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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
@app.route("/")
def hello():
return "hello 🤗, Welcome to Sema AI Chat Service."
# 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):
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:
output += token.token.text
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)
print(f"{prompt}: \n")
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:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
app.run(debug=True, port=5000)
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