app.py

import os
import gradio as gr
from huggingface_hub import HfApi, whoami
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate


from langchain.prompts import PromptTemplate



openai_api_key = os.environ.get("OPENAI_API_KEY")
hf_api = HfApi()
class AdvancedPdfChatbot:
    def __init__(self, openai_api_key):
        os.environ["OPENAI_API_KEY"] = openai_api_key
        self.embeddings = OpenAIEmbeddings()
        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        self.llm =  ChatOpenAI(temperature=0.5,model_name='gpt-4o',max_tokens=3000)
        
        self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        self.qa_chain = None
        self.pdf_path = None
        self.template = """
You are a file-based knowledge assistant that interacts with users like ChatGPT. Your primary source of knowledge comes from user-uploaded files, such as PDFs. You do not rely on general knowledge or the internet. Instead, you extract, analyze, and synthesize information directly from the content of the provided file(s).  
**1. Personality and Tone**  
- Be polite, clear, and professional.  
- Use formal, academic language when the context requires it.  
- Provide concise, well-structured responses, and maintain a helpful and supportive tone.  
**2. Core Capabilities**  
- Extract and summarize key information from the provided file.  
- Answer user questions based on the content of the file.  
- Provide in-depth analysis, explanations, and references to the file's content.  
- Suggest relevant sections, chapters, or pages where specific information can be found.  
- Offer guidance on how users can interpret and understand the file's contents.  
**3. Knowledge and Scope**  
- Your knowledge is limited to the content found in the uploaded file(s).  
- You should not answer questions unrelated to the file's content unless explicitly requested.  
- If a user asks a question that is not found in the file, inform them that the information is not available.  
**4. Interaction Rules**  
- Respond with specific references to the document's content, including page numbers, sections, or headings, if available.  
- If the user asks for clarification, politely request more details.  
- Provide short, clear explanations for user queries, but be ready to offer more depth if asked.  
- Never "make up" information. If something is not in the file, clearly state that it cannot be found.  
**5. Context Awareness**  
- Remember the content of the file for the duration of the session.  
- Use file-specific knowledge to provide logical and evidence-backed responses.  
- If multiple files are uploaded, clarify which file is being referenced and specify which file the information is from.  
**6. Technical Details**  
- Summarize content into concise answers and organize information using bullet points, lists, or structured paragraphs.  
- If asked to provide a summary, focus on key points, main arguments, and essential takeaways.  
- When a user asks for a section or heading, search for relevant text within the file.  
- Do not offer answers beyond the scope of the file, and avoid guessing.  
**7. Example Usage**  
User: "Can you summarize the main argument from the introduction of the file?"  
Response: "Sure! The introduction discusses [key points] and highlights the central argument that [main idea]. This can be found on page 2 under the heading 'Introduction'."  
User: "Where can I find the definition of 'symbolic interactionism' in the document?"  
Response: "The definition of 'symbolic interactionism' appears on page 12 under the subheading 'Key Theoretical Concepts'."  
User: "Explain the concept of 'cognitive dissonance' as it is presented in the document."  
Response: "In the document, 'cognitive dissonance' is defined as [definition from the file]. It appears in the context of [brief explanation] and can be found on page 15 under the section 'Theoretical Foundations'."  

NOTE : DESCRIBE/SUMMARY should always return the overall summary of the documents in well documented and descriptions of the topic in great details.
**End of Prompt**
        Context: {context}
        Question: {question}
        Answer:
        """
        
        self.prompt = PromptTemplate(template=self.template, input_variables=["context", "question"])

    def load_and_process_pdf(self, pdf_path):
        loader = PyPDFLoader(pdf_path)
        documents = loader.load()
        texts = self.text_splitter.split_documents(documents)
        self.db = FAISS.from_documents(texts, self.embeddings)
        self.pdf_path = pdf_path
        self.setup_conversation_chain()

    def setup_conversation_chain(self):
        self.qa_chain = ConversationalRetrievalChain.from_llm(
            self.llm,
            retriever=self.db.as_retriever(),
            memory=self.memory,
            combine_docs_chain_kwargs={"prompt": self.prompt}
        )

    def chat(self, query):
        if not self.qa_chain:
            return "Please upload a PDF first."
        result = self.qa_chain({"question": query})
        return result['answer']

    def get_pdf_path(self):
        # Return the stored PDF path
        if self.pdf_path:
            return self.pdf_path
        else:
            return "No PDF uploaded yet."

# Initialize the chatbot
pdf_chatbot = AdvancedPdfChatbot(openai_api_key)

def get_user_folder():
    try:
        user_info = whoami()
        username = user_info['name']
        user_folder = f"user_data/{username}"
        os.makedirs(user_folder, exist_ok=True)
        return user_folder
    except Exception:
        return None

def upload_pdf(pdf_file):
    if pdf_file is None:
        return "Please upload a PDF file."
    user_folder = get_user_folder()
    if user_folder is None:
        return "Please log in to upload a PDF."
    file_path = os.path.join(user_folder, pdf_file.name)
    with open(file_path, "wb") as f:
        f.write(pdf_file.read())
    pdf_chatbot.load_and_process_pdf(file_path)
    return file_path

def respond(message, history):
    bot_message = pdf_chatbot.chat(message)
    history.append((message, bot_message))
    return "", history

def clear_chatbot():
    pdf_chatbot.memory.clear()
    return []

def get_pdf_path():
    return pdf_chatbot.get_pdf_path()

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# PDF Chatbot")
    
    with gr.Row():
        login_button = gr.LoginButton()
        user_info = gr.Markdown()
    
    with gr.Row():
        pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
        upload_button = gr.Button("Process PDF")

    upload_status = gr.Textbox(label="Upload Status")
    upload_button.click(upload_pdf, inputs=[pdf_upload], outputs=[upload_status])
    path_button = gr.Button("Get PDF Path")
    pdf_path_display = gr.Textbox(label="Current PDF Path")
    chatbot_interface = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    msg.submit(respond, inputs=[msg, chatbot_interface], outputs=[msg, chatbot_interface])
    clear.click(clear_chatbot, outputs=[chatbot_interface])
    path_button.click(get_pdf_path, outputs=[pdf_path_display])

    demo.load(lambda: gr.update(visible=True), outputs=[user_info], inputs=None)

if __name__ == "__main__":
    demo.launch()