Initial commit

.gitignore

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+venv/

app.py

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+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+import configparser
+from typing_extensions import Concatenate
+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+
+from llama_index.llms import Gemini, HuggingFaceInferenceAPI,OpenAI
+from llama_index import VectorStoreIndex, download_loader
+from llama_index.embeddings import HuggingFaceEmbedding
+from llama_index import ServiceContext
+ from llama_index.schema import Document
+
+def read_pdf(uploaded_file):
+    pdf_reader = PdfReader(uploaded_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+      
+
+def querying(document_search, chain):
+    query_text = st.text_input("Enter the Query for PDF:")
+    submit = st.button("Generate The response for the query")
+
+    if submit:
+        docs = document_search.similarity_search(query_text)
+        output = chain.run(input_documents=docs, question=query_text)
+        st.write(output)
+            
+def main():
+    st.title("PdfQuerier using LLAMA by Rahul Bhoyar")
+    hf_token = st.text_input("Enter your Hugging Face token:")
+    
+    llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token) 
+    st.markdown("Query your pdf file data with using this chatbot")
+    uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
+    
+    embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+    service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
+
+    if uploaded_file is not None:
+        file_contents = read_pdf(uploaded_file)
+        documents = Document(text=file_contents)
+        
+        st.success("Documents loaded successfully!")
+
+        # Create Vector Store Index
+        index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+
+        # Persist Storage Context
+        index.storage_context.persist()
+
+        # Create Query Engine
+        query = st.text_input("Ask a question:")
+        query_engine = index.as_query_engine()
+        if query:
+            # Run Query
+            response = query_engine.query(query)
+
+            # Display Result
+            st.markdown(f"**Response:** {response}")
+    else:
+        st.warning("Please enter a valid pdf.")
+        
+                
+if __name__ == "__main__":
+    main()

requirements.txt

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+langchain
+openai
+PyPDF2
+faiss-cpu
+tiktoken
+watchdog
+streamlit
+fitz 
+llama-index
+transformers[torch]
+huggingface_hub[inference]
+beautifulsoup4
+unstructured
+watchdog
+transformers