Spaces:
Sleeping
Sleeping
| import os | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain_community.document_loaders import PyPDFLoader | |
| import google.generativeai as genai | |
| import gradio as gr | |
| # Function for initialization | |
| def initialize(pdf_file, question): | |
| try: | |
| # Access the uploaded file information from Gradio | |
| file_info = pdf_file | |
| # Check if a file was uploaded | |
| if file_info is not None: | |
| # Construct potential file path based on temporary directory and filename | |
| file_path = os.path.join("/tmp", file_info.name) # Adjust temporary directory if needed | |
| if os.path.exists(file_path): | |
| # Process the PDF | |
| pdf_loader = PyPDFLoader(file_path) | |
| pages = pdf_loader.load_and_split() | |
| context = "\n".join(str(page.page_content) for page in pages[:30]) # Limit to first 30 pages | |
| # Configure Google Generative AI (replace with your API key) | |
| model = genai.GenerativeModel('gemini-pro') | |
| model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3) | |
| stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) | |
| stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True) | |
| return stuff_answer['output_text'] | |
| else: | |
| return "Error: The uploaded file could not be found." | |
| else: | |
| return "Error: No PDF file was uploaded." | |
| except Exception as e: | |
| return f"An error occurred: {e}" # Generic error handling | |
| # Create a Gradio interface | |
| interface = gr.Interface( | |
| fn=initialize, | |
| inputs=[ | |
| gr.File(label="Upload PDF"), # No need for 'type' argument | |
| gr.Textbox(label="Question") | |
| ], | |
| outputs="text", | |
| title="GeminiPro Q&A Bot", | |
| description="Ask questions about the uploaded PDF document.", | |
| ) | |
| # Launch the interface | |
| interface.launch() | |