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import gradio as gr
import os
from langchain import PromptTemplate, LLMChain
from langchain_together import Together
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)
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