chat.py

from flask import Flask, request, jsonify
from huggingface_hub import InferenceClient

# Initialize Flask app
app = Flask(__name__)

print("\nHello welcome to Sema AI\n", flush=True)  # Flush to ensure immediate output

# Initialize InferenceClient
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    # Print user prompt
    print(f"\nUser: {prompt}\n", flush=True)

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    # Get response from Mistral model
    response = client.text_generation(
        formatted_prompt,
        **generate_kwargs,
        stream=True,
        details=True,
        return_full_text=False
    )

    output = ""
    for token in response:
        if hasattr(token, 'token') and hasattr(token.token, 'text'):
            output += token.token.text
        else:
            print(f"Unexpected token structure: {token}", flush=True)

    # Print AI response
    print(f"\nSema AI: {output}\n", flush=True)
    return output

@app.route("/generate", methods=["POST"])
def generate_text():
    data = request.json
    prompt = data.get("prompt", "")
    history = data.get("history", [])
    temperature = data.get("temperature", 0.9)
    max_new_tokens = data.get("max_new_tokens", 256)
    top_p = data.get("top_p", 0.95)
    repetition_penalty = data.get("repetition_penalty", 1.0)

    try:
        response_text = generate(
            prompt,
            history,
            temperature=temperature,
            max_new_tokens=max_new_tokens,
            top_p=top_p,
            repetition_penalty=repetition_penalty
        )
        return jsonify({"response": response_text})
    except Exception as e:
        # Print error
        print(f"Error: {str(e)}", flush=True)
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(debug=True, port=5000)