Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+from langchain.llms import OpenAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.agents import create_pandas_dataframe_agent
+import os
+import openai
+openai.api_key = os.getenv('api_token')
+
+# Load the CSV file
+def load_csv(file):
+    print ("File is",file)
+    df = pd.read_csv(file.name)
+    print (df.head())
+    return df
+
+# Define the function that generates the response
+def generate_response(question, file):
+    # Load the CSV file
+    df = load_csv(file)
+
+    # Initialize OpenAI pipeline
+    agent = create_pandas_dataframe_agent(OpenAI(temperature=0,openai_api_key=openai.api_key), df, verbose=True)
+
+    # Generate response using OpenAI
+    response = agent.run(question)
+
+    return response
+
+# Define the input and output interfaces
+title = "DataFrame Detective: Ask questions directly to your dataframe"
+csv_file = gr.inputs.File(label="CSV File")
+question = gr.inputs.Textbox(label="Question")
+output_text = gr.outputs.Textbox(label="Response")
+
+# Create the Gradio app
+gr.Interface(generate_response, inputs=[question, csv_file], outputs=output_text, title=title).launch(debug=True)