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README.md

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+
+---
+license: mit
+---
+
+# Innovative AI Model: 10 Concepts
+
+This repository contains an innovative AI model that merges 10 different concepts to showcase the power and versatility of modern AI. Each concept is implemented as a separate Python script, and a unified interface is provided to run them.
+
+## Concepts
+
+1.  **Multimodal Classification:** Classifies an image based on a text description.
+2.  **Music Generation:** Generates a simple music sequence.
+3.  **Sentiment Analysis:** Analyzes the sentiment of a given text.
+4.  **Chatbot:** A simple conversational chatbot.
+5.  **Reasoning:** Answers a question based on a given context.
+6.  **Model Interpretability:** Explains the prediction of a text classification model using LIME.
+7.  **Emotional Text-to-Speech:** Generates speech from text with a simulated emotion.
+8.  **Adaptive Soundtrack:** Generates a music sequence with a mood based on the sentiment of the input text.
+9.  **Agentic AI:** An agent that analyzes the sentiment of a text and reads it aloud with the corresponding emotion.
+10. **Medical Imaging Analysis:** Classifies a medical image.
+
+## How to Use
+
+To use the model, first install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+Then, run the `main.py` script with the concept number you want to try:
+
+```bash
+python main.py <concept_number>
+```
+
+For example, to run the sentiment analysis concept, use the following command:
+
+```bash
+python main.py 3
+```

concept_10_medical_imaging.py

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+
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import requests
+from datasets import load_dataset
+
+def classify_medical_image(image_path):
+    """
+    Classifies a medical image using a pretrained model.
+    """
+    # Load a pretrained model
+    processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+    # Load an image
+    image = Image.open(image_path).convert("RGB")
+
+    # Preprocess the image and predict the class
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+if __name__ == '__main__':
+    # Load a sample image from the chest x-ray dataset
+    dataset = load_dataset("keremam/chest-xray-classification-augmented", split="train")
+    image_to_classify = dataset[0]["image"]
+    image_to_classify.save("medical_image.png")
+
+    predicted_class = classify_medical_image("medical_image.png")
+    print("Predicted class:", predicted_class)

concept_1_multimodal.py

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+
+import torch
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+
+def classify_image(image_path, text_labels):
+    """
+    Classifies an image based on a list of text labels using a CLIP model.
+    """
+    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+    image = Image.open(image_path)
+    inputs = processor(text=text_labels, images=image, return_tensors="pt", padding=True)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    logits_per_image = outputs.logits_per_image
+    probs = logits_per_image.softmax(dim=1)
+    
+    return dict(zip(text_labels, probs.tolist()[0]))
+
+if __name__ == '__main__':
+    # Create a dummy image for testing
+    dummy_image = Image.new('RGB', (100, 100), color = 'red')
+    dummy_image.save("dummy_image.png")
+
+    labels = ["a red square", "a blue circle", "a green triangle"]
+    probabilities = classify_image("dummy_image.png", labels)
+    print("Probabilities:", probabilities)
+    print("Predicted label:", max(probabilities, key=probabilities.get))
+    

concept_2_music_generation.py

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+
+import numpy as np
+from pydub import AudioSegment
+from pydub.generators import Sine
+
+def generate_music_sequence(note_sequence, output_path="music_sequence.wav"):
+    """
+    Generates a simple music sequence from a list of notes and saves it as a WAV file.
+    """
+    duration = 500  # milliseconds for each note
+    song = AudioSegment.empty()
+    for note in note_sequence:
+        song += Sine(note).to_audio_segment(duration=duration)
+    song.export(output_path, format="wav")
+
+if __name__ == '__main__':
+    # A simple melody sequence (frequency in Hz)
+    melody = [261.63, 293.66, 329.63, 349.23, 392.00, 440.00, 493.88, 523.25]
+    generate_music_sequence(melody)
+    print("Music sequence generated and saved to music_sequence.wav")

concept_3_sentiment_analysis.py

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+
+from transformers import pipeline
+
+def analyze_sentiment(text):
+    """
+    Analyzes the sentiment of a given text using a pretrained model.
+    """
+    sentiment_pipeline = pipeline("sentiment-analysis")
+    result = sentiment_pipeline(text)
+    return result
+
+if __name__ == '__main__':
+    text_to_analyze = "I love the new AI model! It's so innovative and powerful."
+    sentiment = analyze_sentiment(text_to_analyze)
+    print("Sentiment analysis result:", sentiment)

concept_4_chatbot.py

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+
+from transformers import pipeline, Conversation
+
+def chat_with_bot(message, conversation_history=None):
+    """
+    Has a conversation with a chatbot using a pretrained model.
+    """
+    chatbot_pipeline = pipeline("conversational")
+    if conversation_history:
+        conversation = Conversation(text=message, past_user_inputs=conversation_history['past_user_inputs'], generated_responses=conversation_history['generated_responses'])
+    else:
+        conversation = Conversation(text=message)
+    
+    result = chatbot_pipeline(conversation)
+    return result.generated_responses[-1], {'past_user_inputs': result.past_user_inputs, 'generated_responses': result.generated_responses}
+
+if __name__ == '__main__':
+    user_message = "Hello, how are you?"
+    response, history = chat_with_bot(user_message)
+    print("Bot:", response)
+
+    user_message = "What is the weather like today?"
+    response, history = chat_with_bot(user_message, conversation_history=history)
+    print("Bot:", response)

concept_5_reasoning.py

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+
+from transformers import pipeline
+
+def perform_reasoning(context, question):
+    """
+    Performs a simple reasoning task by answering a question based on a given context.
+    """
+    qa_pipeline = pipeline("question-answering")
+    result = qa_pipeline(question=question, context=context)
+    return result
+
+if __name__ == '__main__':
+    context = "The new AI model is capable of performing a wide range of tasks, including multimodal classification, music generation, and sentiment analysis. It is designed to be a versatile and powerful tool for developers."
+    question = "What tasks can the new AI model perform?"
+    answer = perform_reasoning(context, question)
+    print("Reasoning task result:", answer)

concept_6_interpretability.py

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+
+import lime
+import lime.lime_text
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+import numpy as np
+
+def explain_prediction(text):
+    """
+    Explains the prediction of a text classification model using LIME.
+    """
+    # Create a dummy dataset
+    data = ["I love this product", "This is a terrible movie", "The book was okay", "I hate this"]
+    labels = [1, 0, 0, 0]
+
+    # Create a simple text classification pipeline
+    vectorizer = TfidfVectorizer()
+    classifier = LogisticRegression()
+    pipeline = make_pipeline(vectorizer, classifier)
+    pipeline.fit(data, labels)
+
+    # Create a LIME explainer
+    explainer = lime.lime_text.LimeTextExplainer(class_names=['negative', 'positive'])
+    
+    # Explain the prediction for the given text
+    explanation = explainer.explain_instance(text, pipeline.predict_proba, num_features=6)
+    return explanation.as_list()
+
+if __name__ == '__main__':
+    text_to_explain = "This is a fantastic and innovative AI model."
+    explanation = explain_prediction(text_to_explain)
+    print("LIME explanation:", explanation)

concept_7_emotional_tts.py

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+
+from gtts import gTTS
+from pydub import AudioSegment
+import os
+
+def emotional_tts(text, emotion="neutral", output_path="emotional_speech.mp3"):
+    """
+    Generates speech from text with a simulated emotion.
+    """
+    tts = gTTS(text, lang='en', slow=False)
+    tts.save("temp.mp3")
+
+    # Load the audio and manipulate it to simulate emotion
+    audio = AudioSegment.from_mp3("temp.mp3")
+    if emotion == "happy":
+        # Faster speed, higher pitch
+        audio = audio.speedup(playback_speed=1.2)
+        audio = audio._spawn(audio.raw_data, overrides={
+             "frame_rate": int(audio.frame_rate * 1.1)
+        }).set_frame_rate(audio.frame_rate)
+    elif emotion == "sad":
+        # Slower speed, lower pitch
+        audio = audio.speedup(playback_speed=0.9)
+        audio = audio._spawn(audio.raw_data, overrides={
+             "frame_rate": int(audio.frame_rate * 0.9)
+        }).set_frame_rate(audio.frame_rate)
+    
+    audio.export(output_path, format="mp3")
+    os.remove("temp.mp3")
+
+if __name__ == '__main__':
+    text = "This is a demonstration of emotional text-to-speech."
+    emotional_tts(text, emotion="happy")
+    print("Happy speech saved to emotional_speech.mp3")
+    emotional_tts(text, emotion="sad", output_path="sad_speech.mp3")
+    print("Sad speech saved to sad_speech.mp3")

concept_8_adaptive_soundtrack.py

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+
+from transformers import pipeline
+import numpy as np
+from pydub import AudioSegment
+from pydub.generators import Sine
+
+def generate_adaptive_soundtrack(text, output_path="adaptive_soundtrack.wav"):
+    """
+    Generates a music sequence with a mood based on the sentiment of the input text.
+    """
+    # Analyze the sentiment of the text
+    sentiment_pipeline = pipeline("sentiment-analysis")
+    sentiment = sentiment_pipeline(text)[0]['label']
+
+    # Generate a music sequence based on the sentiment
+    duration = 500  # milliseconds for each note
+    song = AudioSegment.empty()
+    if sentiment == "POSITIVE":
+        # Happy melody (major scale)
+        melody = [261.63, 293.66, 329.63, 349.23, 392.00, 440.00, 493.88, 523.25]
+    else:
+        # Sad melody (minor scale)
+        melody = [261.63, 293.66, 311.13, 349.23, 392.00, 415.30, 466.16, 523.25]
+    
+    for note in melody:
+        song += Sine(note).to_audio_segment(duration=duration)
+    song.export(output_path, format="wav")
+
+if __name__ == '__main__':
+    positive_text = "This is a wonderful day!"
+    generate_adaptive_soundtrack(positive_text, output_path="happy_soundtrack.wav")
+    print("Happy soundtrack generated and saved to happy_soundtrack.wav")
+
+    negative_text = "I'm feeling down today."
+    generate_adaptive_soundtrack(negative_text, output_path="sad_soundtrack.wav")
+    print("Sad soundtrack generated and saved to sad_soundtrack.wav")

concept_9_agentic_ai.py

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+
+from transformers import pipeline
+from gtts import gTTS
+from pydub import AudioSegment
+import os
+
+def emotional_tts(text, emotion="neutral", output_path="emotional_speech.mp3"):
+    """
+    Generates speech from text with a simulated emotion.
+    """
+    tts = gTTS(text, lang='en', slow=False)
+    tts.save("temp.mp3")
+
+    audio = AudioSegment.from_mp3("temp.mp3")
+    if emotion == "POSITIVE":
+        audio = audio.speedup(playback_speed=1.2)
+        audio = audio._spawn(audio.raw_data, overrides={"frame_rate": int(audio.frame_rate * 1.1)}).set_frame_rate(audio.frame_rate)
+    elif emotion == "NEGATIVE":
+        audio = audio.speedup(playback_speed=0.9)
+        audio = audio._spawn(audio.raw_data, overrides={"frame_rate": int(audio.frame_rate * 0.9)}).set_frame_rate(audio.frame_rate)
+    
+    audio.export(output_path, format="mp3")
+    os.remove("temp.mp3")
+
+def agentic_ai(text):
+    """
+    An agent that analyzes the sentiment of a text and reads it aloud with the corresponding emotion.
+    """
+    # Analyze the sentiment of the text
+    sentiment_pipeline = pipeline("sentiment-analysis")
+    sentiment = sentiment_pipeline(text)[0]['label']
+
+    # Generate emotional speech
+    emotional_tts(text, emotion=sentiment, output_path="agent_speech.mp3")
+    print(f"Agent read the text with a {sentiment} emotion and saved it to agent_speech.mp3")
+
+if __name__ == '__main__':
+    text_to_process = "I am so excited about the future of AI!"
+    agentic_ai(text_to_process)

main.py

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+
+import argparse
+import os
+
+def main():
+    parser = argparse.ArgumentParser(description="A unified interface for the innovative AI model.")
+    parser.add_argument("concept", type=int, help="The concept number to run (1-10).")
+    args = parser.parse_args()
+
+    concept_scripts = {
+        1: "concept_1_multimodal.py",
+        2: "concept_2_music_generation.py",
+        3: "concept_3_sentiment_analysis.py",
+        4: "concept_4_chatbot.py",
+        5: "concept_5_reasoning.py",
+        6: "concept_6_interpretability.py",
+        7: "concept_7_emotional_tts.py",
+        8: "concept_8_adaptive_soundtrack.py",
+        9: "concept_9_agentic_ai.py",
+        10: "concept_10_medical_imaging.py",
+    }
+
+    if args.concept in concept_scripts:
+        script_to_run = os.path.join(os.path.dirname(__file__), concept_scripts[args.concept])
+        os.system(f"python {script_to_run}")
+    else:
+        print("Invalid concept number. Please choose a number between 1 and 10.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+transformers
+torch
+Pillow
+lime
+shap
+pydub
+gtts
+librosa
+soundfile
+numpy
+scikit-learn
+datasets
+huggingface_hub