First Release

.env

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+GOOGLE_API_KEY=AIzaSyDz-X-hSJYnnllhZ6odQM03J7vSO6pdMYA

app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+import PyPDF2
+import os
+import io
+
+st.title("Chat Your PDFs")  # Updated title
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Retrieve API key from environment variable
+google_api_key = os.getenv("GOOGLE_API_KEY")
+
+# Check if the API key is available
+if google_api_key is None:
+    st.warning("API key not found. Please set the google_api_key environment variable.")
+    st.stop()
+
+# File Upload with user-defined name
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+if uploaded_file is not None:
+    st.text("PDF File Uploaded Successfully!")
+
+    # PDF Processing (using PyPDF2 directly)
+    pdf_data = uploaded_file.read()
+    pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_data))
+    pdf_pages = pdf_reader.pages
+
+    # Create Context
+    context = "\n\n".join(page.extract_text() for page in pdf_pages)
+
+    # Split Texts
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=200)
+    texts = text_splitter.split_text(context)
+
+    # Chroma Embeddings
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_index = Chroma.from_texts(texts, embeddings).as_retriever()
+
+    # Get User Question
+    user_question = st.text_input("Ask a Question:")
+
+    if st.button("Get Answer"):
+        if user_question:
+            # Get Relevant Documents
+            docs = vector_index.get_relevant_documents(user_question)
+
+            # Define Prompt Template
+            prompt_template = """
+            Answer the question as detailed as possible from the provided context,
+            make sure to provide all the details, if the answer is not in
+            provided context just say, "answer is not available in the context",
+            don't provide the wrong answer\n\n
+            Context:\n {context}?\n
+            Question: \n{question}\n
+            Answer:
+            """
+
+            # Create Prompt
+            prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+
+            # Load QA Chain
+            model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, api_key=google_api_key)
+            chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+            # Get Response
+            response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+
+            # Display Answer
+            st.subheader("Answer:")
+            st.write(response['output_text'])
+
+        else:
+            st.warning("Please enter a question.")

requirements.txt

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+langchain
+google-generativeai
+streamlit
+langchain-google-genai
+python-dotenv
+chromadb
+pypdf