Update app.py

app.py

@@ -8,6 +8,36 @@ from phi.embedder.openai import OpenAIEmbedder
 import tempfile
 import os
 
+#initializing the session state variables
+def init_session_state():
+    """Initialize session state variables"""
+    if 'openai_api_key' not in st.session_state:
+        st.session_state.openai_api_key = None
+    if 'qdrant_api_key' not in st.session_state:
+        st.session_state.qdrant_api_key = None
+    if 'qdrant_url' not in st.session_state:
+        st.session_state.qdrant_url = None
+    if 'vector_db' not in st.session_state:
+        st.session_state.vector_db = None
+    if 'legal_team' not in st.session_state:
+        st.session_state.legal_team = None
+    if 'knowledge_base' not in st.session_state:
+        st.session_state.knowledge_base = None
+
+def init_qdrant():
+    """Initialize Qdrant vector database"""
+    if not st.session_state.qdrant_api_key:
+        raise ValueError("Qdrant API key not provided")
+    if not st.session_state.qdrant_url:import streamlit as st
+from phi.agent import Agent
+from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
+from phi.vectordb.qdrant import Qdrant
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.model.openai import OpenAIChat
+from phi.embedder.openai import OpenAIEmbedder
+import tempfile
+import os
+
 #initializing the session state variables
 def init_session_state():
     """Initialize session state variables"""
@@ -32,7 +62,13 @@ def init_qdrant():
         raise ValueError("Qdrant URL not provided")
         
     return Qdrant(          
-        collection="Akshay",
+        collection="legal_knowledge",
+        url=st.session_state.qdrant_url,
+    main() 
+        raise ValueError("Qdrant URL not provided")
+        
+    return Qdrant(          
+        collection="legal_knowledge",
         url=st.session_state.qdrant_url,
         api_key=st.session_state.qdrant_api_key,
         https=True,
@@ -63,21 +99,7 @@ def process_document(uploaded_file, vector_db: Qdrant):
             # Creating knowledge base with explicit Qdrant configuration
             knowledge_base = PDFKnowledgeBase(
                 path=temp_dir, 
-                vector_db=vector_db, 
-                reader=PDFReader(chunk=True),
-                embedder=embedder,
-                recreate_vector_db=True  
-            )
-            knowledge_base.load()     
-            return knowledge_base      
-        except Exception as e:
-            raise Exception(f"Error processing document: {str(e)}")
-
-def main():
-    st.set_page_config(page_title="Legal Document Analyzer", layout="wide")
-    init_session_state()
-
-    st.title("AI Legal Agent Team 👨‍⚖️")import streamlit as st
+                vector_db=vector_db, import streamlit as st
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
 from phi.vectordb.qdrant import Qdrant
@@ -114,6 +136,20 @@ def init_qdrant():
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
     main() 
+                reader=PDFReader(chunk=True),
+                embedder=embedder,
+                recreate_vector_db=True  
+            )
+            knowledge_base.load()     
+            return knowledge_base      
+        except Exception as e:
+            raise Exception(f"Error processing document: {str(e)}")
+
+def main():
+    st.set_page_config(page_title="Legal Document Analyzer", layout="wide")
+    init_session_state()
+
+    st.title("AI Legal Agent Team 👨‍⚖️")
 
     with st.sidebar:
         st.header("🔑 API Configuration")
@@ -138,6 +174,22 @@ from phi.embedder.openai import OpenAIEmbedder
 import tempfile
 import os
 
+#initializing the session state variables
+def init_session_state():
+    """Initialize session state variables"""
+    if 'openai_api_key' not in st.session_state:
+        st.session_state.openai_api_key = None
+    if 'qdrant_api_key' not in st.session_state:
+        st.session_state.qdrant_apiimport streamlit as st
+from phi.agent import Agent
+from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
+from phi.vectordb.qdrant import Qdrant
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.model.openai import OpenAIChat
+from phi.embedder.openai import OpenAIEmbedder
+import tempfile
+import os
+
 #initializing the session state variables
 def init_session_state():
     """Initialize session state variables"""
@@ -164,32 +216,7 @@ def init_qdrant():
     return Qdrant(          
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
-    main() 
-            type="password",
-            value=st.session_state.qdrant_api_key if st.session_state.qdrant_api_key else "",
-            help="Enter your Qdrant API key"
-        )
-        if qdrant_key:
-            st.session_state.qdrant_api_key = qdrant_key
-
-        qdrant_url = st.text_input(
-            "Qdrant URL",import streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
+    main() _key = None
     if 'qdrant_url' not in st.session_state:
         st.session_state.qdrant_url = None
     if 'vector_db' not in st.session_state:
@@ -210,6 +237,15 @@ def init_qdrant():
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
     main() 
+            type="password",
+            value=st.session_state.qdrant_api_key if st.session_state.qdrant_api_key else "",
+            help="Enter your Qdrant API key"
+        )
+        if qdrant_key:
+            st.session_state.qdrant_api_key = qdrant_key
+
+        qdrant_url = st.text_input(
+            "Qdrant URL",
             value=st.session_state.qdrant_url if st.session_state.qdrant_url else "https://f499085c-b4bf-4bda-a9a5-227f62a9ca20.us-west-2-0.aws.cloud.qdrant.io:6333",
             help="Enter your Qdrant instance URL"
         )
@@ -217,7 +253,13 @@ def init_qdrant():
             st.session_state.qdrant_url = qdrant_url
 
         if all([st.session_state.qdrant_api_key, st.session_state.qdrant_url]):
-            try:import streamlit as st
+            try:
+                if not st.session_state.vector_db:
+                    st.session_state.vector_db = init_qdrant()
+                    st.success("Successfully connected to Qdrant!")
+            except Exception as e:
+                st.error(f"Failed to connect to Qdrant: {str(e)}")
+import streamlit as st
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
 from phi.vectordb.qdrant import Qdrant
@@ -254,12 +296,6 @@ def init_qdrant():
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
     main() 
-                if not st.session_state.vector_db:
-                    st.session_state.vector_db = init_qdrant()
-                    st.success("Successfully connected to Qdrant!")
-            except Exception as e:
-                st.error(f"Failed to connect to Qdrant: {str(e)}")
-
         st.divider()
 
         if all([st.session_state.openai_api_key, st.session_state.vector_db]):
@@ -279,43 +315,7 @@ def init_qdrant():
                             model=OpenAIChat(model="gpt-4o"),
                             tools=[DuckDuckGo()],
                             knowledge=st.session_state.knowledge_base,
-                            search_knoimport streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
-    if 'qdrant_url' not in st.session_state:
-        st.session_state.qdrant_url = None
-    if 'vector_db' not in st.session_state:
-        st.session_state.vector_db = None
-    if 'legal_team' not in st.session_state:
-        st.session_state.legal_team = None
-    if 'knowledge_base' not in st.session_state:
-        st.session_state.knowledge_base = None
-
-def init_qdrant():
-    """Initialize Qdrant vector database"""
-    if not st.session_state.qdrant_api_key:
-        raise ValueError("Qdrant API key not provided")
-    if not st.session_state.qdrant_url:
-        raise ValueError("Qdrant URL not provided")
-        
-    return Qdrant(          
-        collection="legal_knowledge",
-        url=st.session_state.qdrant_url,
-    main() wledge=True,
+                            search_knowledge=True,
                             instructions=[
                                 "Find and cite relevant legal cases and precedents",
                                 "Provide detailed research summaries with sources",
@@ -333,43 +333,7 @@ def init_qdrant():
                             knowledge=knowledge_base,
                             search_knowledge=True,
                             instructions=[
-                                "Reimport streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
-    if 'qdrant_url' not in st.session_state:
-        st.session_state.qdrant_url = None
-    if 'vector_db' not in st.session_state:
-        st.session_state.vector_db = None
-    if 'legal_team' not in st.session_state:
-        st.session_state.legal_team = None
-    if 'knowledge_base' not in st.session_state:
-        st.session_state.knowledge_base = None
-
-def init_qdrant():
-    """Initialize Qdrant vector database"""
-    if not st.session_state.qdrant_api_key:
-        raise ValueError("Qdrant API key not provided")
-    if not st.session_state.qdrant_url:
-        raise ValueError("Qdrant URL not provided")
-        
-    return Qdrant(          
-        collection="legal_knowledge",
-        url=st.session_state.qdrant_url,
-    main() view contracts thoroughly",
+                                "Review contracts thoroughly",
                                 "Identify key terms and potential issues",
                                 "Reference specific clauses from the document"
                             ],
@@ -388,7 +352,45 @@ def init_qdrant():
                                 "Consider both risks and opportunities"
                             ],
                             markdown=True
-                        )import streamlit as st
+                        )
+
+                        # Legal Agent Team
+                        st.session_state.legal_team = Agent(
+                            name="Legal Team Lead",
+                            role="Legal team coordinator",
+                            model=OpenAIChat(model="gpt-4o"),
+                            team=[legal_researcher, contract_analyst, legal_strategist],
+                            knowledge=st.session_state.knowledge_base,
+                            search_knowledge=True,
+                            instructions=[
+                                "Coordinate analysis between team members",
+                                "Provide comprehensive responses",
+                                "Ensure all recommendations are properly sourced",
+                                "Reference specific parts of the uploaded document",
+                                "Always search the knowledge base before delegating tasks"
+                            ],
+                            show_tool_calls=True,
+                            markdown=True
+                        )
+                        
+                        st.success("✅ Document processed and team initialized!")
+                            
+                    except Exception as e:
+                        st.error(f"Error processing document: {str(e)}")
+
+            st.divider()
+            st.header("🔍 Analysis Options")
+            analysis_type = st.selectbox(
+                "Select Analysis Type",
+                [
+                    "Contract Review",
+                    "Legal Research",
+                    "Risk Assessment",
+                    "Compliance Check",
+                    "Custom Query"
+                ]
+            )
+        else:import streamlit as st
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
 from phi.vectordb.qdrant import Qdrant
@@ -425,29 +427,15 @@ def init_qdrant():
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
     main() 
+            st.warning("Please configure all API credentials to proceed")
 
-                        # Legal Agent Team
-                        st.session_state.legal_team = Agent(
-                            name="Legal Team Lead",
-                            role="Legal team coordinator",
-                            model=OpenAIChat(model="gpt-4o"),
-                            team=[legal_researcher, contract_analyst, legal_strategist],
-                            knowledge=st.session_state.knowledge_base,
-                            search_knowledge=True,
-                            instructions=[
-                                "Coordinate analysis between team members",
-                                "Provide comprehensive responses",
-                                "Ensure all recommendations are properly sourced",
-                                "Reference specific parts of the uploaded document",
-                                "Always search the knowledge base before delegating tasks"
-                            ],
-                            show_tool_calls=True,
-                            markdown=True
-                        )
-                        
-                        st.success("✅ Document processed and team initialized!")
-                            
-                    except Exceptioimport streamlit as st
+    # Main content area
+    if not all([st.session_state.openai_api_key, st.session_state.vector_db]):
+        st.info("👈 Please configure your API credentials in the sidebar to begin")
+    elif not uploaded_file:
+        st.info("👈 Please upload a legal document to begin analysis")
+    elif st.session_state.legal_team:
+        # Create a dictionary for import streamlit as st
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
 from phi.vectordb.qdrant import Qdrant
@@ -483,31 +471,7 @@ def init_qdrant():
     return Qdrant(          
         collection="legal_knowledge",
         url=st.session_state.qdrant_url,
-    main() n as e:
-                        st.error(f"Error processing document: {str(e)}")
-
-            st.divider()
-            st.header("🔍 Analysis Options")
-            analysis_type = st.selectbox(
-                "Select Analysis Type",
-                [
-                    "Contract Review",
-                    "Legal Research",
-                    "Risk Assessment",
-                    "Compliance Check",
-                    "Custom Query"
-                ]
-            )
-        else:
-            st.warning("Please configure all API credentials to proceed")
-
-    # Main content area
-    if not all([st.session_state.openai_api_key, st.session_state.vector_db]):
-        st.info("👈 Please configure your API credentials in the sidebar to begin")
-    elif not uploaded_file:
-        st.info("👈 Please upload a legal document to begin analysis")
-    elif st.session_state.legal_team:
-        # Create a dictionary for analysis type icons
+    main() analysis type icons
         analysis_icons = {
             "Contract Review": "📑",
             "Legal Research": "🔍",
@@ -542,43 +506,7 @@ def init_qdrant():
             },
             "Custom Query": {
                 "query": None,
-                "agents": ["Legal Rimport streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
-    if 'qdrant_url' not in st.session_state:
-        st.session_state.qdrant_url = None
-    if 'vector_db' not in st.session_state:
-        st.session_state.vector_db = None
-    if 'legal_team' not in st.session_state:
-        st.session_state.legal_team = None
-    if 'knowledge_base' not in st.session_state:
-        st.session_state.knowledge_base = None
-
-def init_qdrant():
-    """Initialize Qdrant vector database"""
-    if not st.session_state.qdrant_api_key:
-        raise ValueError("Qdrant API key not provided")
-    if not st.session_state.qdrant_url:
-        raise ValueError("Qdrant URL not provided")
-        
-    return Qdrant(          
-        collection="legal_knowledge",
-        url=st.session_state.qdrant_url,
-    main() esearcher", "Contract Analyst", "Legal Strategist"],
+                "agents": ["Legal Researcher", "Contract Analyst", "Legal Strategist"],
                 "description": "Custom analysis using all available agents"
             }
         }
@@ -600,43 +528,7 @@ def init_qdrant():
             if analysis_type == "Custom Query" and not user_query:
                 st.warning("Please enter a query")
             else:
-                with st.spinner("Aimport streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
-    if 'qdrant_url' not in st.session_state:
-        st.session_state.qdrant_url = None
-    if 'vector_db' not in st.session_state:
-        st.session_state.vector_db = None
-    if 'legal_team' not in st.session_state:
-        st.session_state.legal_team = None
-    if 'knowledge_base' not in st.session_state:
-        st.session_state.knowledge_base = None
-
-def init_qdrant():
-    """Initialize Qdrant vector database"""
-    if not st.session_state.qdrant_api_key:
-        raise ValueError("Qdrant API key not provided")
-    if not st.session_state.qdrant_url:
-        raise ValueError("Qdrant URL not provided")
-        
-    return Qdrant(          
-        collection="legal_knowledge",
-        url=st.session_state.qdrant_url,
-    main() nalyzing document..."):
+                with st.spinner("Analyzing document..."):
                     try:
                         # Ensure OpenAI API key is set
                         os.environ['OPENAI_API_KEY'] = st.session_state.openai_api_key
@@ -644,43 +536,7 @@ def init_qdrant():
                         # Combine predefined and user queries
                         if analysis_type != "Custom Query":
                             combined_query = f"""
-                            Using timport streamlit as st
-from phi.agent import Agent
-from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.qdrant import Qdrant
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.model.openai import OpenAIChat
-from phi.embedder.openai import OpenAIEmbedder
-import tempfile
-import os
-
-#initializing the session state variables
-def init_session_state():
-    """Initialize session state variables"""
-    if 'openai_api_key' not in st.session_state:
-        st.session_state.openai_api_key = None
-    if 'qdrant_api_key' not in st.session_state:
-        st.session_state.qdrant_api_key = None
-    if 'qdrant_url' not in st.session_state:
-        st.session_state.qdrant_url = None
-    if 'vector_db' not in st.session_state:
-        st.session_state.vector_db = None
-    if 'legal_team' not in st.session_state:
-        st.session_state.legal_team = None
-    if 'knowledge_base' not in st.session_state:
-        st.session_state.knowledge_base = None
-
-def init_qdrant():
-    """Initialize Qdrant vector database"""
-    if not st.session_state.qdrant_api_key:
-        raise ValueError("Qdrant API key not provided")
-    if not st.session_state.qdrant_url:
-        raise ValueError("Qdrant URL not provided")
-        
-    return Qdrant(          
-        collection="legal_knowledge",
-        url=st.session_state.qdrant_url,
-    main() he uploaded document as reference:
+                            Using the uploaded document as reference:
                             
                             Primary Analysis Task: {analysis_configs[analysis_type]['query']}
                             Focus Areas: {', '.join(analysis_configs[analysis_type]['agents'])}