Create app.py

app.py

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+
+import torch
+
+import gradio as gr
+import torch.nn.functional as F
+
+from transformers import AutoTokenizer, GPT2LMHeadModel
+tokenizer = AutoTokenizer.from_pretrained("supermy/jinyong")
+model = GPT2LMHeadModel.from_pretrained("supermy/jinyong")
+model.eval()
+
+def top_k_top_p_filtering( logits, top_k=0, top_p=0.0, filter_value=-float('Inf') ):
+    assert logits.dim() == 1
+    top_k = min( top_k, logits.size(-1) )
+    if top_k > 0:
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+    if top_p > 0.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum( F.softmax(sorted_logits, dim=-1), dim=-1 )
+        sorted_indices_to_remove = cumulative_probs > top_p
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+        indices_to_remove = sorted_indices[sorted_indices_to_remove]
+        logits[indices_to_remove] = filter_value
+    return logits
+
+def generate(title, context, max_len):
+
+    # input_ids.extend( tokenizer.encode(input_text + "-", add_special_tokens=False) )
+
+    title_ids = tokenizer.encode(title, add_special_tokens=False)
+    context_ids = tokenizer.encode(context, add_special_tokens=False)
+    input_ids = title_ids + [sep_id] + context_ids
+    print(input_ids) 
+    cur_len = len(input_ids)
+    input_len = cur_len
+    last_token_id = input_ids[-1]
+    input_ids = torch.tensor([input_ids], dtype=torch.long)
+    
+    # input_ids = [tokenizer.cls_token_id]
+    # input_ids.extend( tokenizer.encode(title + "-" +context, add_special_tokens=False) )
+    # input_ids = torch.tensor( [input_ids] )
+
+    print(input_ids) 
+
+    while True:
+        outputs = model( input_ids=input_ids[:, -200:] )
+        logits = outputs.logits
+        next_token_logits = logits[0, -1, :]
+        next_token_logits = next_token_logits / 1
+        next_token_logits[unk_id] = -float('Inf')
+        filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=0, top_p=0.85)
+        next_token_id = torch.multinomial( F.softmax(filtered_logits, dim=-1), num_samples=1 )
+        input_ids = torch.cat( ( input_ids, next_token_id.unsqueeze(0) ), dim=1 )
+        cur_len += 1
+        word = tokenizer.convert_ids_to_tokens( next_token_id.item() )
+        if cur_len >= ( input_len + max_len ) and last_token_id == 8 and next_token_id == 3:
+            break
+        if cur_len >= ( input_len + max_len ) and word in [".", "。", "！", "!", "?", "？", ",", "，"]:
+            break
+        if next_token_id == eod_id:
+            break
+    result = tokenizer.decode( input_ids.squeeze(0) )
+    return result
+
+if __name__ == '__main__':
+    eod_id = tokenizer.convert_tokens_to_ids("<eod>")
+    sep_id = tokenizer.sep_token_id
+    unk_id = tokenizer.unk_token_id
+ 
+    
+    gr.Interface(
+        fn=generate,
+        inputs=[
+            gr.Textbox(lines=1, placeholder="输入文本标题：射雕英雄传", value="射雕英雄传",label="文本标题"),
+            gr.Textbox(lines=7, placeholder="输入文本内容：郭靖练功偶遇高人,见道长那轻功,转头便拜其为师", value="郭靖练功偶遇高人,见道长那轻功,转头便拜其为师。",label="初始文本"),
+            "number"
+            ],
+        outputs=gr.Textbox(lines=15, placeholder="AI生成的文本显示在这里。",label="生成的文本")
+    ).launch()