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from flask import Flask, request, jsonify | |
from huggingface_hub import InferenceClient | |
# Initialize Flask app and Hugging Face client | |
app = Flask(__name__) | |
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
# Helper function to generate a response from the AI model | |
def generate_response(message, history, 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 = "" | |
# Streaming response from the Hugging Face model | |
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 | |
return response | |
def home(): | |
return "Hi!" | |
# API endpoint to handle requests | |
def chat(): | |
try: | |
data = request.json | |
message = data.get("message", "") | |
history = data.get("history", []) | |
system_message = data.get("system_message", "You are a friendly chatbot.") | |
max_tokens = data.get("max_tokens", 512) | |
temperature = data.get("temperature", 0.7) | |
top_p = data.get("top_p", 0.95) | |
# Validate inputs | |
if not isinstance(history, list) or not all(isinstance(pair, list) for pair in history): | |
return jsonify({"error": "Invalid history format. It should be a list of [message, response] pairs."}), 400 | |
# Generate AI response | |
response = generate_response(message, history, system_message, max_tokens, temperature, top_p) | |
return jsonify({"response": response}) | |
except Exception as e: | |
return jsonify({"error": str(e)}), 500 | |
if __name__ == "__main__": | |
app.run(debug=True) | |