Update app.py

app.py

@@ -3,53 +3,73 @@ from huggingface_hub import InferenceClient
 import gspread
 from google.oauth2.service_account import Credentials
 
-# Google Sheets integration setup
-SERVICE_ACCOUNT_FILE = "path_to_your_service_account.json"
-SPREADSHEET_ID = "your_spreadsheet_id"
+# Connecting to Google Sheets
+SERVICE_ACCOUNT_FILE = "sheet-integration-agent@bold-physics-445007-g4.iam.gserviceaccount.com.json"
 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
+client_gs = gspread.authorize(credentials)
+spreadsheet = client_gs.open_by_key("1LHgb4eYDM2ImFY3e3h4kPUnDh6oRHpVz2PA29lQ3Xxg")
+worksheet = spreadsheet.sheet1
 
+# Connecting to the zephyr-7b-beta model via Inference API
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Function to retrieve data from Google Sheets
 def get_apartment_data(query):
-    """Fetches relevant apartment data from Google Sheets."""
     rows = worksheet.get_all_records()
     results = []
     for row in rows:
         if query.lower() in row["district"].lower() or query.lower() in row["developer"].lower():
             results.append(row)
-    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
+    if results:
+        return "\n".join(
+            [f"District: {row['district']}, Developer: {row['developer']}, Area: {row['apt_area']} m²"
+             for row in results]
+        )
+    else:
+        return "Sorry, no matching options found."
+
+# Function to handle queries
+def respond(message, history):
+    print("Query received:", message)
+    print("Chat history:", history)
+
+    if not history:
+        history = []
+
+    try:
+        # If the query is related to districts or developers
+        if "district" in message.lower() or "developer" in message.lower():
+            response = get_apartment_data(message)
+        else:
+            # Process the query through the model
+            messages = [{"role": "user", "content": message}]
+            for h in history:
+                messages.append(h)
+            generated_response = client.chat_completion(
+                messages=messages,
+                max_tokens=512,
+                temperature=0.7,
+                top_p=0.95,
+            )
+            response = generated_response["choices"][0]["message"]["content"]
+
+        # Forming the correct chat history
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": response})
+        return response, history
+
+    except Exception as e:
+        print(f"Error processing query: {e}")
+        history.append({"role": "assistant", "content": "An error occurred while processing your query."})
+        return "An error occurred while processing your query.", history
+
+# Gradio Interface
 chat_interface = gr.ChatInterface(
-    process_query,
-    chatbot=gr.Chatbot(label="Your Assistant"),
-    title="Apartment Finder Bot",
-    description="Ask about available apartments or anything else.",
+    respond,
+    chatbot_label="Apartment Agent",
+    input_label="Your Question",
+    output_label="Response",
 )
 
 if __name__ == "__main__":