Delete app.py

app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-import PyPDF2
-import os
-from langchain.embeddings import CohereEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.llms.fireworks import Fireworks 
-from langchain.chains import VectorDBQA
-
-FIREWORKS_API_KEY = os.getenv("FIREWORKS_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 
-  llm = Fireworks(model="accounts/fireworks/models/llama-v2-13b-chat", model_kwargs={"temperature": 0, "max_tokens": 500, "top_p": 1.0})  
-  text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-  texts = text_splitter.split_text(text)
-
-  embeddings = CohereEmbeddings(cohere_api_key="Ev0v9wwQPa90xDucdHTyFsllXGVHXouakUMObkNb")
-  #vector store
-  vectorstore = FAISS.from_texts(texts, embeddings)
-
-  #inference
-  qa = VectorDBQA.from_chain_type(llm=llm, 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()