Update app.py

Update with new UI to show history of questions

app.py

@@ -1,6 +1,5 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
 import os
+import gradio as gr
 import pandas as pd
 from langchain_together import ChatTogether
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
@@ -11,7 +10,10 @@ from langchain_core.messages import ToolMessage
 from langchain_core.runnables import RunnablePassthrough
 from operator import itemgetter
 
+# Global variable to store QA history
+qa_history = []
 
+    
 def load_model(api_key):
     return ChatTogether(
         api_key=TOGETHER_API_KEY,
@@ -53,34 +55,80 @@ def create_chain(df, llm):
     
     return chain
 
+
+def update_qa_history():
+    # Convert QA history to DataFrame for display
+    if not qa_history:
+        return pd.DataFrame(columns=["CSV File", "Question", "Answer"]).to_markdown()
+    return pd.DataFrame(qa_history, columns=["CSV File", "Question", "Answer"]).to_markdown()
+
+
 def process_query(csv_file, api_key, query):
     if not api_key.strip():
-        return "Please provide an API key"
+        return "Please provide an API key", update_qa_history()
     
     try:
         df = pd.read_csv(csv_file.name)
         llm = load_model(api_key)
         chain = create_chain(df, llm)
         result = chain.invoke({"question": query})
-        return f"Analysis Result:\n{result['response']}\n\nTechnical Details:\n{result['tool_output']}"
+        
+        # Format the response
+        response = f"Analysis Result:\n{result['response']}\n\nTechnical Details:\n{result['tool_output']}"
+        
+        # Extract just the filename without path
+        filename = os.path.basename(csv_file.name)
+        
+        # Add to QA history
+        qa_history.append([
+            filename,  # Store only the filename
+            query,
+            result['response']  # Store just the human-readable response
+        ])
+        
+        return response, update_qa_history()
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error: {str(e)}", update_qa_history()
 
 # Create Gradio interface
-iface = gr.Interface(
-    fn=process_query,
-    inputs=[
-        gr.File(label="Upload CSV File"),
-        gr.Textbox(label="Together.ai API Key", type="password"),
-        gr.Textbox(label="Your Question")
-    ],
-    outputs=gr.Textbox(label="Result"),
-    title="CSV Analysis Assistant",
-    description="Upload a CSV file and ask questions about it using natural language.",
-    examples=[
-        ["titanic_dataset.csv", "your-api-key-here", "Which columns have missing values?"],
-        ["titanic_dataset.csv", "your-api-key-here", "What's the correlation between age and fare?"]
-    ]
-)
-
-iface.launch()
+with gr.Blocks(title="CSV Analysis Assistant") as iface:
+    gr.Markdown("# CSV Analysis Assistant")
+    gr.Markdown("Upload a CSV file and ask questions about it using natural language.")
+    
+    # Top section: Split into left (inputs) and right (result)
+    with gr.Row():
+        # Left column for inputs
+        with gr.Column(scale=1):
+            file_input = gr.File(label="Upload CSV File")
+            api_key = gr.Textbox(label="Together.ai API Key", type="password")
+            query = gr.Textbox(label="Your Question")
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                submit_btn = gr.Button("Submit", variant="primary")
+        
+        # Right column for result
+        with gr.Column(scale=1):
+            output = gr.Textbox(label="Result", lines=10)
+    
+    # Bottom section: Full width for history table
+    with gr.Row():
+        history = gr.Markdown(value="### Question & Answer History\n" + update_qa_history())
+    
+    # Handle button events
+    submit_btn.click(
+        fn=process_query,
+        inputs=[file_input, api_key, query],
+        outputs=[output, history]
+    )
+    
+    def clear_inputs():
+        return [None, "", "", "", "### Question & Answer History\n" + update_qa_history()]
+    
+    clear_btn.click(
+        fn=clear_inputs,
+        inputs=[],
+        outputs=[file_input, api_key, query, output, history]
+    )
+    
+# For Hugging Face Spaces deployment
+iface.launch()