Upload handler.py

handler.py

@@ -0,0 +1,99 @@
+'''
+# upload model
+import torch
+from transformers import GPT2LMHeadModel,GPT2Tokenizer, GPT2Config
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = torch.load('text_summary_4sets_2_550.pth', map_location=torch.device('mps'))
+
+model.push_to_hub(repo_name="text-summary-gpt2-short", repo_id="Lin0He/text-summary-gpt2-short")
+tokenizer.push_to_hub(repo_name="text-summary-gpt2-short", repo_id="Lin0He/text-summary-gpt2-short")
+'''
+import torch
+from transformers import pipeline, AutoModel, AutoTokenizer
+
+def topk(probs, n=9):
+    # The scores are initially softmaxed to convert to probabilities
+    probs = torch.softmax(probs, dim= -1)
+
+    # PyTorch has its own topk method, which we use here
+    tokensProb, topIx = torch.topk(probs, k=n)
+
+    # The new selection pool (9 choices) is normalized
+    tokensProb = tokensProb / torch.sum(tokensProb)
+
+    # Send to CPU for numpy handling
+    tokensProb = tokensProb.cpu().detach().numpy()
+
+    # Make a random choice from the pool based on the new prob distribution
+    choice = np.random.choice(n, 1, p = tokensProb)#[np.argmax(tokensProb)]#
+    tokenId = topIx[choice][0]
+
+    return int(tokenId)
+
+def model_infer(model, tokenizer, review, max_length=30):
+    # Preprocess the init token (task designator)
+    review_encoded = tokenizer.encode(review)
+    result = review_encoded
+    initial_input = torch.tensor(review_encoded).unsqueeze(0).to(device)
+
+    with torch.set_grad_enabled(False):
+        # Feed the init token to the model
+        output = model(initial_input)
+
+        # Flatten the logits at the final time step
+        logits = output.logits[0,-1]
+
+        # Make a top-k choice and append to the result
+        #choices = [topk(logits) for i in range(5)]
+        choices = topk(logits)
+        result.append(choices)
+        
+        # For max_length times:
+        for _ in range(max_length):
+            # Feed the current sequence to the model and make a choice
+            input = torch.tensor(result).unsqueeze(0).to(device)
+            output = model(input)
+            logits = output.logits[0,-1]
+            res_id = topk(logits)
+
+            # If the chosen token is EOS, return the result
+            if res_id == tokenizer.eos_token_id:
+                return tokenizer.decode(result)
+            else: # Append to the sequence
+                result.append(res_id)
+
+    # IF no EOS is generated, return after the max_len
+    return tokenizer.decode(result)
+
+def predict(text):
+    result_text = []
+    for i in range(6):
+        summary = model_infer(model, tokenizer, input+"TL;DR").strip()
+        result_text.append(summary[len(input)+5:])
+    return sorted(result_text, key=len)[3]
+    #print("summary:", sorted(result_text, key=len)[3])
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and tokenizer from path
+        tokenizer = AutoTokenizer.from_pretrained("Lin0He/text-summary-gpt2-short")
+        model = AutoModel.from_pretrained("Lin0He/text-summary-gpt2-short")
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        result = predict(inputs)
+        return result
+        
+
+        
+'''
+predictor = pipeline("summarization", model = model, tokenizer = tokenizer)
+result = predictor("Input text for prediction")
+print(result)
+'''