Update app.py

app.py

@@ -1,27 +1,12 @@
 import chromadb
 from chromadb.utils import embedding_functions
+from chromadb.config import Settings
 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
-
 
-# import chromadb
-from chromadb.config import Settings
 
 # Configure ChromaDB with persistent SQLite database
 config = Settings(
@@ -29,9 +14,6 @@ config = Settings(
     chroma_db_impl="sqlite",
 )
 
-
-import chromadb
-
 # Initialize persistent client with SQLite
 def setup_chromadb():
     client = chromadb.PersistentClient(path="./chromadb_data")
@@ -43,36 +25,6 @@ def setup_chromadb():
     )
     return client, collection
 
-
-# Initialize ChromaDB client
-# def setup_chromadb():
-#     try:
-#         client = chromadb.Client(config)
-#         collections = client.list_collections()
-#         print(f"Existing collections: {collections}")
-#         if "pdf_data" in [c.name for c in collections]:
-#             client.delete_collection("pdf_data")
-#             print("Existing collection 'pdf_data' deleted.")
-#         collection = client.create_collection(
-#             "pdf_data",
-#             embedding_function=chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction(
-#                 model_name="sentence-transformers/all-MiniLM-L6-v2"
-#             ),
-#         )
-#         return client, collection
-#     except Exception as e:
-#         print("Error setting up ChromaDB:", e)
-#         raise e
-
-
-# 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
-
 def extract_text_from_pdf(uploaded_file):
     with fitz.open(stream=uploaded_file.read(), filetype="pdf") as doc:
         text = ""
@@ -80,7 +32,6 @@ def extract_text_from_pdf(uploaded_file):
             text += page.get_text()
         return 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):
@@ -91,7 +42,6 @@ def add_pdf_text_to_db(collection, pdf_text):
                 metadatas={"line_number": idx, "text": sentence}
             )
 
-# Step 4: Query Function
 def query_pdf_data(collection, query, retriever_model):
     results = collection.query(
         query_texts=[query],
@@ -134,73 +84,5 @@ def main():
             st.error(f"Error extracting text: {e}")
 
 
-            
-    # 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 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()