🎉 feat: add Docker support, AI interaction, and chat interface

🐳 chore: add Dockerfile and .dockerignore for Docker support

🤖 feat(ai.py): add AI interaction with anthropic API

💬 feat(app.py): add chat interface with AI using gradio

🔧 chore(makefile): add makefile for easy start command

🔒 chore(.gitignore): add .gitignore to ignore unnecessary files

📚 chore(const.py): add constants for models, token lengths, and prompts

✨ feat: add pyproject.toml and settings.py for project configuration

📝 docs(pyproject.toml): define project metadata and dependencies using poetry

🔧 chore(settings.py): add settings file to manage environment variables

.dockerignore

@@ -0,0 +1,19 @@
+# Python
+__pycache__
+*.py[cod]
+*.pyo
+*.pyd
+
+# Directories
+__pycache__
+
+# Files
+.DS_Store
+
+# secret
+.env
+
+flagged
+gradio_cached_examples
+
+.venv

.gitignore

@@ -0,0 +1,19 @@
+# Python
+__pycache__
+*.py[cod]
+*.pyo
+*.pyd
+
+# Directories
+__pycache__
+
+# Files
+.DS_Store
+
+# secret
+.env
+
+flagged
+gradio_cached_examples
+
+.venv

Dockerfile

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+FROM python:3.10
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+    # prevents python creating .pyc files
+    PYTHONDONTWRITEBYTECODE=1 \
+    \
+    # pip
+    PIP_NO_CACHE_DIR=off \
+    PIP_DISABLE_PIP_VERSION_CHECK=on \
+    PIP_DEFAULT_TIMEOUT=100 \
+    \
+    # poetry
+    # https://python-poetry.org/docs/configuration/#using-environment-variables
+    POETRY_VERSION=1.3.2 \
+    # make poetry install to this location
+    POETRY_HOME="/opt/poetry" \
+    # make poetry create the virtual environment in the project's root
+    # it gets named `.venv`
+    POETRY_VIRTUALENVS_IN_PROJECT=false \
+    # do not ask any interactive question
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=false
+
+ENV VENV_PATH="/venv"
+ENV PATH="$VENV_PATH/bin:$PATH"
+
+WORKDIR /app
+
+RUN apt-get update
+RUN curl https://sh.rustup.rs -sSf | bash -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+RUN rustc --version
+
+RUN pip install wheel
+RUN apt-get update && apt-get upgrade -y && apt-get install netcat-traditional -y && apt-get install wkhtmltopdf -y
+RUN pip install --upgrade poetry
+RUN python -m venv /venv
+RUN /venv/bin/pip install --upgrade pip wheel setuptools setuptools_rust
+COPY . .
+
+RUN poetry build && \
+    /venv/bin/pip install --upgrade pip wheel setuptools && \
+    /venv/bin/pip install dist/*.whl
+
+COPY . .
+CMD ["python", "app.py"]

ai.py

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+import anthropic
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from settings import settings
+
+syncClient = anthropic.Anthropic(api_key=settings.ANTHROPIC_API_KEY,
+                                 timeout=5)
+asyncClient = anthropic.AsyncAnthropic(api_key=settings.ANTHROPIC_API_KEY,
+                                       timeout=60)
+class AnthropicCustom():
+    def __init__(self, api_key, model, max_tokens=1000, prompt=""):
+        self.api_key = api_key
+        self.model = model
+        self.max_tokens = max_tokens
+        self.prompt = prompt
+        if os.environ.get('ANTHROPIC_API_KEY') is not None:
+            api_key = os.environ.get('ANTHROPIC_API_KEY')
+        else:
+            os.environ['ANTHROPIC_API_KEY'] = api_key
+    def get_anthropic_response(self):
+        response = syncClient.completions.create(
+            prompt=self.prompt,
+            model=self.model,
+            max_tokens_to_sample=self.max_tokens,
+        )
+        return response.completion
+    async def get_anthropic_response_async(self):
+        async for line in await asyncClient.completions.create(
+            prompt=self.prompt,
+            model=self.model,
+            max_tokens_to_sample=self.max_tokens,
+            stop_sequences=[anthropic.HUMAN_PROMPT,],
+            stream=True,
+        ):
+            yield line.completion

app.py

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+import anthropic
+import gradio as gr
+from gradio.components import Dropdown, Checkbox,Textbox,IOComponent
+import re
+from ai import AnthropicCustom
+from const import ClaudeModels,ModelTokenLength,Prompts
+
+
+# Define a global variable for the conversation history
+conversation_history = ""
+
+async def interact_with_ai(user_question,token, model, token_length, prompt, prompt_input, memory):
+    global conversation_history
+        
+    if memory:
+        prompt = Prompts[prompt].value.format(memory=conversation_history, question=user_question)
+    else:
+        prompt = Prompts[prompt].value.format(memory="", question=user_question)
+
+    if prompt_input != re.search(r'Human: (.*?) \n\nConversations:', prompt).group(1):
+        prompt = re.sub(r'Human: (.*?) \n\nConversations:', f'Human: {prompt_input} \n\nConversations:', prompt)
+    
+    # Create an instance of the custom class
+    anth = AnthropicCustom(api_key=token, model=model, max_tokens=token_length, prompt= prompt)
+    
+    # Create a generator to stream the response
+    response_accumulated = ""
+    async for response in anth.get_anthropic_response_async():
+        response_accumulated += response
+        conversation_history = f"{conversation_history} {anthropic.HUMAN_PROMPT} {user_question} {anthropic.AI_PROMPT} {response_accumulated}"
+        yield response_accumulated
+    
+
+async def chat_with_ai(message, history, token,model, token_length, prompt, prompt_input, memory,):
+    global conversation_history
+    if memory:
+        for conversation in history:
+            user_question, response_accumulated = conversation
+            conversation_history = f"{conversation_history} {anthropic.HUMAN_PROMPT} {user_question} {anthropic.AI_PROMPT} {response_accumulated}"
+        prompt = Prompts[prompt].value.format(memory=history, question=message)
+    else:
+        prompt = Prompts[prompt].value.format(memory="", question=message)
+
+    if prompt_input != re.search(r'Human: (.*?) \n\nConversations:', prompt).group(1):
+        prompt = re.sub(r'Human: (.*?) \n\nConversations:', f'Human: {prompt_input} \n\nConversations:', prompt)
+    
+    # Create an instance of the custom class
+    anth = AnthropicCustom(api_key=token, model=model, max_tokens=token_length, prompt= prompt)
+    
+    # Create a generator to stream the response
+    response_accumulated = ""
+    async for response in anth.get_anthropic_response_async():
+        response_accumulated += response
+        yield response_accumulated
+
+promptDropdown:IOComponent = Dropdown(choices=list(Prompts.__members__.keys()),label="Prompt",value=list(Prompts.__members__.keys())[0])
+prompt_input :IOComponent = Textbox(label="Custom Prompt", placeholder="Enter a custom prompt here", lines=3, value=re.search(r'Human: (.*?) \n\nConversations:', Prompts[promptDropdown.value].value).group(1), )
+
+
+iface = gr.Interface(fn=interact_with_ai,
+                     flagging_options=["Inappropriate", "Disrespectful", "Spam"],
+                     allow_flagging='auto',
+                     title="Claude Space",
+                     inputs=[Textbox(label="Question", placeholder="Enter a question here"),Textbox(label="Token", placeholder="Enter a token here",type='password'),Dropdown(choices=[model.value for model in ClaudeModels],label="Model",value=[model.value for model in ClaudeModels][0]),Dropdown(choices=[token.value for token in ModelTokenLength],label="Token Length",value= [token.value for token in ModelTokenLength][0]),promptDropdown,prompt_input,Checkbox(label="Memory", value=False)], 
+                     outputs="markdown",
+                     cache_examples=True,
+                     )
+
+promptDropdown:IOComponent = Dropdown(choices=list(Prompts.__members__.keys()),label="Prompt",value=list(Prompts.__members__.keys())[0])
+prompt_input :IOComponent = Textbox(label="Custom Prompt", placeholder="Enter a custom prompt here", lines=3, value=re.search(r'Human: (.*?) \n\nConversations:', Prompts[promptDropdown.value].value).group(1), )
+
+cface = gr.ChatInterface(fn=chat_with_ai,additional_inputs=[Textbox(label="Token", placeholder="Enter a token here",type='password'),Dropdown(choices=[model.value for model in ClaudeModels],label="Model",value=[model.value for model in ClaudeModels][0]),Dropdown(choices=[token.value for token in ModelTokenLength],label="Token Length",value= [token.value for token in ModelTokenLength][0]),promptDropdown,prompt_input,Checkbox(label="Memory", value=True)])
+
+
+
+
+if __name__ == "__main__":
+    gd = gr.TabbedInterface([iface, cface], tab_names=["Claude Space", "Claude Chat"],title="Claude Space")
+    gd.queue(concurrency_count=75, max_size=100).launch(debug=True, share=False,server_name='0.0.0.0', server_port=7864)

const.py

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+import enum
+import anthropic
+
+
+class ClaudeModels(str, enum.Enum):
+    Inatant1_1: str = "claude-instant-1"
+    Instant1_2: str = "claude-instant-1.2"
+    Instant1_3: str =  "claude-instant-1.3"
+    Claude2: str  = "claude-2"
+    
+    
+class ModelTokenLength(str, enum.Enum):
+    ten : int = 10
+    twenty_five: int = 25
+    fifty: int = 50
+    hundred: int = 100
+    five_hundred: int = 500
+    one_k: int = 1000
+    five_k: int = 5000
+    ten_k: int = 10000
+    twenty_k: int = 20000
+    fifty_k: int = 50000
+    hundred_k: int = 100000
+    
+    
+class Prompts(str, enum.Enum):
+    general: str = "\n\nHuman: You're a AI bot who loves to gossip. Listin user's query and answer in markdown using Conversations. \n\nConversations: {memory} \n\nQuery: {question} \n\nAssistant:"
+    

makefile

@@ -0,0 +1,2 @@
+start:
+	python app.py    

pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "claude-space"
+version = "0.1.0"
+description = ""
+authors = ["Shreyam Maity <sm8967724231@gmail.com>"]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "claude_space"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+requests = "^2.31.0"
+gradio = "^3.41.2"
+anthropic = "^0.3.10"
+python-dotenv = "^1.0.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

settings.py

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+import os
+
+
+class Settings:
+
+    ANTHROPIC_API_KEY:str=os.environ.get('ANTHROPIC_API_KEY')
+    
+
+settings = Settings()