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.env

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+GOOGLE_API_KEY = "AIzaSyBMWuP_369qtz_9d9IIe09MLf0z1O9hSxM"

app.py

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+import streamlit as st
+from langchain.document_loaders.csv_loader import CSVLoader
+import tempfile
+from bd import get_response
+
+def main():
+    st.title("Chat with CSV")
+
+    uploaded_file = st.sidebar.file_uploader("choose your csv file",type='csv')
+    
+    if uploaded_file is not None:
+        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            temp_file.write(uploaded_file.getvalue())
+            temp_file_path = temp_file.name
+
+            #Intializing CSVLoader
+            csv_loader = CSVLoader(file_path = temp_file_path, encoding = 'latin-1', csv_args={'delimiter':','})
+
+            #load csv data
+            data = csv_loader.load()
+
+            #input
+            user_ip = st.text_input("Enter Your Query/Message")
+            print(user_ip)
+
+            if user_ip:
+                response=get_response(data,user_ip)
+                st.write(response)
+
+
+if __name__ == "__main__" :
+    main()

bd.py

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+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+import os
+from dotenv import load_dotenv
+
+
+def list_available_models():
+    models = genai.models.list_models()
+
+    print("Available models:")
+    for model in models:
+        print(f"Name: {model.name}")
+        print(f"Description: {model.description}")
+        print(f"Supported methods: {', '.join(model.supported_methods)}")
+        print("\n")
+
+def get_response(file, query):
+    # Load environment variables
+    load_dotenv()
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=400)
+    context = '\n\n'.join(str(p.page_content) for p in file)
+    data = text_splitter.split_text(context)
+
+    # Specify the correct model name based on your requirements
+    model_name = 'models/chat-bison-001'
+    
+    # Specify the API key directly in the code
+    google_api_key = os.getenv("GOOGLE_API_KEY")
+
+    embeddings = GoogleGenerativeAIEmbeddings(model=model_name, google_api_key=google_api_key)
+
+    searcher = Chroma.from_texts(data, embeddings).as_retriever()
+
+    ques = 'Which country has maximum GDP?'
+    records = searcher.get_relevent_documents(ques)
+
+    prompt_template = """
+        You have to give the correct answer to the question from the provided context and make sure you give all details\n
+        Context: {context}\n
+        Question: {question}\n
+
+        Answer:
+    """
+    prompt = PromptTemplate(template=prompt_template, input_variable=['context', 'question'])
+
+    model = ChatGoogleGenerativeAI(model=model_name, temperature=0.5)
+
+    chain = load_qa_chain(model, chain_type='stuff', prompt=prompt)
+
+    response = chain(
+        {
+            'input_document': records,
+            'question': query
+        },
+        return_only_output=True
+    )
+
+    return response['output_text']

requirements.txt

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+langchain
+langchain_google_genai
+langchain-google-bigtable
+streamlit
+chromadb
+langchain-community
+python-dotenv
+