Create app.py

app.py

@@ -0,0 +1,33 @@
+from fastapi import FastAPI, Request
+from transformers import pipeline
+import json
+import os
+
+app = FastAPI()
+
+# Set your Hugging Face API token
+huggingface_token = os.environ.get("huggingface_token")
+
+# Load the model
+generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+
+conversation_history = []
+
+@app.post("/chat")
+async def chat(message: dict):
+    global conversation_history
+    # Extract user message
+    user_message = message["message"]
+    conversation_history.append({"role": "user", "content": user_message})
+
+    messages = [msg["content"] for msg in conversation_history]
+
+    # Add a system message to instruct the model
+    messages.insert(0, "You are a story writer. Whatever the prompt, you always write a short story of 30 words.")
+
+    # Generate response using Hugging Face model
+    reply = generator(messages, max_length=30, do_sample=False)[0]["generated_text"]
+
+    conversation_history.append({"role": "assistant", "content": reply})
+
+    return reply