Update app.py

app.py

@@ -1,158 +1,147 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.embeddings import OllamaEmbeddings
-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.document_loaders import PyPDFDirectoryLoader
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-# from langchain.vectorstores.cassandra import Cassandra
-from langchain_community.vectorstores import Cassandra
-from langchain_community.llms import Ollama
-from cassandra.auth import PlainTextAuthProvider
-import tempfile
-import cassio
-from PyPDF2 import PdfReader
-from cassandra.cluster import Cluster
-import warnings
-warnings.filterwarnings("ignore")
-
-from dotenv import load_dotenv
-import time
-load_dotenv()
-
-ASTRA_DB_SECURE_BUNDLE_PATH ='G:/GENAI/groq_astra/secure-connect-pdf-query-db.zip'
-groq_api_key=os.environ['GROQ_API_KEY']
-os.environ["LANGCHAIN_TRACING_V2"]="true"
-os.environ["LANGCHAIN_API_KEY"]="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"
-os.environ["LANGCHAIN_PROJECT"]="pt-only-pupil-70"
-os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com"
-ASTRA_DB_APPLICATION_TOKEN="AstraCS:SuHeqXWZDTGfvwliFFyCnCvM:29d8b2ec4888d271b8aa32b3675a20c050280680f2a95873fa33d265c889ae0d"
-ASTRA_DB_ID=os.getenv("ASTRA_DB_ID")
-ASTRA_DB_KEYSPACE="pdf_query_db"
-ASTRA_DB_API_ENDPOINT="https://68dfd628-1ad7-4951-ae84-45402a193c81-us-east1.apps.astra.datastax.com"
-ASTRA_DB_CLIENT_ID="SuHeqXWZDTGfvwliFFyCnCvM"
-ASTRA_DB_CLIENT_SECRET="JNZsN-R156.BfMJ+B4M4XvFMWNtQvxW2QZiR4kjTnPHdy9bcszr3UA-ZK7X_c_P20cKajX1_CeodPuQwJZvfWDfRfY_sEFCGdrYc2pobxoOX7UQ4p5.kIf1.oraLa-p"
-ASTRA_DB_TABLE='qa_mini_demo'
-cassio.init(token=ASTRA_DB_APPLICATION_TOKEN,database_id=ASTRA_DB_ID,secure_connect_bundle=ASTRA_DB_SECURE_BUNDLE_PATH)
-
-cloud_config = {
-    'secure_connect_bundle': ASTRA_DB_SECURE_BUNDLE_PATH
-}
-
-def doc_loader(pdf_reader):
-
-    encode_kwargs = {'normalize_embeddings': True}
-    huggigface_embeddings=HuggingFaceBgeEmbeddings(
-    model_name='BAAI/bge-small-en-v1.5',
-    # model_name='sentence-transformers/all-MiniLM-16-v2',
-    model_kwargs={'device':'cpu'},
-    encode_kwargs=encode_kwargs)
-
-
-    loader=PyPDFLoader(pdf_reader)
-    documents=loader.load_and_split()
-    
-   
-    text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
-    final_documents=text_splitter.split_documents(documents)
-
-    astrasession = Cluster(
-    cloud={"secure_connect_bundle": ASTRA_DB_SECURE_BUNDLE_PATH},
-    auth_provider=PlainTextAuthProvider("token", ASTRA_DB_APPLICATION_TOKEN),
-    ).connect()
-
-   
-    # Truncate the existing table
-    astrasession.execute(f'TRUNCATE {ASTRA_DB_KEYSPACE}.{ASTRA_DB_TABLE}')
-
-    astra_vector_store=Cassandra(
-    embedding=huggigface_embeddings,
-    table_name="qa_mini_demo",
-    session=astrasession,
-    keyspace=ASTRA_DB_KEYSPACE
-    )
-
-   
-    astra_vector_store.add_documents(final_documents)
-
-    return astra_vector_store
-
-def prompt_temp():
-    prompt=ChatPromptTemplate.from_template(
-       """ 
-        Answer the question based on provided context only.
-        Your context retrieval mechanism works correclty but your are not providing answer from context.
-        Please provide the most accurate response based on question.
-        {context},
-        Questions:{input}
-        """
-    )
-
-    return prompt
-
-def generate_response(llm,prompt,user_input,vectorstore):
-
-    
-    document_chain=create_stuff_documents_chain(llm,prompt)
-    retriever=vectorstore.as_retriever(search_type="similarity",search_kwargs={"k":5})
-    retrieval_chain=create_retrieval_chain(retriever,document_chain)
-    response=retrieval_chain.invoke({"input":user_input})
-
-    return response
-# ['answer']
-
-
-
-def main():
-    st.set_page_config(page_title='Chat Groq Demo')
-    st.header('Chat Groq Demo')
-    user_input=st.text_input('Enter the Prompt here')
-    file=st.file_uploader('Choose Invoice File',type='pdf')
-    
-    
-    submit = st.button("Submit")
-    st.session_state.submit_clicked = False
-    if submit :
-        st.session_state.submit_clicked = True
-        if user_input and file:  
-            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-                temp_file.write(file.getbuffer())
-                file_path = temp_file.name
-            # with open(file.name, mode='wb') as w:
-            #     # w.write(file.getvalue())
-            #     w.write(file.getbuffer())
-            llm=ChatGroq(groq_api_key=groq_api_key,model_name="gemma-7b-it")
-            prompt=prompt_temp()
-            
-            vectorstore=doc_loader(file_path)
-            
-            
-            response=generate_response(llm,prompt,user_input,vectorstore)
-            st.write(response['answer'])
-
-            with st.expander("Document Similarity Search"):
-                for i,doc in enumerate(response['context']):
-                        st.write(doc.page_content)
-                        st.write('---------------------------------')
-           
-
-
-if __name__=="__main__":
-    main()
-
-
-
-
-
-    
-
-
-
-
-
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.embeddings import OllamaEmbeddings
+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.document_loaders import PyPDFDirectoryLoader
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+# from langchain.vectorstores.cassandra import Cassandra
+from langchain_community.vectorstores import Cassandra
+from langchain_community.llms import Ollama
+from cassandra.auth import PlainTextAuthProvider
+import tempfile
+import cassio
+from PyPDF2 import PdfReader
+from cassandra.cluster import Cluster
+import warnings
+warnings.filterwarnings("ignore")
+
+from dotenv import load_dotenv
+import time
+load_dotenv()
+
+ASTRA_DB_SECURE_BUNDLE_PATH ='secure-connect-pdf-query-db.zip'
+
+cassio.init(token=ASTRA_DB_APPLICATION_TOKEN,database_id=ASTRA_DB_ID,secure_connect_bundle=ASTRA_DB_SECURE_BUNDLE_PATH)
+
+cloud_config = {
+    'secure_connect_bundle': ASTRA_DB_SECURE_BUNDLE_PATH
+}
+
+def doc_loader(pdf_reader):
+
+    encode_kwargs = {'normalize_embeddings': True}
+    huggigface_embeddings=HuggingFaceBgeEmbeddings(
+    model_name='BAAI/bge-small-en-v1.5',
+    # model_name='sentence-transformers/all-MiniLM-16-v2',
+    model_kwargs={'device':'cpu'},
+    encode_kwargs=encode_kwargs)
+
+
+    loader=PyPDFLoader(pdf_reader)
+    documents=loader.load_and_split()
+    
+   
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
+    final_documents=text_splitter.split_documents(documents)
+
+    astrasession = Cluster(
+    cloud={"secure_connect_bundle": ASTRA_DB_SECURE_BUNDLE_PATH},
+    auth_provider=PlainTextAuthProvider("token", ASTRA_DB_APPLICATION_TOKEN),
+    ).connect()
+
+   
+    # Truncate the existing table
+    astrasession.execute(f'TRUNCATE {ASTRA_DB_KEYSPACE}.{ASTRA_DB_TABLE}')
+
+    astra_vector_store=Cassandra(
+    embedding=huggigface_embeddings,
+    table_name="qa_mini_demo",
+    session=astrasession,
+    keyspace=ASTRA_DB_KEYSPACE
+    )
+
+   
+    astra_vector_store.add_documents(final_documents)
+
+    return astra_vector_store
+
+def prompt_temp():
+    prompt=ChatPromptTemplate.from_template(
+       """ 
+        Answer the question based on provided context only.
+        Your context retrieval mechanism works correclty but your are not providing answer from context.
+        Please provide the most accurate response based on question.
+        {context},
+        Questions:{input}
+        """
+    )
+
+    return prompt
+
+def generate_response(llm,prompt,user_input,vectorstore):
+
+    
+    document_chain=create_stuff_documents_chain(llm,prompt)
+    retriever=vectorstore.as_retriever(search_type="similarity",search_kwargs={"k":5})
+    retrieval_chain=create_retrieval_chain(retriever,document_chain)
+    response=retrieval_chain.invoke({"input":user_input})
+
+    return response
+# ['answer']
+
+
+
+def main():
+    st.set_page_config(page_title='Chat Groq Demo')
+    st.header('Chat Groq Demo')
+    user_input=st.text_input('Enter the Prompt here')
+    file=st.file_uploader('Choose Invoice File',type='pdf')
+    
+    
+    submit = st.button("Submit")
+    st.session_state.submit_clicked = False
+    if submit :
+        st.session_state.submit_clicked = True
+        if user_input and file:  
+            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+                temp_file.write(file.getbuffer())
+                file_path = temp_file.name
+            # with open(file.name, mode='wb') as w:
+            #     # w.write(file.getvalue())
+            #     w.write(file.getbuffer())
+            llm=ChatGroq(groq_api_key=groq_api_key,model_name="gemma-7b-it")
+            prompt=prompt_temp()
+            
+            vectorstore=doc_loader(file_path)
+            
+            
+            response=generate_response(llm,prompt,user_input,vectorstore)
+            st.write(response['answer'])
+
+            with st.expander("Document Similarity Search"):
+                for i,doc in enumerate(response['context']):
+                        st.write(doc.page_content)
+                        st.write('---------------------------------')
+           
+
+
+if __name__=="__main__":
+    main()
+
+
+
+
+
+    
+
+
+
+
+