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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Charger le modèle fine-tuné
MODEL_NAME = "fatmata/psybot"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
print("✅ Modèle chargé avec succès :", model.config) # Debugging
def generate_response(user_input):
""" Génère une réponse du chatbot PsyBot """
prompt = f"<|startoftext|><|user|> {user_input} <|bot|>"
print(f"🔹 Prompt envoyé au modèle : {prompt}") # Debugging
inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
with torch.no_grad():
output = model.generate(
inputs,
max_new_tokens=100,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.9,
repetition_penalty=1.2
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(f"🔹 Réponse brute du modèle : {response}") # Debugging
if "<|bot|>" in response:
response = response.split("<|bot|>")[-1].strip()
return response
# Interface Gradio avec le bon modèle
iface = gr.Interface(fn=generate_response, inputs="text", outputs="text")
iface.launch(server_name="0.0.0.0", server_port=7860)
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