Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import os
+
+# Get the Hugging Face token from the environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# Load the tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2', use_auth_token=HF_TOKEN)
+model = GPT2LMHeadModel.from_pretrained('skylersterling/SentimentGPT', use_auth_token=HF_TOKEN)
+model.eval()
+model.to('cpu')
+
+# Define the function that generates text from a prompt
+def generate_text(prompt, temperature, top_p):
+    prompt_with_eos = prompt + " > "  # Add the "EOS" to the end of the prompt
+    input_tokens = tokenizer.encode(prompt_with_eos, return_tensors='pt')
+
+    input_tokens = input_tokens.to('cpu')
+
+    generated_text = prompt_with_eos  # Start with the initial prompt plus "EOS"
+    prompt_length = len(generated_text)
+
+    for _ in range(80):  # Adjust the range to control the number of tokens generated
+        with torch.no_grad():
+            outputs = model(input_tokens)
+            predictions = outputs.logits[:, -1, :] / temperature
+            sorted_logits, sorted_indices = torch.sort(predictions, descending=True)
+            cumulative_probs = torch.cumsum(torch.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]
+            predictions[:, indices_to_remove] = -float('Inf')
+            next_token = torch.multinomial(torch.softmax(predictions, dim=-1), 1)
+        
+        input_tokens = torch.cat((input_tokens, next_token), dim=1)
+        
+        decoded_token = tokenizer.decode(next_token.item())
+        generated_text += decoded_token  # Append the new token to the generated text
+        if decoded_token == "#":  # Stop if the end of sequence token is generated
+            break
+        yield generated_text[prompt_length:]  # Yield the generated text excluding the initial prompt plus "EOS"
+
+# Create a Gradio interface with a text input, sliders for temperature and top_p, and a text output
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.3, label="Temperature"),
+        gr.Slider(minimum=0.05, maximum=1.0, value=0.3, label="Top-p")
+    ],
+    outputs=gr.Textbox(),
+    live=False,
+    description="SentimentGPT processes the sequence and returns a reasonably accurate guess of whether the sentiment behind the input is positive or negative."
+)
+
+interface.launch()