Update app.py

app.py

@@ -1,134 +1,135 @@
 import streamlit as st
 import os
 import sys
-from crewai import Agent, Task, Crew, Process
+
+# FIX 1: Corrected imports for CrewAI 2.0+
+from crewai import Agent, Task, Crew, Process, BaseTool
 from crewai_tools import SerperDevTool
-from crewai.tools import BaseTool
-from langchain_openai import ChatOpenAI
+from langchain_openai import ChatOpenAI 
 
-# 1. Page Configuration
-st.set_page_config(page_title="Smart Relocation Assistant", page_icon="🏘️")
+# --- 1. PAGE CONFIGURATION ---
+st.set_page_config(page_title="Smart Relocation Assistant", page_icon="🏘️", layout="wide")
 
 st.title("🏘️ Smart Relocation Assistant")
 st.markdown("Use AI Agents to find your perfect neighborhood based on budget and lifestyle.")
 
-# 2. Sidebar for API Keys (Optional but recommended for public spaces)
-with st.sidebar:
-    st.header("API Configuration")
-    st.info("If running locally, set keys in .env. On Hugging Face, set them in 'Settings' -> 'Secrets'.")
-    
-    # Check if keys are already in environment (HF Secrets)
-    openai_api_key = os.getenv("OPENAI_API_KEY")
-    serper_api_key = os.getenv("SERPER_API_KEY")
+# --- 2. API KEY SETUP ---
+# Fetch keys from environment variables (Hugging Face Secrets)
+openai_api_key = os.getenv("OPENAI_API_KEY")
+serper_api_key = os.getenv("SERPER_API_KEY")
+
+if not (openai_api_key and serper_api_key):
+    st.error("🚨 API Keys not found!")
+    st.warning("Please set OPENAI_API_KEY and SERPER_API_KEY in your Hugging Face Space 'Settings' -> 'Variables and secrets'.")
+    st.stop()
+
+# Set keys back into environment for the tools/libraries
+os.environ["OPENAI_API_KEY"] = openai_api_key
+os.environ["SERPER_API_KEY"] = serper_api_key
+
+# --- 3. TOOL DEFINITIONS ---
 
-    # If not found in env, ask user to input them
-    if not openai_api_key:
-        openai_api_key = st.text_input("OpenAI API Key", type="password")
-    if not serper_api_key:
-        serper_api_key = st.text_input("Serper API Key", type="password")
+# The Search Tool is now initialized here
+search_tool = SerperDevTool()
 
-# 3. Define Tools
+# Custom Calculator Tool (inherits from the now-correctly imported BaseTool)
 class CalculatorTool(BaseTool):
     name: str = "Budget Calculator"
-    description: str = "Useful for calculating if a rent price fits within a budget."
+    description: str = "Useful for calculating if a rent price fits within a budget. Input must be a mathematical expression."
 
     def _run(self, expression: str) -> str:
         try:
+            # Safe evaluation of simple math
             return str(eval(expression))
         except:
             return "Error in calculation"
 
-# 4. Main Application Logic
-if openai_api_key and serper_api_key:
-    # Set keys for the libraries
-    os.environ["OPENAI_API_KEY"] = openai_api_key
-    os.environ["SERPER_API_KEY"] = serper_api_key
+calc_tool = CalculatorTool()
+
+# --- 4. LLM DEFINITION (The "gpt-4o-mini" Fix) ---
+# FIX 2: Explicitly define the gpt-4o-mini model
+gpt_4o_mini = ChatOpenAI(
+    model="gpt-4o-mini",
+    temperature=0.7 
+)
+
+
+# --- 5. STREAMLIT INPUTS ---
+st.header("1. Enter Your Relocation Details")
+col1, col2 = st.columns(2)
+with col1:
+    city = st.text_input("Target City", "Austin, TX")
+    budget = st.number_input("Max Monthly Rent ($)", min_value=500, value=2500)
+with col2:
+    work_location = st.text_input("Work Location", "Downtown")
     
-    # Initialize Tools
-    search_tool = SerperDevTool()
-    calc_tool = CalculatorTool()
-
-    # Inputs
-    col1, col2 = st.columns(2)
-    with col1:
-        city = st.text_input("Target City", "Austin, TX")
-        budget = st.number_input("Max Monthly Rent ($)", min_value=500, value=2500)
-    with col2:
-        work_location = st.text_input("Work Location", "Downtown")
-        
-    preferences = st.text_area("Lifestyle Preferences", "Walkable, near coffee shops, safe, good for young professionals")
-
-    if st.button("Start Research"):
-        with st.spinner('🤖 AI Agents are scouting neighborhoods and crunching numbers...'):
-            try:
-                # --- AGENT DEFINITIONS ---
-                city_scout = Agent(
-                    role='City Neighborhood Scout',
-                    goal=f'Identify the top 3 neighborhoods in {city} that match: {preferences}.',
-                    backstory="You are a local expert who knows every corner of the city. You prioritize safety, commute, and lifestyle fit.",
-                    tools=[search_tool],
-                    verbose=True,
-                    allow_delegation=False
-                )
-
-                budget_analyst = Agent(
-                    role='Relocation Financial Advisor',
-                    goal=f'Assess affordability and ensure rent is within {budget}.',
-                    backstory="You are a pragmatic financial advisor. You find average rent prices and filter out options that exceed the budget.",
-                    tools=[search_tool, calc_tool],
-                    verbose=True,
-                    allow_delegation=False
-                )
-
-                # --- TASK DEFINITIONS ---
-                scout_task = Task(
-                    description=f"""
-                    1. Search for neighborhoods in {city} that are: {preferences}.
-                    2. Consider commute to {work_location}.
-                    3. Select top 5 candidates.
-                    """,
-                    expected_output="List of 5 neighborhoods with vibe descriptions.",
-                    agent=city_scout
-                )
-
-                analysis_task = Task(
-                    description=f"""
-                    1. For the neighborhoods found, search current average rent for 1-bedroom.
-                    2. Filter out any where rent > {budget}.
-                    3. Calculate remaining budget.
-                    """,
-                    expected_output="Filtered list of affordable neighborhoods with costs.",
-                    agent=budget_analyst
-                )
-
-                report_task = Task(
-                    description="""
-                    Compile a final report. Rank top 3 affordable neighborhoods.
-                    Include: Neighborhood Name, Vibe, Rent Cost, Remaining Budget, and one Local Highlight.
-                    Format as clear Markdown.
-                    """,
-                    expected_output="Markdown formatted relocation guide.",
-                    agent=budget_analyst
-                )
-                gpt_4o_mini = ChatOpenAI(model="gpt-4o-mini",temperature=0.7)
-
-                # --- CREW EXECUTION ---
-                relocation_crew = Crew(agents=[city_scout, budget_analyst],
-                                       tasks=[scout_task, analysis_task, report_task],
-                                       process=Process.sequential,
-                                       # PASS THE LLM HERE
-                                       llm=gpt_4o_mini
-                                      )
-
-                result = relocation_crew.kickoff()
-                
-                # Display Results
-                st.success("Research Complete!")
-                st.markdown("### 📋 Your Relocation Plan")
-                st.markdown(result)
-                
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
-
-else:
-    st.warning("Please provide your API Keys to proceed.")
+preferences = st.text_area("Lifestyle Preferences", "Walkable, near coffee shops, safe, good for young professionals")
+
+# --- 6. AGENT & TASK DEFINITIONS (Defined in the function for cleaner scope) ---
+
+def run_crew_analysis(city, work_location, budget, preferences):
+    # FIX 2 (CONT.): Passing the LLM instance to the Agents
+    city_scout = Agent(
+        role='City Neighborhood Scout',
+        goal=f'Identify the top 3 neighborhoods in {city} that match these preferences: {preferences}.',
+        backstory="You are a local expert who knows every corner of the city. You prioritize safety, commute, and lifestyle fit.",
+        tools=[search_tool],
+        llm=gpt_4o_mini, # Explicit LLM setting
+        verbose=True,
+        allow_delegation=False
+    )
+
+    budget_analyst = Agent(
+        role='Relocation Financial Advisor',
+        goal=f'Assess the affordability of specific neighborhoods and ensure rent is within {budget}.',
+        backstory="You are a pragmatic financial advisor. You strictly filter out any options that exceed the user's budget.",
+        tools=[search_tool, calc_tool],
+        llm=gpt_4o_mini, # Explicit LLM setting
+        verbose=True,
+        allow_delegation=False
+    )
+
+    scout_task = Task(
+        description=f"Search for neighborhoods in {city} that are: {preferences}. Consider commute to {work_location}. Select top 5 candidates.",
+        expected_output="List of 5 neighborhoods with vibe descriptions.",
+        agent=city_scout
+    )
+
+    analysis_task = Task(
+        description=f"For the 5 neighborhoods found, search current average rent for a 1-bedroom. Filter out any where rent > {budget}. Calculate remaining budget.",
+        expected_output="Filtered list of affordable neighborhoods with costs.",
+        agent=budget_analyst
+    )
+
+    report_task = Task(
+        description="""Compile a final report. Rank top 3 affordable neighborhoods. Include: Neighborhood Name, Vibe, Rent Cost, Remaining Budget, and one Local Highlight. Format as clear Markdown.""",
+        expected_output="Markdown formatted relocation guide.",
+        agent=budget_analyst
+    )
+
+    # --- CREW EXECUTION ---
+    relocation_crew = Crew(
+        agents=[city_scout, budget_analyst],
+        tasks=[scout_task, analysis_task, report_task],
+        process=Process.sequential,
+        llm=gpt_4o_mini # Explicit LLM setting
+    )
+
+    return relocation_crew.kickoff()
+
+# --- 7. BUTTON TRIGGER ---
+if st.button("Start Research", type="primary"):
+    with st.spinner('🤖 AI Agents are scouting neighborhoods and crunching numbers...'):
+        try:
+            result = run_crew_analysis(city, work_location, budget, preferences)
+            
+            # Display Results
+            st.success("Research Complete!")
+            st.markdown("### 📋 Your Relocation Plan")
+            st.markdown(result)
+            
+        except Exception as e:
+            st.error("An error occurred during crew execution. This often means a temporary API issue or a failure to parse a search result.")
+            st.exception(e)
+            st.markdown("---")
+            st.warning("Please wait a few minutes (for rate limit cooldown) and try again, or check your API keys.")