Upload NLP_Final.py

NLP_Final.py

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+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()