Add app

Dockerfile

@@ -0,0 +1,31 @@
+FROM python:3.11
+
+RUN apt-get update
+RUN apt-get --yes install graphviz
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Try and run pip command after setting the user with `USER user` to avoid permission issues with Python
+RUN pip install --no-cache-dir --upgrade pip
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+COPY --chown=user requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user app.py app.py
+
+ENTRYPOINT ["solara", "run", "app.py", "--host=0.0.0.0", "--port", "7860"]

LICENSE

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+MIT License
+
+Copyright (c) 2024 Alonso Silva Allende
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -6,6 +6,7 @@ colorTo: gray
 sdk: docker
 pinned: false
 license: mit
+app_port: 7860
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import solara
+from typing import Any, Callable, Optional, TypeVar, Union, cast, overload, List
+from typing_extensions import TypedDict
+import time
+import ipyvue
+import reacton
+from solara.alias import rv as v
+import os
+import openai
+from openai import OpenAI
+import instructor
+from pydantic import BaseModel, Field
+from graphviz import Digraph
+
+
+# NEEDED FOR INPUT TEXT AREA INSTEAD OF INPUT TEXT
+def use_change(el: reacton.core.Element, on_value: Callable[[Any], Any], enabled=True):
+    """Trigger a callback when a blur events occurs or the enter key is pressed."""
+    on_value_ref = solara.use_ref(on_value)
+    on_value_ref.current = on_value
+    def add_events():
+        def on_change(widget, event, data):
+            if enabled:
+                on_value_ref.current(widget.v_model)
+        widget = cast(ipyvue.VueWidget, solara.get_widget(el))
+        if enabled:
+            widget.on_event("blur", on_change)
+            widget.on_event("keyup.enter", on_change)
+        def cleanup():
+            if enabled:
+                widget.on_event("blur", on_change, remove=True)
+                widget.on_event("keyup.enter", on_change, remove=True)
+        return cleanup
+    solara.use_effect(add_events, [enabled])
+
+
+@solara.component
+def InputTextarea(
+    label: str,
+    value: Union[str, solara.Reactive[str]] = "",
+    on_value: Callable[[str], None] = None,
+    disabled: bool = False,
+    password: bool = False,
+    continuous_update: bool = False,
+    error: Union[bool, str] = False,
+    message: Optional[str] = None,
+):
+    reactive_value = solara.use_reactive(value, on_value)
+    del value, on_value
+    def set_value_cast(value):
+        reactive_value.value = str(value)
+    def on_v_model(value):
+        if continuous_update:
+            set_value_cast(value)
+    messages = []
+    if error and isinstance(error, str):
+        messages.append(error)
+    elif message:
+        messages.append(message)
+    text_area = v.Textarea(
+        v_model=reactive_value.value,
+        on_v_model=on_v_model,
+        label=label,
+        disabled=disabled,
+        type="password" if password else None,
+        error=bool(error),
+        messages=messages,
+        solo=True,
+        hide_details=True,
+        outlined=True,
+        rows=1,
+        auto_grow=True,
+    )
+    use_change(text_area, set_value_cast, enabled=not continuous_update)
+    return text_area
+
+# EXTRACTION
+openai.api_key = os.environ['OPENAI_API_KEY']
+client = instructor.from_openai(OpenAI())
+
+class Node(BaseModel):
+    id: int
+    label: str
+    color: str
+
+class Edge(BaseModel):
+    source: int
+    target: int
+    label: str
+    color: str = "black"
+
+class KnowledgeGraph(BaseModel):
+    nodes: List[Node] = Field(description="Nodes in the knowledge graph")
+    edges: List[Edge] = Field(description="Edges in the knowledge graph")
+
+class MessageDict(TypedDict):
+    role: str
+    content: str
+
+def add_chunk_to_ai_message(chunk: str):
+    messages.value = [
+        *messages.value[:-1],
+        {
+            "role": "assistant",
+            "content": chunk,
+        },
+    ]
+
+import ast
+
+# DISPLAYED OUTPUT
+@solara.component
+def ChatInterface():
+    with solara.lab.ChatBox():
+        if len(messages.value)>0:
+            if messages.value[-1]["role"] != "user":
+                obj = messages.value[-1]["content"]
+                if f"{obj}" != "":
+                    obj = ast.literal_eval(f"{obj}")
+                    dot = Digraph(comment="Knowledge Graph")
+                    if obj['nodes'] not in [None, []]:
+                        if obj['nodes'][0]['label'] not in [None, '']:
+                            for i, node in enumerate(obj['nodes']):
+                                if obj['nodes'][i]['label'] not in [None, '']:
+                                    dot.node(name=str(obj['nodes'][i]['id']), label=obj['nodes'][i]['label'], color=obj['nodes'][i]['color'])
+                    if obj['edges'] not in [None, []]:
+                        if obj['edges'][0]['label'] not in [None, '']:
+                            for i, edge in enumerate(obj['edges']):
+                                if obj['edges'][i]['source'] not in [None,''] and obj['edges'][i]['target'] not in [None,''] and obj['edges'][i]['label'] not in [None,'']:
+                                    dot.edge(str(obj['edges'][i]['source']), str(obj['edges'][i]['target']), label=obj['edges'][i]['label'], color=obj['edges'][i]['color'])
+                    solara.display(dot)
+
+messages: solara.Reactive[List[MessageDict]] = solara.reactive([])
+aux = solara.reactive("")
+text_block = solara.reactive("Alice loves Bob while Charles hates both Alice and Bob.")
+@solara.component
+def Page():
+    title = "Knowledge Graph Generator"
+    with solara.Head():
+        solara.Title(f"{title}")
+    with solara.Column(style={"width": "70%", "padding": "50px"}):
+        solara.Markdown(f"#{title}")
+        solara.Markdown("Enter some text and the language model will try to describe it as a knowledge graph. Done with :heart: by [alonsosilva](https://twitter.com/alonsosilva)")
+        extraction_stream = client.chat.completions.create_partial(
+            model="gpt-3.5-turbo",
+            response_model=KnowledgeGraph,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Help me understand the following by describing it as small knowledge graph: {text_block}",
+                },
+            ],
+            temperature=0,
+            stream=True,
+        )
+
+        user_message_count = len([m for m in messages.value if m["role"] == "user"])
+        def send():
+            messages.value = [*messages.value, {"role": "user", "content": "Hello"}]
+        def response(message):
+            for extraction in extraction_stream:
+                obj = extraction.model_dump()
+                if f"{obj}" != aux.value:
+                    add_chunk_to_ai_message(f"{obj}")
+                    aux.value = f"{obj}"
+        def result():
+            if messages.value != []:
+                response(messages.value[-1]["content"])
+        result = solara.lab.use_task(result, dependencies=[user_message_count])
+        InputTextarea("Enter text:", value=text_block, continuous_update=True)
+        solara.Button(label="Generate Knowledge Graph", on_click=send)
+        ChatInterface()
+Page()

packages.txt

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+graphviz

requirements.txt

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+solara==1.31.0
+openai==1.17.0
+instructor==1.1.0
+graphviz==0.20.3