OpenCHAT-mini

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ILLERRAPS commited on Jul 28, 2024

Commit

ef3b11c

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1 Parent(s): b075a9e

Update app.py

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app.py +105 -24

app.py CHANGED Viewed

@@ -1,27 +1,21 @@
-import gradio as gr
-import requests
-from bs4 import BeautifulSoup
-import cv2
-from PIL import Image
-from transformers import pipeline
-from huggingface_hub import InferenceApi
-import sqlite3
 from sqlalchemy import create_engine, Column, Integer, String, MetaData, Table
 from sqlalchemy.orm import sessionmaker
-import json
-import random
-import io
-# Setting up the SQLAlchemy engine and session
 DATABASE_URL = "sqlite:///chatbot.db"
 engine = create_engine(DATABASE_URL)
 Session = sessionmaker(bind=engine)
 session = Session()
 metadata = MetaData()
-# Load the image generation model (for example, using a Hugging Face model)
-image_generator = pipeline("image-generation", model="CompVis/stable-diffusion-v1-4")
 def create_table(table_name, columns):
     if table_name in engine.table_names():
         return f"Table '{table_name}' already exists."
@@ -56,6 +50,82 @@ def edit_table(table_name, columns):
     return f"Table '{table_name}' updated successfully."
 def chatbot_response(task, table_name=None, columns=None):
     if task == "create_table":
         if table_name and columns:
@@ -70,11 +140,8 @@ def chatbot_response(task, table_name=None, columns=None):
     else:
         result = "Unsupported task. Use 'create_table' or 'edit_table'."
-    # Generate a descriptive image based on the response
     description = f"Task: {task}, Table Name: {table_name}, Columns: {columns}"
-    image = generate_image(description)
-    return result, image
 def handle_chatbot(task, table_name, columns):
     if task not in ['create_table', 'edit_table']:
@@ -93,17 +160,31 @@ def generate_image(description):
     return image
 # Gradio interface setup
 task_input = gr.inputs.Textbox(lines=1, placeholder="Task (create_table or edit_table)")
 table_name_input = gr.inputs.Textbox(lines=1, placeholder="Table Name")
 columns_input = gr.inputs.Textbox(lines=2, placeholder="Columns (JSON format: {'column1': 'type', 'column2': 'type'})")
 interface = gr.Interface(
-    fn=handle_chatbot,
     inputs=[task_input, table_name_input, columns_input],
     outputs=[gr.outputs.Textbox(), gr.outputs.Image(type="pil")],
-    title="SQL Database Chatbot with Image Generation",
-    description="A chatbot interface to create and edit SQL tables with image generation."
 )
-if __name__ == "__main__":
-    interface.launch()

+import asyncio
+import json
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Depends
+from fastapi.responses import HTMLResponse
+from fastapi.security import OAuth2PasswordBearer
 from sqlalchemy import create_engine, Column, Integer, String, MetaData, Table
 from sqlalchemy.orm import sessionmaker
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+# Database Setup
 DATABASE_URL = "sqlite:///chatbot.db"
 engine = create_engine(DATABASE_URL)
 Session = sessionmaker(bind=engine)
 session = Session()
 metadata = MetaData()
 def create_table(table_name, columns):
     if table_name in engine.table_names():
         return f"Table '{table_name}' already exists."
     return f"Table '{table_name}' updated successfully."
+app = FastAPI()
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+class ConnectionManager:
+    def __init__(self):
+        self.active_connections: dict[str, WebSocket] = {}
+    async def connect(self, websocket: WebSocket, username: str):
+        await websocket.accept()
+        self.active_connections[username] = websocket
+    def disconnect(self, username: str):
+        self.active_connections.pop(username, None)
+    async def broadcast(self, message: str):
+        for connection in self.active_connections.values():
+            await connection.send_text(message)
+manager = ConnectionManager()
+@app.websocket("/ws/{username}")
+async def websocket_endpoint(websocket: WebSocket, username: str, token: str = Depends(oauth2_scheme)):
+    await manager.connect(websocket, username)
+    try:
+        while True:
+            data = await websocket.receive_text()
+            await manager.broadcast(f"{username}: {data}")
+    except WebSocketDisconnect:
+        manager.disconnect(username)
+@app.post("/token")
+async def login():
+    # Simplified token generation for demo purposes
+    return {"access_token": "fake_token", "token_type": "bearer"}
+@app.post("/chatbot")
+async def chatbot(task: str, table_name: str, columns: str):
+    response, description = handle_chatbot(task, table_name, columns)
+    return {"result": response, "description": description}
+@app.get("/")
+async def get():
+    return HTMLResponse("""
+    <html>
+        <head>
+            <title>Real-time Chat</title>
+        </head>
+        <body>
+            <h1>WebSocket Chat</h1>
+            <input id="messageText" type="text" autocomplete="off"/>
+            <button onclick="sendMessage()">Send</button>
+            <ul id='messages'>
+            </ul>
+            <script>
+                var ws = new WebSocket("ws://localhost:8000/ws/test_user");
+                ws.onmessage = function(event) {
+                    var messages = document.getElementById('messages')
+                    var message = document.createElement('li')
+                    var content = document.createTextNode(event.data)
+                    message.appendChild(content)
+                    messages.appendChild(message)
+                };
+                function sendMessage() {
+                    var input = document.getElementById("messageText")
+                    ws.send(input.value)
+                    input.value = ''
+                }
+            </script>
+        </body>
+    </html>
+    """)
+# Image generation setup
+image_generator = pipeline("image-generation", model="CompVis/stable-diffusion-v1-4")
+# Helper functions
 def chatbot_response(task, table_name=None, columns=None):
     if task == "create_table":
         if table_name and columns:
     else:
         result = "Unsupported task. Use 'create_table' or 'edit_table'."
     description = f"Task: {task}, Table Name: {table_name}, Columns: {columns}"
+    return result, description
 def handle_chatbot(task, table_name, columns):
     if task not in ['create_table', 'edit_table']:
     return image
 # Gradio interface setup
+def handle_gradio_chatbot(task, table_name, columns):
+    response, description = handle_chatbot(task, table_name, columns)
+    image = generate_image(description)
+    return response, image
 task_input = gr.inputs.Textbox(lines=1, placeholder="Task (create_table or edit_table)")
 table_name_input = gr.inputs.Textbox(lines=1, placeholder="Table Name")
 columns_input = gr.inputs.Textbox(lines=2, placeholder="Columns (JSON format: {'column1': 'type', 'column2': 'type'})")
 interface = gr.Interface(
+    fn=handle_gradio_chatbot,
     inputs=[task_input, table_name_input, columns_input],
     outputs=[gr.outputs.Textbox(), gr.outputs.Image(type="pil")],
+    title="Multiplayer Game with SQL Database and Image Generation",
+    description="A multiplayer game interface to create and edit SQL tables with image generation."
 )
+# Function to run FastAPI server in the background
+def run_server():
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+# Run FastAPI server in a separate thread
+import threading
+threading.Thread(target=run_server, daemon=True).start()
+# Launch Gradio interface
+interface.launch()