update in App.py

app.py

@@ -11,7 +11,7 @@ elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
     device = "mps"
 
 model = GPT(GPTConfig())
-ckpt = torch.load("gpt2.pt", map_location=torch.device(device))
+ckpt = torch.load("model.pt", map_location=torch.device(device))
 unwanted_prefix = '_orig_mod.'
 for k,v in list(ckpt.items()):
     if k.startswith(unwanted_prefix):
@@ -28,22 +28,13 @@ def inference(input_text, num_return_sequences, max_length):
     x = input_tokens.to(device)
 
     while x.size(1) < max_length:
-        # forward the model to get the logits
         with torch.no_grad():
-            logits = model(x)[0] # (B, T, vocab_size)
-            # take the logits at the last position
-            logits = logits[:, -1, :] # (B, vocab_size)
-            # get the probabilities
+            logits = model(x)[0]
+            logits = logits[:, -1, :] 
             probs = F.softmax(logits, dim=-1)
-            # do top-k sampling of 50 (huggingface pipeline default)
-            # topk_probs here becomes (5, 50), topk_indices is (5, 50)
             topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
-            # select a token from the top-k probabilities
-            # note: multinomial does not demand the input to sum to 1
             ix = torch.multinomial(topk_probs, 1) # (B, 1)
-            # gather the corresponding indices
             xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
-            # append to the sequence
             x = torch.cat((x, xcol), dim=1)
 
     decode_list = []
@@ -56,25 +47,25 @@ def inference(input_text, num_return_sequences, max_length):
     output = "\n======\n".join(decode_list)
     return output
 
-title = "GPT-2 trained on Shakespeare Plays dataset"
-description = "A simple Gradio interface to generate text from GPT-2 model trained on Shakespeare Plays"
-examples = [["Please put on these earmuffs because I can't hear you.", 5, 50], 
-           ["Twin 4-month-olds slept in the shade of the palm tree while the mother tanned in the sun.", 5, 50], 
-           ["Happiness can be found in the depths of chocolate pudding.", 5, 50], 
-           ["Seek success, but always be prepared for random cats.", 5, 50], 
-           ["This made him feel like an old-style rootbeer float smells.", 5, 50], 
-           ["The view from the lighthouse excited even the most seasoned traveler.", 5, 50], 
-           ["I've always wanted to go to Tajikistan, but my cat would miss me.", 5, 50], 
-           ["He found rain fascinating yet unpleasant.", 5, 50], 
-           ["Plans for this weekend include turning wine into water.", 5, 50], 
-           ["Iron pyrite is the most foolish of all minerals.", 5, 50], 
+title = "GPT-2"
+description = "A simple Gradio interface to generate text from GPT-2 model trained on Shakespeare Plays dataset"
+examples = [["This above all: to thine own self be true", 5, 50], 
+           ["Our doubts are traitors, And make us lose the good we oft might win, By fearing to attempt.", 5, 50], 
+           ["There is nothing either good or bad, but thinking makes it so.", 5, 50], 
+           ["I do love nothing in the world so well as you: is not that strange?", 5, 50], 
+           ["The course of true love never did run smooth.", 5, 50], 
+           ["Love looks not with the eyes, but with the mind; and therefore is winged Cupid painted blind.", 5, 50], 
+           ["We know what we are, but know not what we may be.", 5, 50], 
+           ["As, I confess, it is my nature's plague To spy into abuses, and oft my jealousy Shapes faults that are not.", 5, 50], 
+           ["Good company, good wine, good welcome can make good people", 5, 50], 
+           ["Better three hours too soon than a minute late", 5, 50], 
            ]
 demo = gr.Interface(
     inference, 
     inputs = [
         gr.Textbox(label="Enter some text", type="text"),
-        gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Number of outputs"),
-        gr.Slider(minimum=10, maximum=100, step=1, value=50, label="Maximum lenght of a sequence")
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of outputs"),
+        gr.Slider(minimum=10, maximum=500, step=1, value=50, label="Maximum lenght of a sequence")
         ], 
     outputs = [
         gr.Textbox(label="Output", type="text")