update.app.py

app.py

@@ -1,63 +1,105 @@
+import asyncio
+from io import BytesIO
+from threading import Lock
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+from fastapi import Body, Depends, FastAPI, HTTPException
+from PIL import Image
+from sqlalchemy import Column, Float, ForeignKey, Integer, String, create_engine
+from sqlalchemy.orm import declarative_base, relationship, sessionmaker
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Database setup
+DATABASE_URL = "sqlite:///./sin_city_rp.db"
+engine = create_engine(DATABASE_URL)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+Base = declarative_base()
+db_lock = Lock()
+
+# Define SQLAlchemy models
+class Player(Base):
+    __tablename__ = "players"
+    id = Column(Integer, primary_key=True, index=True)
+    username = Column(String, unique=True, index=True)
+    email = Column(String, unique=True, index=True)
+    password = Column(String)
+    characters = relationship("Character", back_populates="player")
+
+class Character(Base):
+    __tablename__ = "characters"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, index=True)
+    player_id = Column(Integer, ForeignKey("players.id"))
+    player = relationship("Player", back_populates="characters")
+    level = Column(Integer, default=1)
+    experience = Column(Integer, default=0)
+    health = Column(Integer, default=100)
+    items = relationship("Item", back_populates="character")
+
+class Quest(Base):
+    __tablename__ = "quests"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, index=True)
+    description = Column(String)
+    reward = Column(Integer)
+
+class Item(Base):
+    __tablename__ = "items"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, index=True)
+    description = Column(String)
+    value = Column(Float)
+    character_id = Column(Integer, ForeignKey("characters.id"))
+    character = relationship("Character", back_populates="items")
+
+# Create tables
+Base.metadata.create_all(engine)
 
+# FastAPI setup
+app = FastAPI()
+
+# Dependency for getting the current player
+def get_current_player(username: str = Body(...), password: str = Body(...)):
+    with db_lock:
+        session = SessionLocal()
+        player = session.query(Player).filter(Player.username == username, Player.password == password).first()
+        session.close()
+        if not player:
+            raise HTTPException(status_code=401, detail="Invalid username or password")
+        return player
+
+# Login endpoint
+@app.post("/login")
+async def login(player: Player = Depends(get_current_player)):
+    return {"message": f"Welcome {player.username}!"}
+
+# Load the DALL·E Mini model
+model_name = "flax-community/dalle-mini"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to generate image from text prompt using DALL·E Mini
+async def generate_image(prompt: str):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    image = Image.fromarray(outputs[0].numpy())
+
+    # Convert the image to a format that can be used in FastAPI/Gradio
+    buf = BytesIO()
+    image.save(buf, format='PNG')
+    buf.seek(0)
+    return buf
+
+# Gradio Interface
+def gradio_interface(prompt):
+    response = asyncio.run(generate_image(prompt))
+    return response
+
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(label="Enter prompt"),
+    outputs=gr.Image(label="Generated Image")
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()