Update app.py

app.py

@@ -1,76 +1,56 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
 import gspread
 from google.oauth2.service_account import Credentials
-from huggingface_hub import InferenceClient
-
-# Google Sheets Configuration
-SERVICE_ACCOUNT_FILE = "sheet-integration-agent@bold-physics-445007-g4.iam.gserviceaccount.com.json"
-SPREADSHEET_ID = "1LHgb4eYDM2ImFY3e3h4kPUnDh6oRHpVz2PA29lQ3Xxg"
 
-# Authenticate and connect to Google Sheets
+# Google Sheets integration setup
+SERVICE_ACCOUNT_FILE = "path_to_your_service_account.json"
+SPREADSHEET_ID = "your_spreadsheet_id"
 scopes = ["https://www.googleapis.com/auth/spreadsheets"]
 credentials = Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=scopes)
 client = gspread.authorize(credentials)
 worksheet = client.open_by_key(SPREADSHEET_ID).sheet1
 
-# Hugging Face Model Configuration
-model_name = "HuggingFaceH4/zephyr-7b-beta"
-hf_client = InferenceClient(model_name)
-
 def get_apartment_data(query):
-    """
-    Fetch apartment data based on the user query from Google Sheets.
-    """
+    """Fetches relevant apartment data from Google Sheets."""
     rows = worksheet.get_all_records()
     results = []
     for row in rows:
-        if query.lower() in row.get("district", "").lower() or query.lower() in row.get("developer", "").lower():
+        if query.lower() in row["district"].lower() or query.lower() in row["developer"].lower():
             results.append(row)
-    if results:
-        return "\n".join([str(result) for result in results])
-    return "No matching apartments found for your query."
-
-def respond(message, history):
-    """
-    Respond to user messages using Hugging Face and Google Sheets data.
-    """
-    try:
-        # Check if the query relates to apartments
-        if "apartment" in message.lower() or "district" in message.lower():
-            sheet_response = get_apartment_data(message)
-            history.append({"role": "assistant", "content": sheet_response})
-            return history
-
-        # Default response from the Hugging Face model
-        messages = [{"role": "system", "content": "You are a friendly real estate assistant."}]
-        for user_message, bot_response in history:
-            if user_message:
-                messages.append({"role": "user", "content": user_message})
-            if bot_response:
-                messages.append({"role": "assistant", "content": bot_response})
-
-        messages.append({"role": "user", "content": message})
-
-        response = ""
-        for chunk in hf_client.chat_completion(messages, stream=True):
-            token = chunk.choices[0].delta.content
-            response += token
-
-        history.append({"role": "assistant", "content": response})
-        return history
-
-    except Exception as e:
-        error_message = f"An error occurred: {str(e)}"
-        history.append({"role": "assistant", "content": error_message})
-        return history
-
-# Gradio Interface
+    return results if results else "Sorry, no matching apartments found."
+
+# Hugging Face model setup
+hf_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+def process_query(message, history):
+    """Processes the user's query and returns a response."""
+    # Check for specific queries (e.g., related to apartments)
+    if "apartment" in message.lower() or "district" in message.lower():
+        result = get_apartment_data(message)
+        return {"role": "assistant", "content": str(result)}
+    
+    # Default: Handle with Hugging Face model
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    for msg in history:
+        messages.append(msg)
+    messages.append({"role": "user", "content": message})
+
+    # Fetch response from the Hugging Face model
+    response = ""
+    for res in hf_client.chat_completion(messages, stream=True):
+        token = res.choices[0].delta.content
+        response += token
+
+    return {"role": "assistant", "content": response}
+
+# Gradio ChatInterface
 chat_interface = gr.ChatInterface(
-    respond,
-    chatbot=gr.Chatbot(),
-    title="Real Estate Chatbot",
-    description="Ask about available apartments or districts in our database.",
+    process_query,
+    chatbot=gr.Chatbot(label="Your Assistant"),
+    title="Apartment Finder Bot",
+    description="Ask about available apartments or anything else.",
 )
 
 if __name__ == "__main__":
-    chat_interface.launch()
+    chat_interface.launch()