Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores.faiss import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain import OpenAI, VectorDBQA
+
+import os
+os.environ["OPENAI_API_KEY"] = openai_api_key
+
+
+def pdf_to_text(pdf_file, query):
+  # Open the PDF file in binary mode
+  with open(pdf_file.name, 'rb') as pdf_file:
+      # Create a PDF reader object
+      pdf_reader = PyPDF2.PdfReader(pdf_file)
+
+      # Create an empty string to store the text
+      text = ""
+
+      # Loop through each page of the PDF
+      for page_num in range(len(pdf_reader.pages)):
+          # Get the page object
+          page = pdf_reader.pages[page_num]
+          # Extract the texst from the page and add it to the text variable
+          text += page.extract_text()
+    #embedding step 
+  text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+  texts = text_splitter.split_text(text)
+
+  embeddings = OpenAIEmbeddings()
+  #vector store
+  vectorstore = FAISS.from_texts(texts, embeddings)
+
+    #inference
+  qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vectorstore)
+  return qa.run(query)
+
+
+      
+
+
+# Define the Gradio interface
+pdf_input = gr.inputs.File(label="PDF File")
+query_input = gr.inputs.Textbox(label="Query")
+outputs = gr.outputs.Textbox(label="Chatbot Response")
+interface = gr.Interface(fn=pdf_to_text, inputs=[pdf_input, query_input], outputs=outputs)
+
+# Run the interface
+interface.launch()