Added main file

chat_with_docs.py

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+from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
+from llama_index import download_loader
+from pandasai.llm.openai import OpenAI
+from matplotlib import pyplot as plt
+import streamlit as st
+import pandas as pd
+import os
+
+
+documents_folder = "./documents"
+
+# Load PandasAI loader, Which is a wrapper over PandasAI library
+PandasAIReader = download_loader("PandasAIReader")
+
+st.title("Welcome to `ChatwithDocs`")
+st.header("Interact with Documents such as `PDFs/CSV/Docs` using the power of LLMs\nPowered by `LlamaIndex🦙` \nCheckout the [GITHUB Repo Here](https://github.com/anoopshrma/Chat-with-Docs) and Leave a star⭐")
+
+
+def get_csv_result(df, query):
+  reader = PandasAIReader(llm=csv_llm)
+  response = reader.run_pandas_ai(
+    df, 
+    query, 
+    is_conversational_answer=False
+    )
+  return response
+
+def save_file(doc):        
+    fn = os.path.basename(doc.name)
+    # open read and write the file into the server
+    open(documents_folder+'/'+fn, 'wb').write(doc.read())
+    # Check for the current filename, If new filename
+    # clear the previous cached vectors and update the filename 
+    # with current name     
+    if st.session_state.get('file_name'):
+        if st.session_state.file_name != fn:
+            st.cache_resource.clear()
+            st.session_state['file_name'] = fn
+    else:
+        st.session_state['file_name'] = fn
+
+    return fn
+
+def remove_file(file_path):
+    # Remove the file from the Document folder once 
+    # vectors are created
+    if os.path.isfile(documents_folder+'/'+file_path):
+        os.remove(documents_folder+'/'+file_path)
+
+    
+
+@st.cache_resource
+def create_index():
+    # Create vectors for the file stored under Document folder. 
+    # NOTE: You can create vectors for multiple files at once.
+    documents = SimpleDirectoryReader(documents_folder).load_data()
+    index = GPTVectorStoreIndex.from_documents(documents)
+    return index
+
+
+
+def query_doc(vector_index, query):
+    # Applies Similarity Algo, Finds the nearest match and 
+    # take the match and user query to OpenAI for rich response
+    query_engine = vector_index.as_query_engine()
+    response = query_engine.query(query)
+    return response
+
+
+api_key = st.text_input("Enter your OpenAI API key here:", type="password")
+if api_key:
+    os.environ['OPENAI_API_KEY'] = api_key
+    csv_llm = OpenAI(api_token=api_key)
+
+
+tab1, tab2= st.tabs(["CSV", "PDFs/Docs"])
+
+with tab1:
+   
+   st.write("Chat with CSV files using PandasAI loader with LlamaIndex")
+   input_csv = st.file_uploader("Upload your CSV file", type=['csv'])
+   
+   if input_csv is not None:      
+    st.info("CSV Uploaded Successfully")
+    df = pd.read_csv(input_csv)
+    st.dataframe(df, use_container_width=True)
+    
+
+   st.divider()
+   
+   input_text = st.text_area("Ask your query")
+   
+   if input_text is not None:
+    if st.button("Send"):
+      st.info("Your query: "+ input_text)
+      with st.spinner('Processing your query...'):
+        response = get_csv_result(df, input_text)
+      if plt.get_fignums():
+          st.pyplot(plt.gcf())
+      else:
+        st.success(response)
+
+    
+with tab2:
+   st.write("Chat with PDFs/Docs")
+   input_doc = st.file_uploader("Upload your Docs")
+   
+   if input_doc is not None: 
+       st.info("Doc Uploaded Successfully")
+       file_name = save_file(input_doc)
+       index = create_index()
+       remove_file(file_name)
+       
+   
+   st.divider()
+   input_text = st.text_area("Ask your question")
+   
+   if input_text is not None:
+    if st.button("Ask"):
+        st.info("Your query: \n" +input_text)
+        with st.spinner("Processing your query.."):
+            response = query_doc(index, input_text)
+            print(response)
+        
+        st.success(response)
+
+        st.divider()
+        # Shows the source documents context which 
+        # has been used to prepare the response
+        st.write("Source Documents")
+        st.write(response.get_formatted_sources())