Update app.py

app.py

@@ -1,19 +1,14 @@
 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
+from agno.agent import Agent
+from agno.knowledge.pdf import PDFKnowledgeBase, PDFReader
+from agno.vectordb.qdrant import Qdrant
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.models.openai import OpenAIChat
+from agno.embedder.openai import OpenAIEmbedder
 import tempfile
 import os
-import logging
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Initialize the session state variables
+#initializing the session state variables
 def init_session_state():
     """Initialize session state variables"""
     if 'openai_api_key' not in st.session_state:
@@ -35,36 +30,31 @@ def init_qdrant():
         raise ValueError("Qdrant API key not provided")
     if not st.session_state.qdrant_url:
         raise ValueError("Qdrant URL not provided")
-    
-    try:
-        vector_db = Qdrant(
-            collection="legal_knowledge",
-            url=st.session_state.qdrant_url,
-            api_key=st.session_state.qdrant_api_key,
-            https=True,
-            timeout=None,
-            distance="cosine"
-        )
-        # Test connection
-        vector_db.client.get_collections()
-        return vector_db
-    except Exception as e:
-        logger.error(f"Failed to initialize Qdrant: {str(e)}")
-        raise
+        
+    return Qdrant(          
+        collection="legal_knowledge",
+        url=st.session_state.qdrant_url,
+        api_key=st.session_state.qdrant_api_key,
+        https=True,
+        timeout=None,
+        distance="cosine"
+    )
 
 def process_document(uploaded_file, vector_db: Qdrant):
     """Process document, create embeddings and store in Qdrant vector database"""
     if not st.session_state.openai_api_key:
         raise ValueError("OpenAI API key not provided")
-    
+        
     os.environ['OPENAI_API_KEY'] = st.session_state.openai_api_key
     
     with tempfile.TemporaryDirectory() as temp_dir:
+      
         temp_file_path = os.path.join(temp_dir, uploaded_file.name)
         with open(temp_file_path, "wb") as f:
             f.write(uploaded_file.getbuffer())
 
         try:
+       
             embedder = OpenAIEmbedder(
                 model="text-embedding-3-small",
                 api_key=st.session_state.openai_api_key
@@ -81,7 +71,6 @@ def process_document(uploaded_file, vector_db: Qdrant):
             knowledge_base.load()     
             return knowledge_base      
         except Exception as e:
-            logger.error(f"Error processing document: {str(e)}")
             raise Exception(f"Error processing document: {str(e)}")
 
 def main():
@@ -144,7 +133,7 @@ def main():
                             name="Legal Researcher",
                             role="Legal research specialist",
                             model=OpenAIChat(model="gpt-4o"),
-                            tools=[DuckDuckGo()],
+                            tools=[DuckDuckGoTools()],
                             knowledge=st.session_state.knowledge_base,
                             search_knowledge=True,
                             instructions=[
@@ -271,8 +260,7 @@ def main():
         }
 
         st.info(f"📋 {analysis_configs[analysis_type]['description']}")
-        st.write(f"🤖 Active Legal AI Agents: {', '.join(analysis_configs[analysis_type]['agents'])}")  
-
+        st.write(f"🤖 Active Legal AI Agents: {', '.join(analysis_configs[analysis_type]['agents'])}")  #dictionary!!
 
         # Replace the existing user_query section with this:
         if analysis_type == "Custom Query":