Update app.py

app.py

@@ -1,87 +1,87 @@
-import os
-import pandas as pd
-import gradio as gr
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_experimental.agents import create_pandas_dataframe_agent
-from langchain_core.prompts import PromptTemplate
-# Set up API key for Google Gemini
-os.environ["GOOGLE_API_KEY"] = "AIzaSyDSorjiEVV2KCWelkDLFxQsju3KDQOF344"  # Replace with actual API key
-
-# Initialize the LLM
-llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
-
-# Placeholder for agent and dataframe
-agent = None
-df = None
-
-# Define the function to handle CSV uploads and set up the LangChain agent
-def handle_file_upload(file):
-    global agent, df
-    # Check if file has .csv extension
-    if not file.name.endswith(".csv"):
-        return "Error: Please upload a valid CSV file.", None
-    
-    # Load the uploaded file into a DataFrame
-    try:
-        df = pd.read_csv(file)  # Read directly from the file object
-        # Create a new LangChain agent with the uploaded DataFrame
-        agent = create_pandas_dataframe_agent(llm, df, verbose=True, allow_dangerous_code=True)
-        return "CSV uploaded successfully. You can now ask questions about the data.", df
-    except Exception as e:
-        return f"Error reading CSV file: {e}", None
-
-# Define the function to process the user query
-def answer_query(query):
-    if agent is None:
-        return "Please upload a CSV file first."
-    
-    # Invoke the agent with the query
-
-    formatted_query = PromptTemplate.from_template(
-'''
-Please act as a data analyst and respond to my queries with insights from the provided dataset.
-If your response involves numeric data or comparisons, format the answer in a clear tabular form whenever 
-it enhances readability and clarity.
-Provide analyses that highlight trends, patterns, and notable details in the data, and use tabular format 
-for presenting summaries, comparisons, or grouped data and whenever user asks listing or something similar
-to help illustrate your findings effectively. Additionally, interpret any findings with context and data-driven 
-reasoning as a skilled data analyst would. Also make sure not to give any data that is not asked by the user or
-not relevant to the given context
-Keep the above said details in mind and answer the below query:
-Query: 
-{query}
-'''
-    )
-    response = agent.invoke(query)
-    
-    # Check if the response contains tabular data
-    if isinstance(response, pd.DataFrame):
-        return response  # Display as table if it's a DataFrame
-    else:
-        # Format response as Markdown
-        return f"**Response:**\n\n{response['output']}"
-
-# Create the Gradio interface
-with gr.Blocks() as iface:
-    gr.Markdown("# ZEN-Analyser")
-    gr.Markdown("Upload a CSV file to view the data and ask questions about it.")
-    
-    # File upload component
-    file_input = gr.File(label="Upload CSV", file_types=[".csv"])
-    # Dataframe display for the uploaded CSV
-    data_output = gr.DataFrame(label="Uploaded Data")
-    # Textbox for entering queries
-    query_input = gr.Textbox(label="Enter your query")
-    # Markdown component for displaying the agent's response with Markdown support
-    response_output = gr.Markdown(label="Response")
-    
-    # Button to trigger query processing
-    query_button = gr.Button("Submit Query")
-
-    # Define event actions
-    file_input.upload(handle_file_upload, file_input, [response_output, data_output])
-    query_button.click(answer_query, query_input, response_output)
-
-# Launch the Gradio app
-iface.launch()
-
+import os
+import pandas as pd
+import gradio as gr
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_experimental.agents import create_pandas_dataframe_agent
+from langchain_core.prompts import PromptTemplate
+# Set up API key for Google Gemini
+os.environ["GOOGLE_API_KEY"] = "AIzaSyDSorjiEVV2KCWelkDLFxQsju3KDQOF344"  # Replace with actual API key
+
+# Initialize the LLM
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
+
+# Placeholder for agent and dataframe
+agent = None
+df = None
+
+# Define the function to handle CSV uploads and set up the LangChain agent
+def handle_file_upload(file):
+    global agent, df
+    # Check if file has .csv extension
+    if not file.name.endswith(".csv"):
+        return "Error: Please upload a valid CSV file.", None
+    
+    # Load the uploaded file into a DataFrame
+    try:
+        df = pd.read_csv(file)  # Read directly from the file object
+        # Create a new LangChain agent with the uploaded DataFrame
+        agent = create_pandas_dataframe_agent(llm, df, verbose=True, allow_dangerous_code=True)
+        return "CSV uploaded successfully. You can now ask questions about the data.", df
+    except Exception as e:
+        return f"Error reading CSV file: {e}", None
+
+# Define the function to process the user query
+def answer_query(query):
+    if agent is None:
+        return "Please upload a CSV file first."
+    
+    # Invoke the agent with the query
+
+    formatted_query = PromptTemplate.from_template(
+'''
+Please act as a data analyst and respond to my queries with insights from the provided dataset.
+If your response involves numeric data or comparisons, format the answer in a clear tabular form whenever 
+it enhances readability and clarity.
+Provide analyses that highlight trends, patterns, and notable details in the data, and use tabular format 
+for presenting summaries, comparisons, or grouped data and whenever user asks listing or something similar
+to help illustrate your findings effectively. Additionally, interpret any findings with context and data-driven 
+reasoning as a skilled data analyst would. Also make sure not to give any data that is not asked by the user or
+not relevant to the given context
+Keep the above said details in mind and answer the below query:
+Query: 
+{query}
+'''
+    )
+    response = agent.invoke(query)
+    
+    # Check if the response contains tabular data
+    if isinstance(response, pd.DataFrame):
+        return response  # Display as table if it's a DataFrame
+    else:
+        # Format response as Markdown
+        return f"**Response:**\n\n{response['output']}"
+
+# Create the Gradio interface
+with gr.Blocks() as iface:
+    gr.Markdown("# ZEN-Analyser")
+    gr.Markdown("Upload a CSV file to view the data and ask questions about it.")
+    
+    # File upload component
+    file_input = gr.File(label="Upload CSV", file_types=[".csv"])
+    # Dataframe display for the uploaded CSV
+    data_output = gr.DataFrame(label="Uploaded Data")
+    # Textbox for entering queries
+    query_input = gr.Textbox(label="Enter your query")
+    # Markdown component for displaying the agent's response with Markdown support
+    response_output = gr.Markdown(label="Response")
+    
+    # Button to trigger query processing
+    query_button = gr.Button("Submit Query")
+
+    # Define event actions
+    file_input.upload(handle_file_upload, file_input, [response_output, data_output])
+    query_button.click(answer_query, query_input, response_output)
+
+# Launch the Gradio app
+iface.launch()
+