Update app.py

app.py

@@ -42,10 +42,6 @@ def text_splitter(text):
 
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 
-def embedding(chunk):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    db = Chroma.from_documents(chunk,embeddings, persist_directory="./chroma_db")
-
 def get_conversational_chain():
     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
@@ -60,31 +56,33 @@ def get_conversational_chain():
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
     return chain
 
-def user_call(query):
+def embedding(chunk,query):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    db3 = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+    db = Chroma.from_documents(chunk,embeddings, persist_directory="./chroma_db")
     docs = db3.similarity_search(query)
     chain = get_conversational_chain()
     response = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
     st.write("Reply: ", response["output_text"])
 
+
+    
+
 def main():
     st.header("Chat with your pdf💁")
-
+    st.title("Menu:")
+    pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, key="pdf_uploader")
     query = st.text_input("Ask a Question from the PDF Files", key="query")
+    if st.button("Submit & Process", key="process_button"):
+        with st.spinner("Processing..."):
+            raw_text = get_pdf(pdf_docs)
+            text_chunks = text_splitter(raw_text)
+            embedding(text_chunks)
+    
 
     if query: 
       user_call(query)
 
-
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, key="pdf_uploader")
-        if st.button("Submit & Process", key="process_button"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf(pdf_docs)
-                text_chunks = text_splitter(raw_text)
-                embedding(text_chunks)
+        
                 st.success("Done")
 
 if __name__ == "__main__":