Update app.py

app.py

@@ -1,22 +1,22 @@
 import gradio as gr
-from google import generativeai as genai  # Gemini GenAI SDK :contentReference[oaicite:0]{index=0}
+from google import generativeai as genai
 import os
 
 # — Load and configure Gemini API key from HF Secrets
 gemini_api_key = os.getenv("GEMINI_API_KEY")
 if not gemini_api_key:
-    raise ValueError("GEMINI_API_KEY not set in environment")  # secure secret 
-genai.configure(api_key=gemini_api_key)
+    raise ValueError("GEMINI_API_KEY not set in environment")
+genai.configure(api_key=gemini_api_key)  # sets auth for all calls :contentReference[oaicite:2]{index=2}
 
-# — Path to your uploaded business.txt in the Space
+# — Path to your uploaded business.txt
 business_file = os.path.join(os.path.dirname(__file__), "business.txt")
 
 def chat_with_business(message, history):
-    # 1️⃣ Read the business knowledge
+    # 1️⃣ Read business info
     with open(business_file, "r", encoding="utf-8") as f:
         business_info = f.read().strip()
 
-    # 2️⃣ Build the system prompt
+    # 2️⃣ Build system prompt
     system_prompt = (
         "You are a helpful customer-care assistant. "
         "Use only the information below to answer questions. "
@@ -24,27 +24,30 @@ def chat_with_business(message, history):
         f"{business_info}\n\n"
     )
 
-    # 3️⃣ Call Gemini 2.5 Flash to generate response :contentReference[oaicite:1]{index=1}
+    # 3️⃣ Call Gemini 2.5 Flash
     model = genai.GenerativeModel(model_name="gemini-2.5-flash-preview-04-17")
-    response = model.generate_content(
-        system_prompt + "User: " + message
-    )
+    response = model.generate_content(system_prompt + "User: " + message)  # :contentReference[oaicite:3]{index=3}
 
-    # 4️⃣ Return the assistant’s reply
+    # 4️⃣ Return text
     return response.text
 
-# — Build Gradio frontend (Blocks API for future customization)
+# — Build Gradio UI with Blocks and messages format
 with gr.Blocks(theme="soft") as demo:
     gr.Markdown("## 🌿 My Business Bot")
     gr.Markdown("*Ask anything about your business in Hindi-English*")
-    chatbot = gr.Chatbot(elem_id="chatbox", height=400)
+    chatbot = gr.Chatbot(elem_id="chatbox", height=400, type="messages")  # use messages format :contentReference[oaicite:4]{index=4}
     user_input = gr.Textbox(placeholder="Type your question here...", show_label=False)
 
-    user_input.submit(
-        lambda msg, hist: (chat_with_business(msg, hist), ""), 
-        [user_input, chatbot], 
-        [chatbot, user_input]
-    )
+    def handle(msg, hist):
+        reply = chat_with_business(msg, hist)
+        # Append OpenAI-style dicts, not tuples :contentReference[oaicite:5]{index=5}
+        hist = hist + [
+            {"role": "user",      "content": msg},
+            {"role": "assistant", "content": reply}
+        ]
+        return hist, ""
+
+    user_input.submit(handle, [user_input, chatbot], [chatbot, user_input])
 
 if __name__ == "__main__":
     demo.launch()