# import chromadb
# from chromadb.utils import embedding_functions
# from sentence_transformers import SentenceTransformer
# from transformers import pipeline
# import streamlit as st
# import fitz  # PyMuPDF for PDF parsing

# # Step 1: Setup ChromaDB
# def setup_chromadb():
#     # Initialize ChromaDB in-memory instance
#     client = chromadb.Client()
#     try:
#         client.delete_collection("pdf_data")
#         print("Existing collection 'pdf_data' deleted.")
#     except:
#         print("Collection 'pdf_data' not found, creating a new one.")
#     # Create a new collection with the embedding function
#     ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="sentence-transformers/all-MiniLM-L6-v2")
#     collection = client.create_collection("pdf_data", embedding_function=ef)
#     return client, collection

# # Step 2: Extract Text from PDF
# def extract_text_from_pdf(pdf_path):
#     pdf_text = ""
#     with fitz.open(pdf_path) as doc:
#         for page in doc:
#             pdf_text += page.get_text()
#     return pdf_text

# # Step 3: Add Extracted Text to Vector Database
# def add_pdf_text_to_db(collection, pdf_text):
#     sentences = pdf_text.split("\n")  # Split text into lines for granularity
#     for idx, sentence in enumerate(sentences):
#         if sentence.strip():  # Avoid empty lines
#             collection.add(
#                 ids=[f"pdf_text_{idx}"],
#                 documents=[sentence],
#                 metadatas={"line_number": idx, "text": sentence}
#             )

# # Step 4: Query Function
# def query_pdf_data(collection, query, retriever_model):
#     results = collection.query(
#         query_texts=[query],
#         n_results=3
#     )
#     context = " ".join([doc for doc in results["documents"][0]])
#     answer = retriever_model(f"Context: {context}\nQuestion: {query}")
#     return answer, results["metadatas"]

# # Streamlit Interface
# def main():
#     st.title("PDF Chatbot with Retrieval-Augmented Generation")
#     st.write("Upload a PDF, and ask questions about its content!")

#     # Initialize components
#     client, collection = setup_chromadb()
#     retriever_model = pipeline("text2text-generation", model="google/flan-t5-small")  # Free LLM

#     # File upload
#     uploaded_file = st.file_uploader("Upload your PDF file", type="pdf")
#     if uploaded_file:
#         st.write("Extracting text and populating the database...")
#         pdf_text = extract_text_from_pdf(uploaded_file)
#         add_pdf_text_to_db(collection, pdf_text)
#         st.success("PDF text has been added to the database. You can now query it!")

#         # Query Input
#         query = st.text_input("Enter your query about the PDF:")
#         if query:
#             try:
#                 answer, metadata = query_pdf_data(collection, query, retriever_model)
#                 st.subheader("Answer:")
#                 st.write(answer[0]['generated_text'])
#                 st.subheader("Retrieved Context:")
#                 for meta in metadata[0]:
#                     st.write(meta)
#             except Exception as e:
#                 st.error(f"An error occurred: {str(e)}")

# if __name__ == "__main__":
#     main()



import streamlit as st
from streamlit_chromadb_connection.chromadb_connection import ChromadbConnection

configuration = {
    "client": "PersistentClient",
    "path": "/tmp/.chroma"
}

collection_name = "documents_collection"

conn = st.connection("chromadb",
                     type=ChromaDBConnection,
                     **configuration)
documents_collection_df = conn.get_collection_data(collection_name)
st.dataframe(documents_collection_df)







# import tempfile
# import PyPDF2
# import streamlit as st
# from transformers import GPT2LMHeadModel, GPT2Tokenizer

# # Load pre-trained GPT-3 model and tokenizer
# tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
# model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")

        
# def extract_text_from_pdf(file_path):
#     text = ""
#     with open(file_path, "rb") as f:
#         reader = PyPDF2.PdfFileReader(f)
#         for page_num in range(reader.numPages):
#             text += reader.getPage(page_num).extractText()
#     return text

# def generate_response(user_input):
#     input_ids = tokenizer.encode(user_input, return_tensors="pt")
#     output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.7)
#     response = tokenizer.decode(output[0], skip_special_tokens=True)
#     return response

# def main():
#     st.title("PDF Chatbot")

#     pdf_file = st.file_uploader("Upload an pdf file", type=["pdf"], accept_multiple_files=False)

#     if pdf_file is not None:
#         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
#             tmp_file.write(pdf_file.read())
#             st.success("PDF file successfully uploaded and stored temporally.")
#         file_path = tmp_file.name
#         pdf_text = extract_text_from_pdf(file_path)
#         st.text_area("PDF Content", pdf_text)
#     else:
#         st.markdown('File not found!')        

#     user_input = st.text_input("You:", "")
#     if st.button("Send"):
#         response = generate_response(user_input)
#         st.text_area("Chatbot:", response)

# if __name__ == "__main__":
#     main()