Delete app.py

app.py

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-# HuggingFace Spaces file to run a Gradio Interface for the ALBERT v2 Steam Review Constructiveness Classifier by Samuel Ruairí Bullard
-
-# Package Imports
-import gradio as gr
-from transformers import pipeline
-import torch
-
-# Checks if CUDA is available on the machine
-print("CUDA Available: ", torch.cuda.is_available())
-
-# if not os.path.isfile("./README.md"):
-# !git clone https://huggingface.co/spaces/abullard1/albert-v2-steam-review-constructiveness-classifier
-
-# Sets the torch dtype to 16-bit half-precision floating-point format if CUDA is available, otherwise sets it to 32-bit single-precision floating-point format. (Available for GPUs with Tensor Cores like NVIDIA's Volta, Turing, Ampere Architectures have for example)
-device = 0 if torch.cuda.is_available() else -1
-torch_d_type = torch.float16 if torch.cuda.is_available() else torch.float32
-print(f"Device: {device}")
-
-# Defines the name of the base model, the classifier was fine-tuned from
-base_model_name = "albert-base-v2"
-
-# Defines the name of the fine-tuned model used for the steam-review constructiveness classification
-finetuned_model_name = "abullard1/albert-v2-steam-review-constructiveness-classifier"
-
-# PyTorch classifier pipeline
-classifier = pipeline(
-    task="text-classification",  # Defines the task
-    model=finetuned_model_name,  # Defines the fine-tuned model to use
-    tokenizer=base_model_name,  # Defines the tokenizer to use (same as the base model)
-    device=device,  # Defines the device the classification will be run on
-    top_k=None,  # Returns all scores for all labels, not just the one with the highest score
-    truncation=True,  # Truncates the input text if it exceeds the maximum length
-    max_length=512,  # Defines the maximum length of the input text (512 for BERT. Explicitly set here)
-    torch_dtype=torch_d_type
-    # Sets the torch dtype to 16-bit half-precision floating-point format if CUDA is available, otherwise sets it to 32-bit single-precision floating-point format
-)
-
-
-# Extracts the labels and scores from the prediction result
-def classify_steam_review(input_text):
-    result = classifier(input_text)
-
-    label_1, label_2 = result[0][0]["label"], result[0][1]["label"]
-    score_1, score_2 = result[0][0]["score"], result[0][1]["score"]
-
-    return {"label_1": label_1, "score_1": score_1, "label_2": label_2, "score_2": score_2}
-
-
-# Provides a textual representation of the classification result
-def get_steam_review_classification_result_text(label_1, score_1, label_2, score_2):
-    # Maps label values to constructiveness
-    def label_to_constructiveness(label):
-        return "Constructive" if label == "LABEL_1" else "Not Constructive"
-
-    # Formats the output in a readable format
-    def format_output(label, score, emoji):
-        return f'{label_to_constructiveness(label)} with a score of {score}. {emoji}'
-
-    # Determines the label and score with the highest score
-    if score_1 >= score_2:
-        return format_output(label_1, score_1, "👍🏻")
-    else:
-        return format_output(label_2, score_2, "👎🏻")
-
-
-# Examples Steam Reviews to display in the Gradio Interface using the "examples" parameter
-examples = [
-    [
-        "Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"],
-    ["Review: Trash game. Deleted., Playtime: 1, Voted Up: False, Upvotes: 0, Votes Funny: 0"],
-    ["Review: This game is amazing., Playtime: 100, Voted Up: True, Upvotes: 1, Votes Funny: 0"],
-    ["Review: Great game, but the community is toxic., Playtime: 50, Voted Up: True, Upvotes: 1, Votes Funny: 0"]
-]
-
-# HTML article to display in the Gradio Interface using the "article" parameter
-article = (
-    """
-    *Format your input as follows for the best results: **Review**: {review_text}, **Playtime**: {author_playtime_at_review}, **Voted Up**: {voted_up}, **Upvotes**: {upvotes}, **Votes Funny**: {votes_funny}.*
-    """
-)
-
-# Main Gradio Interface using Gradio Blocks
-# Docs: https://www.gradio.app/docs/gradio/blocks
-with gr.Blocks() as steam_reviews_classifier_block:
-    gr.Markdown("## Steam Review Constructiveness Classifier")
-    gr.Markdown(article)
-
-    # Main Column
-    with gr.Column():
-        # Upper Row (Input Textbox, Constructive Label, Not Constructive Label)
-        with gr.Row(equal_height=True):
-            # Input Textbox Column
-            with gr.Column():
-                input_textbox = gr.Textbox(
-                    lines=8,
-                    label="Steam Review",
-                    # info="Input Steam Review here",
-                    placeholder="Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0",
-                    show_copy_button=False,
-                    value="Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"
-                )
-
-            # Constructive and Not Constructive Labels Column
-            with gr.Column():
-                constructive_label = gr.Label(label="Constructive")
-                not_constructive_label = gr.Label(label="Not Constructive")
-
-        # Examples Component
-        example_component = gr.Examples(
-            examples=examples,
-            inputs=input_textbox
-        )
-
-        # Output Textbox which shows the textual representation of the Constructiveness Prediction
-        output_textbox = gr.Textbox(
-            label="Constructiveness Prediction",
-            interactive=False,
-            show_copy_button=False,
-            # info="Textual representation of the Constructiveness Prediction"
-        )
-
-        # Submit Button
-        submit_button = gr.Button(value="Submit")
-
-    # Function to run when the Submit Button is clicked (Passes the input text to the classifier and displays the output text)
-    def on_submit_click(input_text):
-        classification_result = classify_steam_review(input_text)
-        classification_result_text = get_steam_review_classification_result_text(
-            label_1=classification_result["label_1"],
-            score_1=classification_result["score_1"],
-            label_2=classification_result["label_2"],
-            score_2=classification_result["score_2"]
-        )
-        output_text = classification_result_text
-        constructive, not_constructive = str(classification_result["score_1"]), str(classification_result["score_2"])
-        return output_text, constructive, not_constructive
-
-
-    # onClick event for the Submit Button
-    submit_button.click(
-        fn=on_submit_click,
-        inputs=input_textbox,
-        outputs=[output_textbox, not_constructive_label, constructive_label]
-    )
-
-# Launches the Gradio Blocks Interface
-steam_reviews_classifier_block.launch()