Delete embedding.py

embedding.py

@@ -1,370 +0,0 @@
-from PyPDF2 import PdfReader
-import requests
-import json
-import os
-import concurrent.futures
-import random
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import google.generativeai as genai
-from langchain_core.messages import HumanMessage
-from io import BytesIO
-import numpy as np
-import re
-import torch
-from transformers import AutoTokenizer, AutoModel
-
-from search import search_images
-
-gemini = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyCo-TeDp0Ou--UwhlTgMwCoTEZxg6-v7wA',temperature = 0.1)
-gemini1 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI',temperature = 0.1)
-gemini2 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBzbZQBffHFK3N-gWnhDDNbQ9yZnZtaS2E',temperature = 0.1)
-gemini3 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBNN4VDMAOB2gSZha6HjsTuH71PVV69FLM',temperature = 0.1)
-
-vision = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyCo-TeDp0Ou--UwhlTgMwCoTEZxg6-v7wA',temperature = 0.1)
-vision1 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI',temperature = 0.1)
-vision2 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyBzbZQBffHFK3N-gWnhDDNbQ9yZnZtaS2E',temperature = 0.1)
-vision3 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyBNN4VDMAOB2gSZha6HjsTuH71PVV69FLM',temperature = 0.1)
-
-tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5',trust_remote_code = True)
-model = AutoModel.from_pretrained('Alibaba-NLP/gte-base-en-v1.5',trust_remote_code = True)
-model.to('cpu')  # Ensure the model is on the CPU
-
-
-genai.configure(api_key="AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI")
-
-def pdf_extractor(link):
-    text = ''
-
-    try:
-        # Fetch the PDF file from the URL
-        response = requests.get(link)
-        response.raise_for_status()  # Raise an error for bad status codes
-
-        # Use BytesIO to handle the PDF content in memory
-        pdf_file = BytesIO(response.content)
-
-        # Load the PDF file
-        reader = PdfReader(pdf_file)
-        for page in reader.pages:
-            text += page.extract_text()  # Extract text from each page
-
-    except requests.exceptions.HTTPError as e:
-        print(f'HTTP error occurred: {e}')
-    except Exception as e:
-        print(f'An error occurred: {e}')
-    
-    return text
-
-def web_extractor(link):
-    text = ''
-
-    try:
-        loader = WebBaseLoader(link)
-        pages = loader.load_and_split()
-
-        for page in pages:
-            text+=page.page_content
-    except:
-        pass
-    
-    return text
-
-def imporve_text(text):
-
-    prompt = f'''
-    Please rewrite the following text to make it short, concise, and of high quality.
-    Ensure that all essential information and key points are retained. 
-    Focus on improving clarity, coherence, and word choice without altering the original meaning.
-
-    text = {text}
-    '''
-
-    model = random.choice([gemini,gemini1,gemini2,gemini3])
-    result = model.invoke(prompt)
-
-    return result.content
-
-def feature_extraction(tag, history , context):
-
-    prompt = f'''
-    You are an intelligent assistant tasked with updating product information. You have two data sources:
-    1. Tag_History: Previously gathered information about the product.
-    2. Tag_Context: New data that might contain additional details.
-    Your job is to read the Tag_Context and update the relevant field in the Tag_History with any new details found. The field to be updated is the {tag} FIELD.
-    Guidelines:
-    - Only add new details that are relevant to the {tag} FIELD.
-    - Do not add or modify any other fields in the Tag_History.
-    - Ensure your response is in coherent sentences, integrating the new details seamlessly into the existing information.
-    Here is the data:
-    Tag_Context: {str(context)}
-    Tag_History: {history}
-    Respond with the updated Tag_History.
-    '''
-
-    model = random.choice([gemini,gemini1,gemini2,gemini3])
-    result = model.invoke(prompt)
-
-    return result.content
-
-def feature_extraction_image(url):
-    text = ' '
-    model = genai.GenerativeModel('gemini-1.5-flash-001')
-    try:
-        res = model.generate_content(['Describe this image to me',url])
-        text = res.text
-
-    except:
-        pass
-    return text
-
-def detailed_feature_extraction(find, context):
-
-    prompt = f'''
-    You are an intelligent assistant tasked with finding product information. You have one data source and one output format:
-    1. Context: The gathered information about the product.
-    2. Format: Details which need to be filled based on Context.
-    Your job is to read the Context and update the relevant field in Format using Context.
-    Guidelines:
-    - Only add details that are relevant to the individual FIELD.
-    - Do not add or modify any other fields in the Format.
-    - If nothing found return None.
-    Here is the data:
-    The Context is {str(context)}
-    The Format is {str(find)}
-    '''
-
-    model = random.choice([gemini,gemini1,gemini2,gemini3])
-    result = model.invoke(prompt)
-
-    return result.content
-
-def detailed_history(history):
-
-    details = {
-    "Introduction": {
-        "Product Name": None,
-        "Overview of the product": None,
-        "Purpose of the manual": None,
-        "Audience": None,
-        "Additional Details": None
-    },
-    "Specifications": {
-        "Technical specifications": None,
-        "Performance metrics": None,
-        "Additional Details": None
-    },
-    "Product Overview": {
-        "Product features": None,
-        "Key components and parts": None,
-        "Additional Details": None
-    },
-    "Safety Information": {
-        "Safety warnings and precautions": None,
-        "Compliance and certification information": None,
-        "Additional Details": None
-    },
-    "Installation Instructions": {
-        "Unboxing and inventory checklist": None,
-        "Step-by-step installation guide": None,
-        "Required tools and materials": None,
-        "Additional Details": None
-    },
-    "Setup and Configuration": {
-        "Initial setup procedures": None,
-        "Configuration settings": None,
-        "Troubleshooting setup issues": None,
-        "Additional Details": None
-    },
-    "Operation Instructions": {
-        "How to use the product": None,
-        "Detailed instructions for different functionalities": None,
-        "User interface guide": None,
-        "Additional Details": None
-    },
-    "Maintenance and Care": {
-        "Cleaning instructions": None,
-        "Maintenance schedule": None,
-        "Replacement parts and accessories": None,
-        "Additional Details": None
-    },
-    "Troubleshooting": {
-        "Common issues and solutions": None,
-        "Error messages and their meanings": None,
-        "Support Information": None,
-        "Additional Details": None
-    },
-    "Warranty Information": {
-        "Terms and Conditions": None,
-        "Service and repair information": None,
-        "Additional Details": None
-    },
-    "Legal Information": {
-        "Copyright information": None,
-        "Trademarks and patents": None,
-        "Disclaimers": None,
-        "Additional Details": None
-
-    }
-}
-
-    for key,val in history.items():
-
-        find = details[key]
-
-        details[key] = str(detailed_feature_extraction(find,val))
-
-    return details
-
-
-def get_embeddings(link,tag_option): 
-
-        print(f"\n--> Creating Embeddings - {link}")
-
-        if tag_option=='Complete Document Similarity':
-            history = { "Details": "" }
-
-        else:
-            history = {
-                    "Introduction": "",
-                    "Specifications": "",
-                    "Product Overview": "",
-                    "Safety Information": "",
-                    "Installation Instructions": "",
-                    "Setup and Configuration": "",
-                    "Operation Instructions": "",
-                    "Maintenance and Care": "",
-                    "Troubleshooting": "",
-                    "Warranty Information": "",
-                    "Legal Information": ""
-                }
-
-        # Extract Text -----------------------------
-        print("Extracting Text")
-        if link[-3:] == '.md' or link[8:11] == 'en.':
-            text = web_extractor(link)
-        else:
-            text = pdf_extractor(link)
-
-        # Create Chunks ----------------------------
-        print("Writing Tag Data")
-
-        if tag_option=="Complete Document Similarity":
-            history["Details"] = feature_extraction("Details", history["Details"], text[0][:50000])
-            
-        else:
-            chunks = text_splitter.create_documents(text)
-
-            for chunk in chunks:
-
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                        future_to_key = {
-                            executor.submit(
-                                feature_extraction, f"Product {key}", history[key], chunk.page_content
-                            ): key for key in history
-                        }
-                        for future in concurrent.futures.as_completed(future_to_key):
-                            key = future_to_key[future]
-                            try:
-                                response = future.result()
-                                history[key] = response
-                            except Exception as e:
-                                print(f"Error processing {key}: {e}")
-            
-        print("Creating Vectors")
-        genai_embeddings=[]
-            
-        for tag in history:
-            result = genai.embed_content(
-                    model="models/embedding-001",
-                    content=history[tag],
-                    task_type="retrieval_document")
-            genai_embeddings.append(result['embedding'])
-
-
-        return history,genai_embeddings
-
-def get_embed_chroma(link):
-
-    print(f"\n--> Creating Embeddings - {link}")
-
-    # Extract Text -----------------------------
-    if link[-3:] == '.md' or link[8:11] == 'en.':
-        text = web_extractor(link)
-    else:
-        text = pdf_extractor(link)
-    print("\u2713 Extracting Text")
-
-    # Create Chunks ----------------------------
-
-    text = re.sub(r'\.{2,}', '.', text)
-    text = re.sub(r'\s{2,}', ' ', text)
-    text = [re.sub(r'\n{2,}', '\n', text)]
-
-    chunks = text_splitter_small.create_documents(text)
-    print("\u2713 Writing Tag Data")
-
-    # Creating Vector
-    embedding_vectors=[]
-    textual_data = []
-    print("\u2713 Creating Vectors")
-
-
-    for text in chunks:
-        
-        inputs = tokenizer(text.page_content, return_tensors="pt", padding=True, truncation=True)
-        inputs = {k: v.to('cpu') for k, v in inputs.items()}
-        
-        # Get the model's outputs
-        with torch.no_grad():
-            outputs = model(**inputs)
-
-        embeddings = outputs.last_hidden_state.mean(dim=1)
-        embedding_vectors.append(embeddings.squeeze().cpu().numpy().tolist())
-        textual_data.append(text.page_content)
-
-    return textual_data , embedding_vectors
-
-
-
-def get_image_embeddings(Product):
-    image_embeddings = []
-    
-    links = search_images(Product)
-    with concurrent.futures.ThreadPoolExecutor() as executor:
-        descriptions = list(executor.map(feature_extraction_image, links))
-    
-    for description in descriptions:
-        result = genai.embed_content(
-                model="models/embedding-001",
-                content=description,
-                task_type="retrieval_document")
-        
-        image_embeddings.append(result['embedding'])
-    # print(image_embeddings)
-    return image_embeddings
-
-
-            
-global text_splitter
-global data
-global history
-
-
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = 10000,
-    chunk_overlap  = 100,
-    separators = ["",''," "]
-)
-
-text_splitter_small = RecursiveCharacterTextSplitter(
-    chunk_size = 2000,
-    chunk_overlap  = 100,
-    separators = ["",''," "]
-)
-
-if __name__ == '__main__':
-    print(get_embed_chroma('https://www.galaxys24manual.com/wp-content/uploads/pdf/galaxy-s24-manual-SAM-S921-S926-S928-OS14-011824-FINAL-US-English.pdf'))
-    # print(get_image_embeddings(Product='Samsung Galaxy S24'))