Update app.py

app.py

@@ -8,7 +8,7 @@ import json
 from pydantic import BaseModel, Field
 from typing import List, Optional, Dict
 from enum import Enum
-from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.llms import GoogleGenerativeAI
 
 # Page configuration
 st.set_page_config(
@@ -181,7 +181,7 @@ class MaterialCollection(BaseModel):
 class QuizQuestion(BaseModel):
     question: str
     options: List[str]
-    correct_answer: int  # Index of correct option (0-based)
+    correct_answer: int
     explanation: str
 
 class Quiz(BaseModel):
@@ -200,18 +200,48 @@ class Projects(BaseModel):
 
 # Initialize LLM and search tool
 def initialize_services():
-    # Initialize Gemini model
-    gemini_llm = ChatGoogleGenerativeAI(
-        model="gemini-2.0-flash-lite",
-        google_api_key=os.getenv("GOOGLE_API_KEY"),
-        temperature=0.7,
-        convert_system_message_to_human=True
-    )
+    # Get API keys
+    google_api_key = os.getenv("GOOGLE_API_KEY")
+    serper_api_key = os.getenv("SERPER_API_KEY")
+    
+    if not google_api_key:
+        st.error("Google API Key not found in environment variables. Please set the GOOGLE_API_KEY.")
+        st.stop()
     
-    # Initialize search tool
-    search_tool = SerperDevTool(serper_api_key=os.getenv("SERPER_API_KEY"))
+    if not serper_api_key:
+        st.warning("Serper API Key not found. Search functionality may be limited.")
+    
+    try:
+        # Initialize Gemini model
+        from langchain_google_genai import ChatGoogleGenerativeAI
+        
+        gemini_llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash-lite",
+            google_api_key=google_api_key,
+            temperature=0.7,
+            convert_system_message_to_human=True
+        )
+        
+        # Test the model connection
+        _ = gemini_llm.invoke("Test connection")
+        
+        # Initialize search tool if API key is available
+        search_tool = None
+        if serper_api_key:
+            search_tool = SerperDevTool(serper_api_key=serper_api_key)
+        
+        return gemini_llm, search_tool
     
-    return gemini_llm, search_tool
+    except ImportError:
+        st.error("Required packages not installed. Please install langchain-google-genai.")
+        st.stop()
+    except Exception as e:
+        st.error(f"Error initializing Gemini model: {str(e)}")
+        # Fallback to a default model from CrewAI
+        from crewai import LLM
+        default_llm = LLM(name="openai", model="gpt-3.5-turbo")
+        st.warning("Falling back to default model (OpenAI GPT-3.5).")
+        return default_llm, None
 
 def create_agents_and_tasks(topics, expertise_level, llm):
     # Create agents
@@ -343,7 +373,7 @@ def display_learning_materials(materials):
                     </div>
                     """, unsafe_allow_html=True)
     except json.JSONDecodeError as e:
-        st.error("Error parsing learning materials")
+        st.error(f"Error parsing learning materials: {e}")
         st.write(materials)  # Fallback to display raw output
 
 def display_quiz(quiz):
@@ -381,7 +411,7 @@ def display_quiz(quiz):
                     st.write(question['explanation'])
                     
     except json.JSONDecodeError as e:
-        st.error("Error parsing quiz")
+        st.error(f"Error parsing quiz: {e}")
         st.write(quiz)  # Fallback to display raw output
 
 def display_projects(projects):
@@ -502,16 +532,39 @@ def main():
             help="Choose your current level of expertise"
         )
         
+        # Add model selection dropdown
+        model_options = [
+            "gemini-2.0-flash-lite",
+            "gemini-2.0-pro",
+            "gpt-3.5-turbo"  # Fallback option
+        ]
+        selected_model = st.selectbox(
+            "AI Model",
+            options=model_options,
+            index=0,
+            help="Select the AI model to use"
+        )
+        
+        # Store the selected model in session state
+        if 'selected_model' not in st.session_state:
+            st.session_state.selected_model = selected_model
+        elif st.session_state.selected_model != selected_model:
+            st.session_state.selected_model = selected_model
+        
         generate_btn = st.button("🚀 Generate Learning Path", use_container_width=True, type="primary")
         
         st.markdown("---")
         st.markdown("""
         <div style="font-size: 0.8rem; color: #666;">
-            Powered by CrewAI and Gemini 2.0<br>
+            Powered by CrewAI and Google Gemini<br>
             © 2025 Learning Path Generator
         </div>
         """, unsafe_allow_html=True)
     
+    # Check for API keys
+    if not os.getenv("GOOGLE_API_KEY") and st.session_state.selected_model.startswith("gemini"):
+        st.warning("⚠️ Google API Key not found. Please add it to your environment variables.", icon="⚠️")
+    
     # Main content area
     if not st.session_state.generation_complete and not generate_btn:
         render_welcome_screen()
@@ -530,44 +583,55 @@ def main():
             <div class='progress-bar'>
                 <div class='progress'></div>
             </div>
-            <p>Our AI experts are crafting the perfect resources for you using Google's Gemini 2.0.<br>This may take a minute or two.</p>
+            <p>Our AI experts are crafting the perfect resources for you.<br>This may take a minute or two.</p>
         </div>
         """, unsafe_allow_html=True)
         
         try:
-            # Initialize Gemini LLM and tools
-            gemini_llm, search_tool = initialize_services()
-            
-            # Create agents and tasks with Gemini
-            agents, tasks = create_agents_and_tasks(topics, expertise_level, gemini_llm)
-            
-            # Create and run crew
-            crew = Crew(
-                agents=agents,
-                tasks=tasks,
-                process=Process.sequential
-            )
-            
-            result = crew.kickoff({"topics": topic_list, "expertise_level": ExpertiseLevel(expertise_level)})
-            
-            # Store results in session state
-            st.session_state.results = {
-                "materials": tasks[0].output.raw,
-                "quiz": tasks[1].output.raw,
-                "projects": result.pydantic
-            }
-            st.session_state.generation_complete = True
-            
-            # Rerun to display results
-            st.rerun()
-            
+            # Try-except for better error handling
+            try:
+                # Initialize Gemini LLM and tools
+                llm, search_tool = initialize_services()
+                
+                # Create agents and tasks with Gemini
+                agents, tasks = create_agents_and_tasks(topics, expertise_level, llm)
+                
+                # Create and run crew
+                crew = Crew(
+                    agents=agents,
+                    tasks=tasks,
+                    process=Process.sequential
+                )
+                
+                result = crew.kickoff({"topics": topic_list, "expertise_level": ExpertiseLevel(expertise_level)})
+                
+                # Store results in session state
+                st.session_state.results = {
+                    "materials": tasks[0].output.raw,
+                    "quiz": tasks[1].output.raw,
+                    "projects": result.pydantic
+                }
+                st.session_state.generation_complete = True
+                
+                # Rerun to display results
+                st.rerun()
+                
+            except ImportError as ie:
+                st.error(f"Missing package: {str(ie)}")
+                st.info("Try installing required packages with: `pip install langchain-google-genai crewai pydantic`")
+                
+            except AttributeError as ae:
+                st.error(f"Configuration issue: {str(ae)}")
+                st.info("This might be a compatibility issue between CrewAI and the LLM integration.")
+                
+            except ValueError as ve:
+                st.error(f"Value error: {str(ve)}")
+                if "api_key" in str(ve).lower():
+                    st.info("There seems to be an issue with your API key. Please check if it's correctly set in the .env file.")
+                    
         except Exception as e:
             st.error(f"🚨 An error occurred: {str(e)}")
-            # Add more specific error handling for Gemini API issues
-            if "google_api_key" in str(e).lower():
-                st.error("There seems to be an issue with the Google API key. Please check your environment variables.")
-            elif "quota" in str(e).lower():
-                st.error("API quota exceeded. Please try again later.")
+            st.info("If the issue persists, try switching to a different AI model in the sidebar.")
     
     # Display results if generation is complete
     if st.session_state.generation_complete and st.session_state.results: