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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'] = "d88cb7414e4039a84d2ed63f1b47daaaa4230c4c53a422045d8a30a9a3bc87d8"
text = ""
max_pages = 16
with pdfplumber.open("New Data Set.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=50)
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)
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