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Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+RUN wget https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_K_M.gguf -O model.gguf
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+COPY --chown=user . .
+
+CMD ["python", "app.py"]

README.md

@@ -1,12 +1,12 @@
 ---
-title: Llama 13b
-emoji: 📚
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 4.17.0
-app_file: app.py
+title: Natural SQL
+emoji: 🐶
+colorFrom: purple
+colorTo: blue
+sdk: docker
+models:
+- chatdb/natural-sql-7b-GGUF
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from llama_cpp import Llama
+
+llm = Llama(model_path="model.gguf", n_ctx=8000, n_threads=2, chat_format="chatml")
+  
+def generate(message, history,temperature=0.3,max_tokens=512):
+    system_prompt = """You are a super Inteligent AI assistant in SQL ANSI coding."""
+    formatted_prompt = [{"role": "system", "content": system_prompt}]
+    for user_prompt, bot_response  in history:
+        formatted_prompt.append({"role": "user", "content": user_prompt})
+        formatted_prompt.append({"role": "assistant", "content": bot_response })
+    formatted_prompt.append({"role": "user", "content": message})
+    stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True)
+    response  = ""
+    for chunk in stream_response:
+        if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
+            response  += chunk['choices'][0]["delta"]["content"]
+        yield response 
+
+mychatbot = gr.Chatbot(
+avatar_images=["user.png", "botnb.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+        
+iface = gr.ChatInterface(fn=generate, chatbot=mychatbot, retry_btn=None, undo_btn=None)
+
+with gr.Blocks() as demo:
+    gr.HTML("<center><h1>Natural SQL</h1></center>")
+    iface.render()
+
+demo.queue().launch(show_api=False, server_name="0.0.0.0")
+    

gitattributes

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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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readme.txt

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+Natural SQL

requirements.txt

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+gradio
+llama-cpp-python