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| # Import necessary libraries | |
| from transformers import AutoModel, AutoTokenizer | |
| import streamlit as st | |
| import PyPDF2 | |
| import pytesseract | |
| from PIL import Image | |
| # Your Hugging Face access token | |
| access_token = "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" | |
| # Correct model name or path | |
| model_name = "bigcode/starcoderplus" | |
| # Load model and tokenizer | |
| model = AutoModel.from_pretrained(model_name, use_auth_token=access_token) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token) | |
| # Streamlit app | |
| st.title("Conversational AI Chatbot") | |
| # User input for code-related question | |
| user_input = st.text_input("Enter your code-related question:") | |
| if user_input: | |
| # Tokenize and generate code | |
| inputs = tokenizer(user_input, return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| code = tokenizer.decode(outputs[0]) | |
| st.code(code) # Display the generated code | |
| # User input for PDF reading | |
| pdf_file = st.file_uploader("Upload a PDF file:", type=["pdf"]) | |
| if pdf_file: | |
| pdf_reader = PyPDF2.PdfFileReader(pdf_file) | |
| pdf_text = "" | |
| for page_num in range(pdf_reader.numPages): | |
| page = pdf_reader.getPage(page_num) | |
| pdf_text += page.extractText() | |
| st.text(pdf_text) # Display the extracted text | |
| # User input for OCR (image to text) | |
| image_file = st.file_uploader("Upload an image for OCR:", type=["png", "jpg", "jpeg"]) | |
| if image_file: | |
| image = Image.open(image_file) | |
| ocr_text = pytesseract.image_to_string(image) | |
| st.text(ocr_text) # Display the extracted text | |