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 @app.route("/chat", methods=["POST"]) def home(): return "Hi!" # API endpoint to handle requests @app.route("/chat", methods=["POST"]) 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)