Delete NLP_Final.py

NLP_Final.py

@@ -1,139 +0,0 @@
-from transformers import pipeline, RobertaTokenizer, RobertaForSequenceClassification
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
-from datasets import load_dataset
-from PIL import Image, ImageDraw, ImageFont
-import textwrap
-import random
-from diffusers import StableDiffusionPipeline
-import torch
-from sklearn.metrics import classification_report, accuracy_score, f1_score
-from sklearn.model_selection import train_test_split  # Import train_test_split
-import gradio as gr
-
-# Load the datasets for emotion detection and quotes
-emotion_dataset = load_dataset("dair-ai/emotion")
-quotes_dataset = load_dataset("Abirate/english_quotes")
-
-# Prepare the Bag-of-Words Model
-vectorizer = CountVectorizer()
-naive_bayes_classifier = MultinomialNB()
-bow_pipeline = make_pipeline(vectorizer, naive_bayes_classifier)
-texts = [example['text'] for example in emotion_dataset['train']]
-labels = [example['label'] for example in emotion_dataset['train']]
-train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)
-bow_pipeline.fit(train_texts, train_labels)  # Train the Bag-of-Words model
-predicted_labels = bow_pipeline.predict(test_texts)
-print("Bag-of-Words Model Evaluation Metrics:")
-print(classification_report(test_labels, predicted_labels))
-print("Accuracy:", accuracy_score(test_labels, predicted_labels))
-print("F1 Score:", f1_score(test_labels, predicted_labels, average='weighted'))
-
-# Load the emotion classification models
-distilbert_classifier = pipeline('text-classification', model='bhadresh-savani/distilbert-base-uncased-emotion')
-roberta_tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-roberta_model = RobertaForSequenceClassification.from_pretrained('roberta-base')
-
-# Assuming there's a test split for evaluation
-test_data = emotion_dataset['test']
-test_texts = [example['text'] for example in test_data]
-test_labels = [example['label'] for example in test_data]
-label_mapping = {0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}
-test_labels = [label_mapping[label].lower() for label in test_labels]
-'''
-# Evaluate DistilBERT
-distilbert_predictions = [distilbert_classifier(text)[0]['label'].lower() for text in test_texts]
-print("DistilBERT Model Evaluation Metrics:")
-print("Accuracy:", accuracy_score(test_labels, distilbert_predictions))
-print("F1 Score:", f1_score(test_labels, distilbert_predictions, average='weighted'))
-'''
-
-# Function to generate an image from a prompt using Stable Diffusion
-def generate_image(prompt):
-    try:
-        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", variant="fp16", torch_dtype=torch.float16).to("cuda")
-        output = pipe(prompt=prompt)
-        image = output.images[0]
-        image.save("output_image.png")
-        return "output_image.png"
-    except Exception as e:
-        print(f"Failed during the image generation process: {e}")
-        return None
-
-# Function to create an image with the quote
-def create_image_with_quote(quote, author, emotion):
-    prompt = f"{emotion} themed image"
-    img_path = generate_image(prompt)
-    if not img_path:
-        print("Image generation failed.")
-        return None
-    img = Image.open(img_path)
-    d = ImageDraw.Draw(img)
-    quote_font = ImageFont.load_default()
-    author_font = ImageFont.load_default()
-    quote_wrapped = textwrap.fill(quote, width=40)
-    author_wrapped = textwrap.fill(f"- {author}", width=40)
-    draw_text_with_background(d, quote_wrapped, author_wrapped, quote_font, author_font,img.width, img.height)
-    return img
-
-def draw_text_with_background(d, quote, author, quote_font, author_font, img_width, img_height):
-    # Calculate text bounding boxes
-    quote_bbox = d.textbbox((0, 0), quote, font=quote_font)
-    author_bbox = d.textbbox((0, 0), author, font=author_font)
-
-    # Calculate the position for the quote
-    quote_x = (img_width - (quote_bbox[2] - quote_bbox[0])) / 2
-    quote_y = (img_height - (quote_bbox[3] - quote_bbox[1])) / 2 - 20  # Slightly above the center
-
-    # Calculate the position for the author
-    author_x = (img_width - (author_bbox[2] - author_bbox[0])) / 2
-    author_y = quote_y + (quote_bbox[3] - quote_bbox[1]) + 10  # Just below the quote
-
-    # Draw background for quote
-    d.rectangle([quote_x - 10, quote_y - 5, quote_x + (quote_bbox[2] - quote_bbox[0]) + 10, quote_y + (quote_bbox[3] - quote_bbox[1]) + 5], fill=(255, 255, 255, 128))
-    # Draw background for author
-    d.rectangle([author_x - 10, author_y - 5, author_x + (author_bbox[2] - author_bbox[0]) + 10, author_y + (author_bbox[3] - author_bbox[1]) + 5], fill=(255, 255, 255, 128))
-
-    # Draw text over the boxes
-    d.text((quote_x, quote_y), quote, font=quote_font, fill="black")
-    d.text((author_x, author_y), author, font=author_font, fill="black")
-
-
-
-
-def predict_emotion(text, model_choice):
-    if model_choice == 'distilbert':
-        result = distilbert_classifier(text)
-        return result[0]['label'].lower()
-    elif model_choice == 'bow':
-        prediction = bow_pipeline.predict([text])[0]
-        return 'positive' if prediction == 1 else 'negative'
-    elif model_choice == 'roberta':
-        inputs = roberta_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-        outputs = roberta_model(**inputs)
-        prediction = torch.argmax(outputs.logits, dim=-1)
-        return 'positive' if prediction.item() == 1 else 'negative'
-
-def evaluate_quotes_for_emotion(emotion):
-    # Filter quotes based on the predicted emotion
-    suitable_quotes = [q for q in quotes_dataset['train'] if emotion.lower() in q['tags']]
-    if not suitable_quotes:
-        suitable_quotes = quotes_dataset['train']  # fallback to any quote if no tags match
-    selected_quote = random.choice(suitable_quotes)
-    return selected_quote['quote'], selected_quote.get('author', 'Unknown')
-
-def predict_emotion_and_generate_quote(feelings, model_choice):
-    emotion = predict_emotion(feelings, model_choice)
-    quote, author = evaluate_quotes_for_emotion(emotion)
-    image = create_image_with_quote(quote, author, emotion)
-    return quote, author, image
-
-iface = gr.Interface(fn=predict_emotion_and_generate_quote, 
-                     inputs=["text", "text"], 
-                     outputs=["text", "text", "image"],
-                     title="Quote Generator: Feeling's Inspired",
-                     description="Enter your feelings and choose a model to receive an inspiring quote with an accompanying image. Model Choices include distilbert,roberta,bow",
-                     allow_flagging=False,
-                     theme="default")
-iface.launch()