0.2.5.5

1 files changed, 42 insertions, 0 deletions

diff --git a/g4f/local/_engine.py b/g4f/local/_engine.py
new file mode 100644
index 00000000..5a701894
--- /dev/null
+++ b/g4f/local/_engine.py
@@ -0,0 +1,42 @@
+import os
+
+from gpt4all  import GPT4All
+from ._models import models
+
+class LocalProvider:
+    @staticmethod
+    def create_completion(model, messages, stream, **kwargs):
+        if model not in models:
+            raise ValueError(f"Model '{model}' not found / not yet implemented")
+        
+        model           = models[model]
+        model_dir       = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../models/')
+        full_model_path = os.path.join(model_dir, model['path'])
+        
+        if not os.path.isfile(full_model_path):
+            print(f"Model file '{full_model_path}' not found.")
+            download = input(f'Do you want to download {model["path"]} ? [y/n]')
+            
+            if download in ['y', 'Y']:
+                GPT4All.download_model(model['path'], model_dir)
+            else:
+                raise ValueError(f"Model '{model['path']}' not found.")
+        
+        model = GPT4All(model_name=model['path'],
+                               n_threads=8,
+                               verbose=False,
+                               allow_download=False,
+                               model_path=model_dir)
+        
+        system_template = next((message['content'] for message in messages if message['role'] == 'system'), 
+                               'A chat between a curious user and an artificial intelligence assistant.')
+        
+        prompt_template = 'USER: {0}\nASSISTANT: '
+        conversation    = '\n'.join(f"{msg['role'].upper()}: {msg['content']}" for msg in messages) + "\nASSISTANT: "
+        
+        with model.chat_session(system_template, prompt_template):
+            if stream:
+                for token in model.generate(conversation, streaming=True):
+                    yield token
+            else:
+                yield model.generate(conversation)
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