+try:
+    import chromadb
+
+    chroma_client = chromadb.PersistentClient(path=EMBEDDINGS_DB_PATH)
+except:
+    chroma_client = None
 
 
 # Load environment variables from .env file

npcsh/response.py

@@ -2,9 +2,7 @@
 from pydantic import BaseModel
 import os
 import anthropic
-import ollama  # Add to setup.py if missing
 from openai import OpenAI
-from diffusers import StableDiffusionPipeline
 from google.generativeai import types
 from google import genai
 
@@ -143,6 +141,8 @@ def get_ollama_response(
     Returns:
         Dict[str, Any]: The response, optionally including updated messages.
     """
+    import ollama
+
     # try:
     # Prepare the message payload
     system_message = get_system_message(npc) if npc else "You are a helpful assistant."

npcsh/search.py

@@ -162,8 +162,12 @@ def rag_search(
 
     """
     if embedding_model is None:
-        embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
-
+        try:
+            embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+        except:
+            raise Exception(
+                "Please install the sentence-transformers library to use this function or provide an embedding transformer model."
+            )
     results = []
 
     # Compute the embedding of the query

npcsh/shell_helpers.py

@@ -1433,7 +1433,6 @@ def execute_slash_command(
             command_parts, model=model, provider=provider, npc=npc, api_url=api_url
         )
     elif command_name == "help":  # New help command
-
         return {
             "messages": messages,
             "output": get_help(),
@@ -1763,7 +1762,7 @@ def execute_command(
     messages: list = None,
     conversation_id: str = None,
     stream: bool = False,
-    embedding_model: Union[SentenceTransformer, Any] = None,
+    embedding_model=None,
 ):
     """
     Function Description:
@@ -1774,7 +1773,7 @@ def execute_command(
         db_path : str : Database path
         npc_compiler : NPCCompiler : NPC compiler
     Keyword Args:
-        embedding_model : Union[SentenceTransformer, Any] : Embedding model
+        embedding_model :  Embedding model
         current_npc : NPC : Current NPC
         messages : list : Messages
     Returns:
@@ -2086,7 +2085,7 @@ def execute_command_stream(
     command: str,
     db_path: str,
     npc_compiler: NPCCompiler,
-    embedding_model: Union[SentenceTransformer, Any] = None,
+    embedding_model=None,
     current_npc: NPC = None,
     model: str = None,
     provider: str = None,

npcsh/stream.py

@@ -10,9 +10,7 @@
 from typing import Any, Dict, Generator, List
 import os
 import anthropic
-import ollama  # Add to setup.py if missing
 from openai import OpenAI
-from diffusers import StableDiffusionPipeline
 from google import genai
 
 from google.generativeai import types
@@ -53,6 +51,8 @@ def get_anthropic_stream(
         messages = messages[1:]
     elif npc is not None:
         system_message = get_system_message(npc)
+    else:
+        system_message = "You are a helpful assistant."
 
     # Preprocess messages to ensure content is a list of dicts
     for message in messages:
@@ -274,6 +274,8 @@ def get_ollama_stream(
     **kwargs,
 ) -> Generator:
     """Streams responses from Ollama, supporting images and tools."""
+    import ollama
+
     messages_copy = messages.copy()
 
     # Handle images if provided