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from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load the fine-tuned GPT-2 model and tokenizer
fine_tuned_model_path = "./fine-tuned-model"
model = GPT2LMHeadModel.from_pretrained(fine_tuned_model_path)
tokenizer = GPT2Tokenizer.from_pretrained(fine_tuned_model_path)

# Input prompt for text generation
prompt = "what is a star ?"

# Tokenize the input prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
attention_mask = torch.ones_like(input_ids)
pad_token_id = tokenizer.eos_token_id
max_new_tokens = 50

# Generate text using the fine-tuned model
output = model.generate(input_ids, attention_mask=attention_mask, pad_token_id=pad_token_id, max_length=len(input_ids[0]) + max_new_tokens, num_beams=5, no_repeat_ngram_size=2)

# Decode the generated tokens back to text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

# Print the generated text
print("Generated Text:", generated_text)
 

#include "FiveWin.ch"

request dbfcdx

function Main()

    local aPosts := {}, n

    USE posts VIA "dbfcdx"

    INDEX ON posts->topic + posts->date + posts->time + posts->forum TO subject
    GO TOP

    for n = 1 to 20
       AAdd( aPosts, GetTopic() )
    next
    hb_memoWrit( "dataset.json", hb_jsonEncode( aPosts ) )
    XBrowser( aPosts )

return nil

function GetTopic()

    local hTopic := {=>}, cTopic := RTrim( posts->topic )

    hTopic[ "topic" ]    = RTrim( posts->topic )
    hTopic[ "messages" ] = {}

    AAdd( hTopic[ "messages" ], GetPost() )
    SKIP
    while posts->topic == cTopic
       AAdd( hTopic[ "messages" ], GetPost() )
       SKIP
    end

return hTopic    

function GetPost()

    local hPost := {=>}

    hPost[ "topic" ]    = RTrim( posts->topic )
    hPost[ "forum" ]    = RTrim( posts->forum )
    hPost[ "username" ] = RTrim( posts->username )
    hPost[ "date" ]     = posts->date
    hPost[ "time" ]     = posts->time
    hPost[ "text" ]     = posts->text

return hPost    

[
   {  "topic": the title of the topic,
      "messages":
      [
         {
            "topic": the title of the topic,
            "forum": the forum name,
            "username": name of the author,
            "date": date of the post,
            "time": time of the post,
            "text": text of the post
         },
        next posts for the same topic
      ]
   },
   next topic,
   ...
]