Enhanced prompt for property

examples/openai-bot/main.py

@@ -1,25 +1,57 @@
 from textbase import bot, Message
 from textbase.models import OpenAI
 from typing import List
-
+import pandas as pd
+import pandas as pd
 # Load your OpenAI API key
-OpenAI.api_key = ""
+OpenAI.api_key = "YOUR_API_KEY_HERE"  # Replace with your actual API key
+
+# Load property data from a CSV file
+property_data = pd.read_csv("property_data.csv")  # Replace with the actual CSV file path
 
 # Prompt for GPT-3.5 Turbo
-SYSTEM_PROMPT = """You are chatting with an AI. There are no specific prefixes for responses, so you can ask or talk about anything you like.
-The AI will respond in a natural, conversational manner. Feel free to start the conversation with any question or topic, and let's have a
-pleasant chat!
+SYSTEM_PROMPT = """Welcome to the Best Property Finder! 
+
+I'm here to assist you in finding the perfect property investment that aligns with your unique preferences and behavior.
+To do that, let's engage in a meaningful conversation:    
+Tell me about your financial situation. What's your budget or investment capacity? Feel free to share your goals.
+Describe your ideal location for property investment. What city or neighborhood attracts you, and why?
+What are your long-term and short-term investment goals? How do you envision profiting from this investment?
+Imagine your dream property. What type of property appeals to you the most? What features or amenities are essential?
+Are you looking to diversify your investment portfolio or focusing solely on real estate?
+Share your thoughts on risk. How comfortable are you with investment risks?
+Are you willing to take risks for potentially higher returns?
 """
 
 @bot()
 def on_message(message_history: List[Message], state: dict = None):
+    # Extract the latest user message
+    user_message = message_history[-1].content[0].value.strip()
+
+    # Check if the user has provided budget and location
+    if "budget" in user_message.lower() and "location" in user_message.lower():
+        # User has provided budget and location, perform property search
+        budget = extract_budget(user_message)  # Implement a function to extract budget from user input
+        location = extract_location(user_message)  # Implement a function to extract location from user input
+
+        # Perform property search based on budget and location
+        property_results = search_properties(budget, location)  # Implement the property search function
 
-    # Generate GPT-3.5 Turbo response
-    bot_response = OpenAI.generate(
-        system_prompt=SYSTEM_PROMPT,
-        message_history=message_history, # Assuming history is the list of user messages
-        model="gpt-3.5-turbo",
-    )
+        if property_results:
+            bot_response = "Here are some properties that match your criteria:\n\n"
+            for result in property_results:
+                bot_response += f"- {result['property_name']} in {result['location']} for ${result['price']}\n"
+
+        else:
+            bot_response = "I couldn't find any properties that match your criteria. Please try different budget or location."
+
+    else:
+        # User hasn't provided budget and location, continue with the standard chatbot responses
+        bot_response = OpenAI.generate(
+            system_prompt=SYSTEM_PROMPT,
+            message_history=message_history,
+            model="gpt-3.5-turbo",
+        )
 
     response = {
         "data": {
@@ -41,4 +73,51 @@ def on_message(message_history: List[Message], state: dict = None):
     return {
         "status_code": 200,
         "response": response
-    }
+    }
+
+
+
+# Load property data from a CSV file (make sure to adjust the file path)
+property_data = pd.read_csv("property_data.csv")
+
+def extract_budget(user_input):
+    # Extract budget information from user input using regular expressions
+    # For example, extract any numeric value that represents the budget
+    matches = re.findall(r'\b\d+\b', user_input)
+    if matches:
+        return int(matches[0])  # Assuming the first numeric value represents the budget
+    return None
+
+def extract_location(user_input):
+    # Extract location information from user input using keyword matching
+    # You can customize this function to match specific keywords or use more advanced NLP techniques
+    keywords = ['location', 'city', 'neighborhood', 'area', 'place']
+    for keyword in keywords:
+        if keyword in user_input.lower():
+            # Remove the keyword from the user input to get the location
+            location = user_input.lower().replace(keyword, '').strip()
+            return location
+    return None
+
+def search_properties(budget, location):
+    # Filter the property_data DataFrame based on budget and location
+    filtered_properties = property_data
+
+    if budget is not None:
+        filtered_properties = filtered_properties[filtered_properties['price'] <= budget]
+
+    if location is not None:
+        filtered_properties = filtered_properties[filtered_properties['location'].str.lower() == location.lower()]
+
+    # Convert the filtered results to a list of dictionaries for response
+    results = []
+    for index, row in filtered_properties.iterrows():
+        result = {
+            'property_name': row['property_name'],
+            'location': row['location'],
+            'property_type': row['property_type'],
+            'price': row['price']
+        }
+        results.append(result)
+
+    return results

textbase/message.py

@@ -1,10 +1,34 @@
-from pydantic import BaseModel
-from typing import List
+@bot()
+def on_message(message_history: List[Message], state: dict = None):
+    # Extract the latest user message
+    user_message = message_history[-1].content[0].value.strip()
 
-class Content(BaseModel):
-    data_type: str
-    value: str
+    # Check if the user has provided property requirements
+    if "budget" in user_message.lower() or "location" in user_message.lower() or "investment goals" in user_message.lower():
+        # User has started providing requirements
+        bot_response = "Great! Thanks for sharing your preferences. Let's continue. Do you have a specific location in mind, or should I suggest some popular ones?"
+    else:
+        # User hasn't provided requirements yet, so prompt them
+        bot_response = "To assist you better, please share your property requirements. You can start by telling me about your budget, preferred location, and investment goals."
 
-class Message(BaseModel):
-    role: str  # "user" or "assistant"
-    content: List[Content]
+    response = {
+        "data": {
+            "messages": [
+                {
+                    "data_type": "STRING",
+                    "value": bot_response
+                }
+            ],
+            "state": state
+        },
+        "errors": [
+            {
+                "message": ""
+            }
+        ]
+    }
+
+    return {
+        "status_code": 200,
+        "response": response
+    }