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BOLD / app.py

Update app.py

602e19f over 1 year ago

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	import transformers
	import gradio as gr
	import torch
	import csv

	# Load a pre-trained model
	model = transformers.AutoModel.from_pretrained("Wessel/DiabloGPT-medium-harrypotter")
	model.eval()

	# Define a function to run the model on input text
	def predict_sentiment(input_text):
	input_ids = transformers.BertTokenizer.encode(input_text, add_special_tokens=True)
	input_ids = torch.tensor(input_ids).unsqueeze(0)
	outputs = model(input_ids)
	logits = outputs[0]
	sentiment = "Positive" if logits[0][0] > 0 else "Negative"
	return sentiment

	# Create a chat history to store previous inputs and outputs
	chat_history = []

	# Define a function to update the chat history
	def update_history(input_text, sentiment):
	chat_history.append(f"User: {input_text}")
	chat_history.append(f"Model: {sentiment}")

	# Read the prompts from a CSV file
	prompts = []
	with open("prompts.csv") as csvfile:
	reader = csv.reader(csvfile)
	for row in reader:
	prompts.append(row[0])

	# Create an input interface using Gradio
	inputs = gr.inputs.Dropdown(prompts, default=prompts[0])

	# Create an output interface using Gradio
	outputs = gr.outputs.Chatbox(label="Sentiment", lines=1)

	# Run the interface
	interface = gr.Interface(predict_sentiment, inputs, outputs, title="Sentiment Analysis",
	on_output=update_history)
	interface.launch()