Delete app.py

app.py

@@ -1,65 +0,0 @@
-import os
-import streamlit as st
-import fitz  # PyMuPDF
-import openai
-from dotenv import load_dotenv
-from pinecone import Pinecone, ServerlessSpec
-
-# Load the environment variables from the .env file
-load_dotenv()
-openai_api_key = os.getenv('OPENAI_API_KEY')
-pinecone_api_key = os.getenv('PINECONE_API_KEY')
-pinecone_environment = os.getenv('PINECONE_ENVIRONMENT')
-
-# Initialize Pinecone
-pc = Pinecone(api_key=pinecone_api_key)
-
-# Streamlit app
-st.title("Chat with Your Document")
-st.write("Upload a PDF file to chat with its content using Pinecone and OpenAI.")
-
-# File upload
-uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
-
-if uploaded_file is not None:
-    # Load the PDF file
-    pdf_document = fitz.open(stream=uploaded_file.read(), filetype="pdf")
-    pdf_text = ""
-    for page_num in range(pdf_document.page_count):
-        page = pdf_document.load_page(page_num)
-        pdf_text += page.get_text()
-
-    # Initialize OpenAI embeddings
-    openai.api_key = openai_api_key
-
-    # Create a Pinecone vector store
-    index_name = "pdf-analysis"
-    if index_name not in pc.list_indexes().names():
-        pc.create_index(
-            name=index_name,
-            dimension=512,
-            metric='euclidean',
-            spec=ServerlessSpec(cloud='aws', region=pinecone_environment)
-        )
-    vector_store = pc.Index(index_name)
-
-    # Add the PDF text to the vector store
-    vector_store.upsert([(str(i), openai.Embedding.create(input=pdf_text)["data"][0]["embedding"]) for i in range(len(pdf_text))])
-
-    # Chat with the document
-    user_input = st.text_input("Ask a question about the document:")
-    if st.button("Ask"):
-        if user_input:
-            response = openai.Completion.create(
-                engine="davinci",
-                prompt=f"Analyze the following text and answer the question: {pdf_text}\n\nQuestion: {user_input}",
-                max_tokens=150
-            )
-            st.write(response.choices[0].text.strip())
-        else:
-            st.write("Please enter a question to ask.")
-
-    # Display the PDF text
-    st.write("Extracted Text from PDF:")
-    st.write(pdf_text)
-    #