Create app.py

app.py

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+import os
+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Load the GROQ API KEY
+os.environ['GROQ_API_KEY'] = GROQ_API_KEY
+
+llm = ChatGroq(temperature=0, model_name='llama-3.1-8b-instant', groq_api_key=GROQ_API_KEY)
+
+prompt = ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the provided context only.
+Please provide the most accurate response based on the question
+<context>
+{context}
+</context>
+Question: {input}
+"""
+)
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vectors = None
+
+def process_pdf(file):
+    global vectors
+    if file is not None:
+        loader = PyPDFLoader(file.name)
+        docs = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        final_documents = text_splitter.split_documents(docs)
+        if vectors is None:
+            vectors = FAISS.from_documents(final_documents, embeddings)
+        else:
+            vectors.add_documents(final_documents)
+        return "PDF processed and added to the knowledge base."
+    return "No file uploaded."
+
+def process_question(question):
+    if vectors is None:
+        return "Please upload a PDF first.", "", 0
+
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    response = retrieval_chain.invoke({'input': question})
+    
+    context = "\n\n".join([doc.page_content for doc in response["context"]])
+    
+    # Calculate a simple confidence score based on the relevance of retrieved documents
+    confidence_score = sum([doc.metadata.get('score', 0) for doc in response["context"]]) / len(response["context"])
+    
+    return response['answer'], context, round(confidence_score, 2)
+
+CSS = """
+.duplicate-button { 
+    margin: auto !important; 
+    color: white !important; 
+    background: black !important; 
+    border-radius: 100vh !important;
+}
+h3, p, h1 { 
+    text-align: center; 
+    color: white;
+}
+footer { 
+    text-align: center; 
+    padding: 10px; 
+    width: 100%; 
+    background-color: rgba(240, 240, 240, 0.8); 
+    z-index: 1000; 
+    position: relative; 
+    margin-top: 10px; 
+    color: black;
+}
+"""
+
+FOOTER_TEXT = """
+<footer>
+    <p>If you enjoyed the functionality of the app, please leave a like!<br>
+    Check out more on <a href="https://www.linkedin.com/in/your-linkedin/" target="_blank">LinkedIn</a> | 
+    <a href="https://your-portfolio-url.com/" target="_blank">Portfolio</a></p>
+</footer>
+"""
+
+TITLE = "<h1>📚 RAG Document Q&A 📚</h1>"
+
+with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
+    gr.HTML(TITLE)
+
+    with gr.Tab("PDF Uploader"):
+        pdf_file = gr.File(label="Upload PDF")
+        upload_button = gr.Button("Process PDF")
+        upload_output = gr.Textbox(label="Upload Status")
+
+    with gr.Tab("Q&A System"):
+        question_input = gr.Textbox(lines=2, placeholder="Enter your question here...")
+        submit_button = gr.Button("Ask Question")
+        answer_output = gr.Textbox(label="Answer")
+        context_output = gr.Textbox(label="Relevant Context", lines=10)
+        confidence_output = gr.Number(label="Confidence Score")
+
+    upload_button.click(process_pdf, inputs=[pdf_file], outputs=[upload_output])
+    submit_button.click(process_question, inputs=[question_input], outputs=[answer_output, context_output, confidence_output])
+
+    gr.HTML(FOOTER_TEXT)
+
+if __name__ == "__main__":
+    demo.launch()