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| # v1.1 | |
| # %% auto 0 | |
| __all__ = ['s_model', 'qa_model', 'question_1', 'question_2', 'question_3', 'question_4', 'question_5', 'question_6', | |
| 'question_7', 'question_8', 'question_9', 'question_10', 'contexts', 'data', 'context_df', 'a_text', | |
| 'n_slider', 'intf', 'QA_similarity'] | |
| # %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 2 | |
| import pandas as pd | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| s_model = SentenceTransformer('clips/mfaq') | |
| from transformers import pipeline | |
| qa_model = pipeline(task = 'question-answering', | |
| model = 'Francesco-A/bert-finetuned-squad-v1') | |
| # %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 3 | |
| # Define the question(s) | |
| question_1 = "What are the main features of the new XPhone 20?" | |
| question_2 = "What are some benefits of regular exercise?" | |
| question_3 = "What is the color of a rose?" | |
| question_4 = "How does photosynthesis work in plants?" | |
| question_5 = "At what temperature does water boil?" | |
| question_6 = "Where can I find potassium?" | |
| question_7 = "How does the internet function?" | |
| question_8 = "What are the ingredients for making a classic margarita?" | |
| question_9 = "How does cellular respiration work?" | |
| question_10 = "Is money important?" | |
| # Define the contexts as a list of strings | |
| contexts = [ | |
| "The XPhone 20 is expected to come with an improved camera system, featuring advanced image stabilization and enhanced low-light capabilities.", | |
| "Regular exercise has been shown to reduce the risk of chronic diseases such as heart disease, diabetes, and certain types of cancer.", | |
| "Roses come in various colors, including red, pink, yellow, white, and even blue (though blue roses are rare and often created through genetic modification).", | |
| "Photosynthesis occurs in the chloroplasts of plant cells, where chlorophyll captures sunlight and converts it into chemical energy.", | |
| "Water boils at different temperatures depending on factors like altitude and atmospheric pressure. At sea level, it boils at 100 degrees Celsius or 212 degrees Fahrenheit.", | |
| "Potassium is an essential mineral that can be found in various foods such as bananas, potatoes, spinach, and oranges.", | |
| "The internet functions through a complex system of data transmission protocols, routers, and servers that allow for the exchange of information globally.", | |
| "A classic margarita typically consists of tequila, lime juice, triple sec (or orange liqueur), and is often served with a salted rim.", | |
| "Cellular respiration takes place in the mitochondria of cells, where glucose and oxygen are converted into ATP (adenosine triphosphate) and carbon dioxide.", | |
| "Money is a medium of exchange that facilitates transactions of goods and services. Its importance lies in its role in economic systems and the ability to represent value.", | |
| "The XPhone 20 may feature an OLED display for vibrant colors and deep blacks, providing a high-quality visual experience.", | |
| "Exercise releases endorphins, which are chemicals in the brain that help improve mood and reduce feelings of stress and anxiety.", | |
| "Different species of roses can have variations in color, including shades of red, pink, yellow, and white.", | |
| "During photosynthesis, plants also release oxygen as a byproduct, which is essential for the survival of many organisms on Earth.", | |
| "Water boils at a lower temperature at higher altitudes due to the reduced atmospheric pressure. For example, in the mountains, it may boil below 100 degrees Celsius.", | |
| "Potassium is crucial for proper muscle function, nerve function, and maintaining fluid balance in the body.", | |
| "The internet relies on a system of interconnected networks, including wired and wireless connections, to transmit data across the globe.", | |
| "In addition to the core ingredients, a classic margarita can also be garnished with a wedge of lime for added flavor.", | |
| "Cellular respiration involves several stages, including glycolysis, the Krebs cycle, and the electron transport chain, to extract energy from glucose.", | |
| "Money serves as a unit of account, allowing for standardized pricing and valuation of goods and services in economies worldwide." | |
| "The XPhone 20 is rumored to feature a smaller notch, providing more screen real estate for users. This allows for an immersive viewing experience.", | |
| "Photosynthesis is the process by which plants convert carbon dioxide, water, and sunlight into glucose and oxygen.", | |
| "Bananas are a great source of potassium.", | |
| "The theory of relativity was developed by Albert Einstein and revolutionized our understanding of space and time.", | |
| "The Eiffel Tower is located in Paris, France.", | |
| "Reports suggest that the XPhone 20 will have significant improvements in battery life compared to its predecessor. Users can expect a longer-lasting device.", | |
| "A penny saved is a penny earned.", | |
| "Water boils at 100 degrees Celsius.", | |
| "The Great Wall of China is one of the most impressive architectural feats in history.", | |
| "The capital of Japan is Tokyo.", | |
| "One of the anticipated features of the XPhone 20 is a faster and more powerful A16 chip. This will result in smoother and more efficient performance.", | |
| "Roses are red, violets are blue.", | |
| "Regular exercise can help improve cardiovascular health and strengthen muscles.", | |
| "A classic margarita is made with tequila, lime juice, and orange liqueur.", | |
| "Cellular respiration is the process by which cells convert glucose and oxygen into energy, carbon dioxide, and water.", | |
| "The internet is a global network of interconnected computers and servers that allows the sharing of information and resources.", | |
| "Mount Everest is the highest mountain in the world, located in the Himalayas.", | |
| "The sun rises in the east and sets in the west.", | |
| "The Mona Lisa is a famous portrait painting by Leonardo da Vinci.", | |
| "The Declaration of Independence was adopted by the Continental Congress on July 4, 1776.", | |
| ] | |
| # %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 4 | |
| # Function to find similar answers in a list of contexts | |
| def QA_similarity(question, contexts, n_answers=1): | |
| if not isinstance(contexts, pd.DataFrame): | |
| contexts = pd.read_csv(contexts) | |
| contexts.rename(index={0: 'Context'}) | |
| contexts = contexts['Context'].to_list() | |
| else: | |
| contexts = contexts['Context'].to_list() | |
| # to use 'clips/mfaq' questions need to be prepended with <Q>, and answers with <A>. | |
| question = "<Q>"+question | |
| mfaq_contexts = ["<A>"+answer for answer in contexts] | |
| # Import the necessary module for itemgetter | |
| from operator import itemgetter | |
| # Create context embedding | |
| query_embedding = s_model.encode(question) | |
| context_embeddings = s_model.encode(mfaq_contexts) | |
| # Perform semantic search to find similar contexts | |
| data = util.semantic_search(query_embedding, context_embeddings)[0] | |
| # Sort the list of dictionaries by the 'score' key in descending order | |
| sorted_answers = sorted(data, key=itemgetter('score'), reverse=True) | |
| # Extract answer, context and score | |
| answer_ids = [item['corpus_id'] for item in sorted_answers] | |
| context_score = [item['score'] for item in sorted_answers] | |
| answers_scores = [qa_model(question, contexts[n]) for n in answer_ids[:n_answers]] | |
| # Extract relevant information for answers and scores | |
| answer_context = [contexts[n] for n in answer_ids] | |
| answers = [a['answer'] for a in answers_scores] | |
| scores = [s['score'] for s in answers_scores] | |
| # Create a DataFrame for the results | |
| df = pd.DataFrame(zip(answer_ids, answers, context_score, answer_context), columns=["ANSWER_IDX", "ANSWER", "CONTEXT_SCORE", "CONTEXT"]) | |
| return df | |
| # %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 6 | |
| #|export | |
| title = 'SemanticSearch_QA-v2' | |
| description = """ | |
| FAQ retrieval model: [clips/mfaq](https://huggingface.co/clips/mfaq) \n | |
| QA model: [Francesco-A/bert-finetuned-squad-v1](https://huggingface.co/Francesco-A/bert-finetuned-squad-v1) | |
| """ | |
| data = { | |
| 'Context': contexts, | |
| } | |
| context_df = pd.DataFrame(data) | |
| a_text = gr.components.Dataframe(col_count=(1,"fixed"),interactive = True) | |
| n_slider = gr.components.Slider(minimum=1, maximum = 10, label = "Select n answers (max= 10)",step = 1) | |
| intf = gr.Interface(fn=QA_similarity, | |
| inputs= ["text", a_text, n_slider], | |
| outputs= gr.components.Dataframe(), | |
| examples = [[question_1,context_df,3], | |
| [question_2,context_df,5], | |
| [question_4,context_df,10]], | |
| debug=True, | |
| title=title, | |
| description=description, | |
| # article=long_desc | |
| ) | |
| intf.launch(inline=True, | |
| share=True | |
| ) |