Update app.py

app.py

@@ -1,48 +1,51 @@
 import gradio as gr
 import os
+import pandas as pd
 from huggingface_hub import InferenceClient
 
 # ===============================
-# LLM CLIENT
+# LLM CLIENT SETUP
 # ===============================
 HF_TOKEN = os.getenv("HF")
-
-client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN )
-
-# Problem Statement:
-# 1. Add your own HF token in the settings to get the LLM working.
-# 2. Update requirements.txt, app.py as needed.
-# 3. Develop a robust "Text-to-Code" analytical workflow hosted on a Hugging Face Space using the Qwen/Qwen2.5-7B-Instruct model.
-# The Workflow Requirements:
-
-# a.Code Generation (Planner): Transform natural language user queries into executable, sandboxed Python code (specifically using pandas).
-
-# b.Execution (Action): Securely execute the generated code on the Hugging Face Space server against the uploaded dataset.
-
-# c.Synthesis (Insight): Capture the raw output of the code execution and feed it back to the LLM to generate a natural language insight.
+client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
+
+def analyze_excel(message, history, file):
+    """
+    Problem Statement:
+    1. Add your own HF token in the settings to get the LLM working.
+    2. Update requirements.txt, app.py as needed.
+    3. Develop a robust "Text-to-Code" analytical workflow.
+    
+    Requirements:
+    a.Code Generation : Transform natural language user queries into executable, sandboxed Python code (specifically using pandas).
+    b.Execution : Securely execute the generated code on the Hugging Face Space server against the uploaded dataset.
+    c.Synthesis : Capture the raw output of the code execution and feed it back to the LLM to generate a natural language insight.
+    """
+    if file is None:
+        return "Please upload an Excel file to begin."
+    
+    # The function needs a return here to avoid a NoneType error in Gradio
+    return "File received! Candidate: Implement the Planner-Action-Synthesis logic here."
 
 # ===============================
-# UI
+# UI CONFIGURATION
 # ===============================
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 📊 Excel Analysis Agent (Code + Insight)")
-
-    excel_file = gr.File(
-        label="Upload Excel File",
-        file_types=[".xlsx"]
-    )
-
+    gr.Markdown("# 📊 Technical Assessment: Data Analysis Agent")
+    gr.Markdown("### Objective: Build a Text-to-Code workflow using Qwen 2.5")
+    
+    with gr.Row():
+        excel_file = gr.File(
+            label="1. Upload Dataset (.xlsx)",
+            file_types=[".xlsx"]
+        )
+    
     gr.ChatInterface(
         fn=analyze_excel,
         additional_inputs=[excel_file],
         type="messages",
-        examples=[
-            ["Which provider has the highest average claim amount?", None],
-            ["How many unique members are there?", None],
-            ["Explain trends in processing time across regions.", None],
-            ["Show python code to compute correlation between ClaimAmount and ProcessingCost.", None],
-        ],
+        description="2. Ask questions about your data (e.g., 'What is the average profit by region?')",
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()