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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import os | |
| from langchain import PromptTemplate | |
| from langchain import LLMChain | |
| from langchain_together import Together | |
| import re | |
| import pdfplumber | |
| # Set the API key | |
| os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5" | |
| def extract_text_from_pdf(pdf_file, max_pages=16): | |
| text = "" | |
| with pdfplumber.open(pdf_file) as pdf: | |
| for i, page in enumerate(pdf.pages): | |
| if i >= max_pages: | |
| break | |
| text += page.extract_text() + "\n" | |
| return text | |
| def Bot(text, question): | |
| chat_template = """ | |
| Based on the provided context: {text} | |
| Please answer the following question: {Questions} | |
| Only provide answers that are directly related to the context. If the question is unrelated, respond with "I don't know". | |
| """ | |
| prompt = PromptTemplate( | |
| input_variables=['text', 'Questions'], | |
| template=chat_template | |
| ) | |
| llama3 = Together(model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", max_tokens=50) | |
| Generated_chat = LLMChain(llm=llama3, prompt=prompt) | |
| try: | |
| response = Generated_chat.invoke({ | |
| "text": text, | |
| "Questions": question | |
| }) | |
| response_text = response['text'] | |
| response_text = response_text.replace("assistant", "") | |
| # Post-processing to handle repeated words and ensure completeness | |
| words = response_text.split() | |
| seen = set() | |
| filtered_words = [word for word in words if word.lower() not in seen and not seen.add(word.lower())] | |
| response_text = ' '.join(filtered_words) | |
| response_text = response_text.strip() # Ensuring no extra spaces at the ends | |
| if not response_text.endswith('.'): | |
| response_text += '.' | |
| return response_text | |
| except Exception as e: | |
| return f"Error in generating response: {e}" | |
| def ChatBot(history, document, question): | |
| greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"] | |
| question_lower = question.lower().strip() | |
| if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings): | |
| return history + [("User", question), ("Bot", "Hello! How can I assist you with the document today?")] | |
| # Extract text from the uploaded PDF document | |
| text = extract_text_from_pdf(document) | |
| # Generate the bot response based on the question and extracted text | |
| response = Bot(text, question) | |
| # Update chat history with the user's question and bot's response | |
| history.append(("User", question)) | |
| history.append(("Bot", response)) | |
| return history | |
| # Set up the Gradio interface using Blocks | |
| with gr.Blocks() as iface: | |
| chatbot = gr.Chatbot() | |
| document = gr.File(label="Upload PDF Document", type="filepath") | |
| question = gr.Textbox(label="Ask a Question", placeholder="Type your question here...") | |
| def respond(history, document, question): | |
| return ChatBot(history, document, question) | |
| question.submit(respond, [chatbot, document, question], chatbot) | |
| iface.launch(debug=True) | |