Create app.py

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_community.vectorstores.faiss import FAISS
+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
+st.set_page_config(layout="centered")
+st.markdown("<h1 style='font-size:24px;'>PDF ChatBot by Arooj & Ali</h1>", unsafe_allow_html=True)
+
+# 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("Your PDF file here", type=["pdf"])
+
+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:
+"""
+
+# Additional prompts to enhance the template
+prompt_template = prompt_template + """
+--------------------------------------------------
+Prompt Suggestions:
+1. Summarize the primary theme of the context.
+2. Elaborate on the crucial concepts highlighted in the context.
+3. Pinpoint any supporting details or examples pertinent to the question.
+4. Examine any recurring themes or patterns relevant to the question within the context.
+5. Contrast differing viewpoints or elements mentioned in the context.
+6. Explore the potential implications or outcomes of the information provided.
+7. Assess the trustworthiness and validity of the information given.
+8. Propose recommendations or advice based on the presented information.
+9. Forecast likely future events or results stemming from the context.
+10. Expand on the context or background information pertinent to the question.
+11. Define any specialized terms or technical language used within the context.
+12. Analyze any visual representations like charts or graphs in the context.
+13. Highlight any restrictions or important considerations when responding to the question.
+14. Examine any presuppositions or biases evident within the context.
+15. Present alternate interpretations or viewpoints regarding the information provided.
+16. Reflect on any moral or ethical issues raised by the context.
+17. Investigate any cause-and-effect relationships identified in the context.
+18. Uncover any questions or areas requiring further exploration.
+19. Resolve any vague or conflicting information in the context.
+20. Cite case studies or examples that demonstrate the concepts discussed in the context.
+"""
+
+# Return the enhanced prompt template
+prompt_template = prompt_template + """
+--------------------------------------------------
+Context:\n{context}\n
+Question:\n{question}\n
+Answer:
+"""
+
+if uploaded_file is not None:
+    st.text("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
+
+    context = "\n\n".join(page.extract_text() for page in pdf_pages)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=200)
+    texts = text_splitter.split_text(context)
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    # vector_index = Chroma.from_texts(texts, embeddings).as_retriever()
+    vector_index = FAISS.from_texts(texts, embeddings).as_retriever()
+
+    user_question = st.text_input("Ask Anything from PDF:", "")
+
+
+
+    if st.button("Get Answer"):
+        if user_question:
+            with st.spinner("Processing..."):
+                # Get Relevant Documents
+                docs = vector_index.get_relevant_documents(user_question)
+                prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+                model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, api_key=google_api_key)
+                chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+                response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+                st.subheader("Answer:")
+                st.write(response['output_text'])
+
+        else:
+            st.warning("Please Ask.")