initial commit

.gitignore

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+/env

app.py

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+import os
+import gradio as gr
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# Hugging FaceのAPIトークンを設定
+os.environ["HUGGINGFACE_TOKEN"] = os.getenv("HUGGINGFACE_TOKEN")
+
+model_name_or_path = "TheBloke/OpenBuddy-Llama2-13B-v11.1-GGUF"
+model_basename = "openbuddy-llama2-13b-v11.1.Q2_K.gguf"
+
+model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename, revision="main")
+llama = Llama(model_path)
+
+def predict(message, history):
+    messages = []
+    for human_content, system_content in history:
+        message_human = {
+            "role": "user",
+            "content": human_content + "\n",
+        }
+        message_system = {
+            "role": "system",
+            "content": system_content + "\n",
+        }
+        messages.append(message_human)
+        messages.append(message_system)
+    message_human = {
+        "role": "user",
+        "content": message + "\n",
+    }
+    messages.append(message_human)
+    # Llamaでの回答を取得（ストリーミングオン）
+    streamer = llama.create_chat_completion(messages, stream=True)
+
+    partial_message = ""
+    for msg in streamer:
+        message = msg['choices'][0]['delta']
+        if 'content' in message:
+            partial_message += message['content']
+            yield partial_message
+
+gr.ChatInterface(predict).launch(enable_queue=True)