ollama client can now call multimodal models

ollama_client.py

@@ -1,10 +1,14 @@
 import os
 import json
 import time
+import base64
 import urllib3
 import requests
+from PIL import Image
+from io import BytesIO
 from argparse import ArgumentParser
 
+
 def ollama_list(api_base='http://localhost:11434'):
     # call api http://localhost:11434/api/tags with http get request
     urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
@@ -45,7 +49,10 @@ def ollama_chat_endpoint(api_base='http://localhost:11434', model_name='llama3.2
     }
     return endpoint
 
-def ollama_chat(endpoint, prompt='Hello World', temperature=0.0, max_tokens=32768):
+def hex2base64(hex_string):
+    return base64.b64encode(bytes.fromhex(hex_string)).decode('utf-8')
+
+def ollama_chat(endpoint, prompt='Hello World', base64_image=None, temperature=0.0, max_tokens=32768):
 
     # Disable SSL warnings
     urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
@@ -68,12 +75,38 @@ def ollama_chat(endpoint, prompt='Hello World', temperature=0.0, max_tokens=3276
     if modelname.startswith("o1") or modelname.startswith("gpt-o1"):
         temperature = 1.0 # o1 models need temperature 1.0
     else:
-        messages.append({"content": "You are a helpful assistant", "role": "system"})
+        messages.append({"role": "system", "content": "You are a helpful assistant"})
     if modelname.startswith("4o") or modelname.startswith("gpt-4o") or modelname.startswith("gpt-3.5"):
         # reduce number of stoptokes to 4
         stoptokens = ["[/INST]", "<|im_end|>", "<|end_of_turn|>", "<|eot_id|>"]
 
-    messages.append({"role": "user", "content": prompt})
+    if base64_image:
+        image_type = "jpeg"
+        #base64_magic = {"/9j/": "jpeg", "iVBO": "png", "Qk": "bmp", "R0lG": "gif", "SUkq": "tiff", "SUkr": "tiff", "TU0A": "tiff", "GkXf": "webp", "UklG": "webp"}
+        base64_magic = {"/9j/": "jpeg", "iVBO": "png", "R0lG": "gif"} # only jpeg and png are allowed as data type; however all of the types above (but gif!) are supported by the API
+        for magic, itype in base64_magic.items():
+            if base64_image.startswith(magic):
+                #print(f"Detected {itype} image")
+                image_type = itype
+                break
+        # If this is a gif we must convert it to png
+        if image_type == "gif":
+            #print("Converting gif to png")
+            image = Image.open(BytesIO(base64.b64decode(base64_image)))
+            png_image = BytesIO()
+            image.save(png_image, format="PNG")
+            base64_image = base64.b64encode(png_image.getvalue()).decode('utf-8')
+            image_type = "png"
+
+        # Add the image to the message
+        image_url_object = {"url": f"data:image/{image_type};base64,{base64_image}"}
+        usermessage = {"role": "user", "content": [
+            {"type": "text", "text": prompt},
+            {"type": "image_url", "image_url": image_url_object}
+        ]}
+    else:
+        usermessage = {"role": "user", "content": prompt}
+    messages.append(usermessage)
 
     if modelname.startswith("o1") or modelname.startswith("4o"):
         stoptokens = []
@@ -129,17 +162,31 @@ def ollama_chat(endpoint, prompt='Hello World', temperature=0.0, max_tokens=3276
     except json.JSONDecodeError as e:
         raise Exception(f"Failed to parse JSON response from the API: {e}")
 
+def test_multimodal(endpoint):
+    image_path = "llmtest/testimage.png"
+    with open(image_path, "rb") as image_file:
+        base64_image = base64.b64encode(image_file.read()).decode('utf-8')
+    try:
+        answer, total_tokens, token_per_second = ollama_chat(endpoint, prompt="what is in the image", base64_image=base64_image)
+        if "42" in answer:
+            return True
+        return False
+    except Exception as e:
+        return False
+
 def main():
     parser = ArgumentParser(description="Testing the ollama API.")
     parser.add_argument('--api_base', required=False, default='http://localhost:11434', help='API base URL for the LLM, default is http://localhost:11434')
     parser.add_argument('--endpoint', required=False, default='', help='Name of an <endpoint>.json file in the endpoints directory')
     parser.add_argument('--model', required=False, default='llama3.2:latest', help='Name of the model to use, default is llama3.2:latest')
+    parser.add_argument('--image', required=False, default=None, help='path to an image that shall be processed')
     
     # parse the arguments
     args = parser.parse_args()
     api_base = args.api_base
     endpoint_name = args.endpoint
     model_name = args.model
+    image_path = args.image
 
     # load the endpoint file
     endpoint = {}
@@ -154,11 +201,29 @@ def main():
     else:
         endpoint = ollama_chat_endpoint(api_base, model_name)
     
+    # test if the endpoint is a multimodal model
+    if test_multimodal(endpoint):
+        print("Endpoint is a multimodal model.")
+    else:
+        print("Endpoint is not a multimodal model.")
+
+    # load the image, if a path is given
+    base64_image = None
+    if image_path:
+        with open(image_path, "rb") as image_file:
+            base64_image = base64.b64encode(image_file.read()).decode('utf-8')
+
     # access the ollama API
     models_dict = ollama_list()
     for (model, attr) in models_dict.items():
         print(f"Model: {model}")
+    try:
+        if base64_image:
+            answer, total_tokens, token_per_second = ollama_chat(endpoint, prompt="what is in the image", base64_image=base64_image)
+        else:
             answer, total_tokens, token_per_second = ollama_chat(endpoint)
+    except Exception as e:
+        answer = f"Error: {str(e)}"
     print(answer)
 
 if __name__ == "__main__":

requirements.txt

@@ -1,3 +1,4 @@
+PIL
 sympy
 urllib3
 requests