Add Gemini API. Set latest Gradio version.

README.md

@@ -1,15 +1,15 @@
 ---
+# This is the Hugging Face Space config block
 title: Excel AI Analyzer
 emoji: 📊
 colorFrom: indigo
 colorTo: blue
 sdk: gradio
-sdk_version: 4.39.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 ---
 
 # 📊 Excel AI Analyzer
 
-Upload an Excel file and ask questions about revenue and occupancy.  
-Built with **Gradio** + **pandas**.
+This application allows users to upload an Excel file, analyze its contents, and interact with the data through a chat interface powered by Google's Gemini API.

app.py

@@ -1,61 +1,85 @@
 import pandas as pd
 import gradio as gr
+from langchain.agents import initialize_agent, Tool
+from langchain_google_genai import ChatGoogleGenerativeAI  # requires GOOGLE_API_KEY set as env var
 
 # Store the uploaded dataframe in memory
 dataframes = {}
 
-def upload_excel(file):
-    if file is None:
-        return "Please upload an Excel file first."
+# -----------------------------
+# Tools (Excel Q&A Functions)
+# -----------------------------
+def get_otb_revenue(month: str) -> str:
+    if "df" not in dataframes:
+        return "Please upload a file first."
+    df = dataframes["df"]
     try:
-        df = pd.read_excel(file.name)
-        dataframes["df"] = df
-        preview = df.head().to_string()
-        return f"✅ File uploaded successfully! Here are the first rows:\n\n{preview}"
-    except Exception as e:
-        return f"❌ Error reading Excel file: {e}"
+        otb = df.loc[df['Month'].str.lower() == month.lower(), 'OTB Revenue'].values[0]
+        stly = df.loc[df['Month'].str.lower() == month.lower(), 'STLY Revenue'].values[0]
+        return f"OTB Revenue for {month}: {otb}, STLY Revenue: {stly}"
+    except Exception:
+        return f"Could not find OTB/ STLY Revenue for {month}"
 
-def chat_agent(message, history):
+def check_occupancy(month: str) -> str:
     if "df" not in dataframes:
-        return "Please upload an Excel file first."
-
+        return "Please upload a file first."
     df = dataframes["df"]
+    try:
+        occ = df.loc[df['Month'].str.lower() == month.lower(), 'Occupancy %'].values[0]
+        target = df.loc[df['Month'].str.lower() == month.lower(), 'Budgeted Occupancy %'].values[0]
+        if occ >= target:
+            return f"Occupancy for {month} is {occ}% vs target {target}% ✅ On track!"
+        else:
+            return f"Occupancy for {month} is {occ}% vs target {target}% ⚠️ Might not meet target."
+    except Exception:
+        return f"Could not find occupancy data for {month}"
 
-    # Very simple “question routing” demo
-    message_lower = message.lower()
-
-    if "otb" in message_lower and "revenue" in message_lower and "august" in message_lower:
-        try:
-            otb_august = df.loc[df['Month'] == 'August', 'OTB Revenue'].values[0]
-            stly_august = df.loc[df['Month'] == 'August', 'STLY Revenue'].values[0]
-            return f"OTB Revenue (August): {otb_august}\nSTLY Revenue (August): {stly_august}"
-        except:
-            return "I couldn't find OTB Revenue data for August in your file."
+# -----------------------------
+# Upload Excel
+# -----------------------------
+def upload_excel(file):
+    if file is None:
+        return "Please upload an Excel file."
+    df = pd.read_excel(file.name)
+    dataframes["df"] = df
+    return f"✅ File loaded with {len(df)} rows and {len(df.columns)} columns."
 
-    elif "occupancy" in message_lower and "august" in message_lower:
-        try:
-            occ_actual = df.loc[df['Month'] == 'August', 'Occupancy %'].values[0]
-            occ_target = df.loc[df['Month'] == 'August', 'Budgeted Occupancy %'].values[0]
-            if occ_actual >= occ_target:
-                return f"Occupancy for August is {occ_actual}% vs target {occ_target}% ✅ On track!"
-            else:
-                return f"Occupancy for August is {occ_actual}% vs target {occ_target}% ⚠️ Might not meet target."
-        except:
-            return "I couldn't find occupancy data for August in your file."
+# -----------------------------
+# LangChain Agent Setup
+# -----------------------------
+tools = [
+    Tool(
+        name="Get OTB Revenue",
+        func=get_otb_revenue,
+        description="Get OTB Revenue and STLY Revenue for a given month (e.g. 'August')"
+    ),
+    Tool(
+        name="Check Occupancy",
+        func=check_occupancy,
+        description="Check occupancy vs target for a given month (e.g. 'August')"
+    )
+]
 
-    else:
-        return "Sorry, I can only answer about OTB Revenue and Occupancy for now. (You can extend me with more rules!)"
+llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0)  # Requires GOOGLE_API_KEY in environment
+agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
 
+def chat_agent(message, history):
+    if "df" not in dataframes:
+        return "Please upload a file first."
+    try:
+        return agent.run(message)
+    except Exception as e:
+        return f"⚠️ Agent error: {e}"
 
+# -----------------------------
+# Gradio UI
+# -----------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# 📊 OtelAI Excel Q&A Agent")
-    gr.Markdown("Upload an Excel report and then ask questions about OTB revenue or occupancy.")
-
+    gr.Markdown("# 🤖 Excel Agent (LangChain + Gemini)")
     with gr.Row():
         with gr.Column():
-            file_input = gr.File(label="Upload Excel file", type="file")
+            file_input = gr.File(label="Upload Excel file", type="filepath")
             upload_output = gr.Textbox(label="Upload status")
-
         file_input.upload(upload_excel, file_input, upload_output)
 
     gr.ChatInterface(fn=chat_agent, title="Excel Chat Agent")

requirements.txt

@@ -1,3 +1,5 @@
+gradio==4.44.1
 pandas
 openpyxl
-gradio
+langchain
+langchain-google-genai