import gradio as gr import PyPDF2 import io import os import time from together import Together import textwrap import tempfile # 1st work is to extract the pdf file def extract_pdf(pdf_file): text= "" try: if hasattr(pdf_file, "read"): #convert pf into bytes format (data byte bit type) pdf_content= pdf_file.read() if hasattr(pdf_file,"seek"): pdf_file.seek(0) else: #if it already in bites and read pdf_content= pdf_file pdf_reader= PyPDF2.PdfReader(io.BytesIO(pdf_content)) #START reading and need to extract the pdf for page_num in range(len(pdf_reader.pages)): page_text= pdf_reader.pages[page_num].extract_text() if page_text: # check the extract is working or not print("okay") text= text + page_text + "\n" else: print("no extractable text is found there") text += f"[Page{page_num+1}]" if not text.strip(): return "no text extracted from PDF it a scanned file or image" return text except Exception as e: return f"Error when extracting text from pdf : {str(e)}" ## create a function to chat with the pdf def chat_with_pdf(api_key, pdf_text, user_question, history): if not api_key.strip(): return history + [(user_question,"Error: plase enter your own api key")], history if not pdf_text.strip() or pdf_text.startswith("Error") or pdf_text.startswith("No text"): return history + [(user_question,"Error: plase upload a valid pdf file with extractable text format")], history if not user_question.strip(): return history + [(user_question,"Error: please enter a valid question")], history try: # model connections all for same client= Together(api_key=api_key) # server e giye hi dibe and server response korbe vhul hole server response korbe na max_content_len= 150000 # check for the number of word if len(pdf_text) > max_content_len: half_len = max_content_len//2 pdf_context= pdf_text[: half_len]+ "\n\n[....Content truncated due to length ....]\n\n" + pdf_text[-half_len:] else: pdf_content= pdf_text system_message= f"""You are an intelligent assistant designed to read, understand, and extract information from PDF documents. Based on any question or query the user asks—whether it's about content, summaries, data extraction, definitions, insights, or interpretation—you will analyze the following PDF content and provide an accurate, helpful response grounded in the document. Always respond with clear, concise, and context-aware information. PDF CONTENT: {pdf_context} Answer the user's questions only based on the PDF content above. If the answer cannot be found in the PDF, politely state that the information is not available in the provided document.""" # Prepare message history for Together API messages = [ {"role": "system", "content": system_message}, ] # Add chat history for h_user, h_bot in history: messages.append({"role": "user", "content": h_user}) messages.append({"role": "assistant", "content": h_bot}) # Add the current user question messages.append({"role": "user", "content": user_question}) #connect the model with the app reponse = client.chat.completions.create( model= "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", messages= messages, max_tokens= 5000, #5000 temperature= .7, ) assistance_reponse = response.choices[0].message.content # update the chat history new_history= history + [(user_question, assistance_reponse)] #history update purono conversation + current conver= new history return new_history, new_history except Exception as e: error_message= f"Error:{str(e)}" return history + [(user_question, error_message)], history # now process the pdf before the chat def process_pdf(pdf_file, api_key_input): if pdf_file is None: return "please upload a PDF file.","",[] try: file_name= os.path.basename(pdf_file.name) if hasattr(pdf_file,"name") else "please Upload" pdf_text= extract_pdf(pdf_file) if pdf_text.startswith("Error extracting text from PDF"): return f"❌ {pdf_text}", "", [] if not pdf_text.strip() or pdf_text.startswith("No text could be extracted"): return f"⚠️ {pdf_text}", "", [] word_count= len(pdf_text.split()) # Return a message with the file name and text content status_message = f"✅ Successfully processed PDF: {file_name} ({word_count} words extracted)" return status_message, pdf_text,[] except Exception as e: return f"❌ Error processing PDF: {str(e)}", "", [] # def validate_api_key(api_key): """Simple validation for API key format""" if not api_key or not api_key.strip(): return "❌ API Key is required" if len(api_key.strip()) < 10: return "❌ API Key appears to be too short" return "✓ API Key format looks valid (not verified with server)" with gr.Blocks(title="chatsPDFsupport", theme=gr.themes.Ocean()) as app: gr.Markdown("# 📄 ChatPDF with Together AI") gr.Markdown("Upload a PDF and chat with it using the Llama-3.3-70B model.") with gr.Row(): with gr.Column(scale=1) api_key_input = gr.Textbox( label="Together API Key", placeholder="Enter your Together API key here...", type="password") # API key validation api_key_status = gr.Textbox( label="API Key Status", interactive=False ) # PDF upload pdf_file = gr.File( label="Upload PDF", file_types=[".pdf"], type="binary" # Ensure we get binary data ) # Process PDF button process_button = gr.Button("Process PDF") # Status message status_message = gr.Textbox( label="Status", interactive=False ) # Hidden field to store the PDF text pdf_text = gr.Textbox(visible=False) # Optional: Show PDF preview with gr.Accordion("PDF Content Preview", open=False): pdf_preview = gr.Textbox( label="Extracted Text Preview", interactive=False, max_lines=10, show_copy_button=True ) with gr.Column(scale=2): #right side column row # Chat interface chatbot = gr.Chatbot( label="Chat with PDF", height=500, show_copy_button=True ) # Question input question = gr.Textbox( label="Ask a question about the PDF", placeholder="What is the main topic of this document?", lines=2 ) #submit button sumbit_button= gr.Button("Submit Question") # Event handlers def update_preview(text): """Update the preview with the first few lines of the PDF text""" if not text or text.startswith("Error") or text.startswith("No text"): return text # Get the first ~500 characters for preview preview = text[:500] if len(text) > 500: preview += "...\n[Text truncated for preview. Full text will be used for chat.]" return preview # API key validation event api_key_input.change( fn=validate_api_key, inputs=[api_key_input], outputs=[api_key_status] ) process_button.click( fn=process_pdf, inputs=[pdf_file, api_key_input], outputs=[status_message, pdf_text, chatbot] ).then( fn=update_preview, inputs=[pdf_text], outputs=[pdf_preview] ) submit_button.click( fn=chat_with_pdf, inputs=[api_key_input, pdf_text, question, chatbot], outputs=[chatbot, chatbot] ).then( fn=lambda: "", outputs=question ) question.submit( fn=chat_with_pdf, inputs=[api_key_input, pdf_text, question, chatbot], outputs=[chatbot, chatbot] ).then( fn=lambda: "", outputs=question ) # Launch the app if __name__ == "__main__": app.launch(share=True)