Upload app.py

app.py

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+import streamlit as st
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+import pandas as pd
+
+st.set_page_config(
+    page_title="AI Data Explorer",
+    page_icon="💻",
+)
+st.header("AI Data Explorer with Gemini API",divider="rainbow")
+
+
+api_key = st.sidebar.text_input("Enter your Gemini API key", type="password")
+
+# File uploader for CSV file
+uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type="csv")
+
+# Function to create and return an agent
+def create_agent(api_key, df, llm):
+    
+    # Create the pandas agent with the DataFrame and LLM
+    agent = create_pandas_dataframe_agent(
+        llm, 
+        df, 
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 
+        verbose=True, 
+        allow_dangerous_code=True
+    )
+    return agent
+
+# Application description
+st.markdown("""
+## About this Application 🤖📊
+
+This application allows you to explore and analyze your dataset using an AI-powered agent. 
+You can upload a CSV file and provide your Gemini API key to create an agent capable of answering questions about your data.
+
+### How to Use 🛠️
+1. 🔑 Enter your Gemini API key in the sidebar.
+2. 📁 Upload a CSV file containing your dataset.
+3. ❓ Enter your query about the dataset in the input field provided.
+4. 🚀 The AI agent will process your query and display the results.
+
+The AI agent leverages the power of a LangChain and large language model (LLM) to understand and analyze your data, providing insights and answers based on your questions.
+""")
+
+# Process the uploaded CSV file and create the agent
+if uploaded_file is not None and api_key:
+    llm = ChatGoogleGenerativeAI(model="gemini-pro",google_api_key=api_key)
+
+    df = pd.read_csv(uploaded_file)
+    st.write("Uploaded CSV file:")
+    st.dataframe(df)
+
+    agent = create_agent(api_key, df, llm)
+
+    # Input field for user query
+    user_query = st.text_input("Enter your query about the dataset")
+
+    # Process the user query and display the result
+    if user_query:
+        with st.spinner('Processing your query...'):
+            try:
+                result = agent.run(user_query)
+                st.success("Query result:")
+                result
+            except Exception as e:
+                st.error(f"Error processing query: {e}")
+else:
+    st.write("Please enter your Gemini API key and upload a CSV file")