Create app.py

app.py

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+import os
+
+import pandas as pd
+import streamlit as st
+from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
+from llama_index import download_loader
+from matplotlib import pyplot as plt
+from pandasai.llm.openai import OpenAI
+
+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)
+    csv_response = reader.run_pandas_ai(
+        df,
+        query,
+        is_conversational_answer=False
+    )
+    return csv_response
+
+
+def save_file(doc):
+    fn = os.path.basename(doc.name)
+    # check if documents_folder exists in the directory
+    if not os.path.exists(documents_folder):
+        # if documents_folder does not exist then making the directory
+        os.makedirs(documents_folder)
+    # 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.
+    try:
+        documents = SimpleDirectoryReader(documents_folder).load_data()
+        index = GPTVectorStoreIndex.from_documents(documents)
+        return index
+    except Exception as e:
+        st.error("Failed to read documents")
+    
+
+
+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())