Create app.py

app.py

@@ -0,0 +1,101 @@
+import gradio as gr
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Tuple
+from huggingface_hub import InferenceClient
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=os.getenv("HF_TOKEN"))
+
+class ChatRequest(BaseModel):
+    message: str
+    history: List[Tuple[str, str]]
+    system_message: str
+    max_tokens: int
+    temperature: float
+    top_p: float
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    max_tokens,
+    temperature,
+    top_p,
+    system_message: str = """You are a chatbot serving a user a text based adventure. When the user says 'start adventure', you will write a short (((70 word))) adventure story with 2 to 4 choices for the user to take at the end. Progress the story based on their choices. Number the choices as 1,2,3 and 4 etc. Don't take the choice yourself. Wait for the user to respond.""",
+):
+    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
+
+@app.post("/chat")
+async def chat_endpoint(request: ChatRequest):
+    try:
+        response = respond(
+            request.message,
+            request.history,
+            request.max_tokens,
+            request.temperature,
+            request.top_p,
+            request.system_message,
+        )
+        return {"response": list(response)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Gradio interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Slider(minimum=1, maximum=2048, value=250, 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)",
+        ),
+    ],
+)
+
+# Mount the Gradio app
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)