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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 with double quotes | |
| os.environ['TOGETHER_API_KEY'] = "5653bbfbaf1f7c1438206f18e5dfc2f5992b8f0b6aa9796b0131ea454648ccde" | |
| text = "" | |
| max_pages = 16 | |
| with pdfplumber.open("Ad_Mod_Daily_Sales.pdf") as pdf: | |
| for i, page in enumerate(pdf.pages): | |
| if i >= max_pages: | |
| break | |
| text += page.extract_text() + "\n" | |
| def Bot(Questions): | |
| 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/Llama-3-70b-chat-hf", max_tokens=250) | |
| Generated_chat = LLMChain(llm=llama3, prompt=prompt) | |
| try: | |
| response = Generated_chat.invoke({ | |
| "text": text, | |
| "Questions": Questions | |
| }) | |
| 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(Questions): | |
| greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"] | |
| # Check if the input question is a greeting | |
| question_lower = Questions.lower().strip() | |
| if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings): | |
| return "Hello! How can I assist you with the document today?" | |
| else: | |
| response=Bot(Questions) | |
| return response.translate(str.maketrans('', '', '\n')) | |
| # text_embedding = model.encode(text, convert_to_tensor=True) | |
| # statement_embedding = model.encode(statement, convert_to_tensor=True) | |
| # # Compute the cosine similarity between the embeddings | |
| # similarity = util.pytorch_cos_sim(text_embedding, statement_embedding) | |
| # # Print the similarity score | |
| # print(f"Cosine similarity: {similarity.item()}") | |
| # # Define a threshold for considering the statement as related | |
| # threshold = 0.7 | |
| # if similarity.item() > threshold: | |
| # response=Bot(Questions) | |
| # return response | |
| # else: | |
| # response="The statement is not related to the text." | |
| # return response | |
| iface = gr.Interface(fn=ChatBot, inputs="text", outputs="text", title="Chatbot") | |
| iface.launch(debug=True) | |