Update app.py

app.py

@@ -10,26 +10,21 @@ 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
+load_dotenv()  # Load the GROQ API KEY
 GROQ_API_KEY = os.environ.get("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.
+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}
-"""
-)
+<context>{context}</context>
+Question: {input}""")
 
 embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-vectors = None
+
+def initialize_vectors():
+    return None
+
+vectors = initialize_vectors()
 
 def process_pdf(file):
     global vectors
@@ -38,57 +33,32 @@ def process_pdf(file):
         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)
+        vectors = FAISS.from_documents(final_documents, embeddings)
         return "PDF processed and added to the knowledge base."
     return "No file uploaded."
 
 def process_question(question):
+    global vectors
     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;
-}
+.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>
+    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>
 """
 
@@ -96,22 +66,22 @@ 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__":