added document support

agents.py

@@ -1,6 +1,7 @@
 # agents.py
 from langgraph.prebuilt import create_react_agent
 import os
+import json
 import logging
 
 # Set up logging
@@ -22,6 +23,23 @@ def set_api_key(api_key: str) -> bool:
         logger.error("Invalid API key provided")
         return False
 
+def load_document_data() -> str:
+    """Load document data from JSON file if it exists"""
+    try:
+        if os.path.exists("document_data.json"):
+            with open("document_data.json", "r", encoding="utf-8") as f:
+                doc_data = json.load(f)
+                if doc_data.get("documents"):
+                    # Format the documents for agent consumption
+                    formatted_docs = []
+                    for doc_id, doc_info in doc_data["documents"].items():
+                        formatted_docs.append(f"Document: {doc_info['title']}\nContent: {doc_info['content']}\n---")
+                    return "\n".join(formatted_docs)
+        return ""
+    except Exception as e:
+        logger.error(f"Error loading document data: {e}")
+        return ""
+
 def create_agent_response(prompt: str, user_input: str, model: str = "anthropic:claude-3-haiku-20240307") -> str:
     """Generic function to create and invoke any agent"""
     global _api_key
@@ -73,9 +91,13 @@ def create_agent_response(prompt: str, user_input: str, model: str = "anthropic:
 
 # ==================== CORE AGENT FUNCTIONS ====================
 
-def judge_agent(student_arg: str, opponent_arg: str = "", documents: str = "") -> str:
+def judge_agent(student_arg: str, opponent_arg: str = "") -> str:
     """Judge agent for evaluating legal arguments"""
     logger.info("Judge agent called")
+    
+    # Load document data
+    doc_data = load_document_data()
+    
     prompt = """You are an experienced federal judge presiding over a courtroom.
     
 Your role is to:
@@ -84,17 +106,22 @@ Your role is to:
 - Point out strengths and weaknesses in arguments
 - Ensure proper legal procedure is followed
 - Give guidance on how to strengthen legal positions
+- Reference case documents when relevant to support your analysis
 
 Be fair, professional, and educational in your feedback.
 ALWAYS respond in this exact format: Judge: [your response]
 Keep responses 2-4 sentences, focusing on legal analysis."""
 
-    user_input = f"Student Argument: {student_arg}\nOpponent Argument: {opponent_arg}\nCase Documents: {documents}"
+    user_input = f"Student Argument: {student_arg}\nOpponent Argument: {opponent_arg}\nCase Documents: {doc_data}"
     return create_agent_response(prompt, user_input)
 
 def opponent_agent(student_arg: str, case_context: str = "") -> str:
     """Opponent agent for counter-arguments"""
     logger.info("Opponent agent called")
+    
+    # Load document data
+    doc_data = load_document_data()
+    
     prompt = """You are a skilled attorney arguing the opposing side in this case.
     
 Your role is to:
@@ -103,17 +130,22 @@ Your role is to:
 - Question the student's reasoning and evidence
 - Provide compelling opposition that helps the student improve
 - Stay focused on the legal issues at hand
+- Reference case documents when they support your opposing arguments
 
 Be persuasive, professional, and challenging but fair.
 ALWAYS respond in this exact format: Opponent: [your response]
 Keep responses 2-4 sentences, focused on strong legal counter-arguments."""
 
-    user_input = f"Student's Argument: {student_arg}\nCase Context: {case_context}"
+    user_input = f"Student's Argument: {student_arg}\nCase Context: {case_context}\n\nCase Documents:\n{doc_data}"
     return create_agent_response(prompt, user_input)
 
 def narrator_agent(context: str, student_arg: str = "", opponent_arg: str = "") -> str:
     """Enhanced narrator agent that handles courtroom atmosphere AND side character arguments"""
     logger.info("Narrator agent called")
+    
+    # Load document data
+    doc_data = load_document_data()
+    
     prompt = """You are a courtroom narrator who describes the scene and occasionally voices side characters.
     
 Your roles include:
@@ -122,6 +154,7 @@ Your roles include:
 - Occasionally speaking as side characters (bailiff, court clerk, witnesses, gallery reactions)
 - Setting the dramatic tone and maintaining engagement
 - Providing procedural context when needed
+- Referencing case documents when they add to the narrative
 
 You can voice side characters like:
 - Bailiff: "Order in the court!" or procedural announcements
@@ -134,7 +167,7 @@ When voicing side characters, format as: "Narrator: [description] - Character: '
 Otherwise format as: "Narrator: [your narration]"
 Keep responses 2-4 sentences, atmospheric and engaging."""
 
-    user_input = f"Context: {context}\nStudent Argument: {student_arg}\nOpponent Argument: {opponent_arg}"
+    user_input = f"Context: {context}\nStudent Argument: {student_arg}\nOpponent Argument: {opponent_arg}\n\nCase Documents:\n{doc_data}"
     return create_agent_response(prompt, user_input)
 
 # ==================== AGENT REGISTRY ====================

app.py

@@ -1,3 +1,4 @@
+import json
 import gradio as gr
 import time
 import logging
@@ -5,7 +6,8 @@ from typing import Dict, List, Tuple, Optional
 from agents import judge_agent, opponent_agent, narrator_agent, set_api_key
 from dotenv import load_dotenv
 import os
-
+from LawyersTrainerAgenticSystem.files_extraction import (
+    extract_text_from_pdf_bytes,extract_text_from_docx_bytes,extract_text_from_pptx_bytes, extract_text_from_txt_bytes)
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -70,11 +72,11 @@ class LawTrainingSystem:
 system = LawTrainingSystem()
 
 # ==================== AGENT RESPONSE HANDLERS ====================
-def generate_judge_response(student_message: str, opponent_message: str = "", documents: str = "") -> str:
+def generate_judge_response(student_message: str, opponent_message: str = "") -> str:
     """Generate Judge AI response"""
     try:
         logger.info("Generating judge response")
-        return judge_agent(student_message, opponent_message, documents)
+        return judge_agent(student_message, opponent_message)
     except Exception as e:
         logger.error(f"Error in judge response: {e}", exc_info=True)
         return f"Judge: I'm having trouble processing your argument. Error: {str(e)}"
@@ -203,15 +205,52 @@ def upload_case_documents(files) -> str:
     try:
         if not files:
             return "No documents uploaded"
-        
-        # TODO: Process uploaded documents with document parsing
-        file_names = [f.name for f in files]
-        logger.info(f"Uploaded {len(files)} documents: {file_names}")
-        return f"Uploaded {len(files)} documents: {', '.join(file_names)}"
-        
+
+        all_extracted_text = []
+        document_data_for_agent = {"documents": {}}
+
+        for idx, file_path in enumerate(files):
+            # Read bytes from the file path
+            with open(file_path, "rb") as f:
+                file_bytes = f.read()
+
+            # Determine extension
+            file_extension = os.path.splitext(file_path)[1].lstrip(".").lower()
+
+            # Extract text based on file type
+            if file_extension == "pdf":
+                text = extract_text_from_pdf_bytes(file_bytes)
+            elif file_extension == "docx":
+                text = extract_text_from_docx_bytes(file_bytes)
+            elif file_extension == "pptx":
+                text = extract_text_from_pptx_bytes(file_bytes)
+            elif file_extension == "txt":
+                text = extract_text_from_txt_bytes(file_bytes)
+            else:
+                text = ""
+
+            # Collect results
+            if text:
+                all_extracted_text.append(text)
+                document_data_for_agent["documents"][f"doc_{idx+1}"] = {
+                    "title": os.path.basename(file_path),
+                    "content": text
+                }
+
+        # Save JSON only once, after processing
+        if document_data_for_agent["documents"]:
+            json_filename = "document_data.json"
+            with open(json_filename, "w", encoding="utf-8") as json_file:
+                json.dump(document_data_for_agent, json_file, indent=4, ensure_ascii=False)
+
+        # Log and return summary
+        uploaded_names = [os.path.basename(p) for p in files]
+        logger.info(f"Uploaded {len(files)} documents: {', '.join(uploaded_names)}")
+        return f"Uploaded {len(files)} documents: {', '.join(uploaded_names)}"
+
     except Exception as e:
-        logger.error(f"Error uploading documents: {e}", exc_info=True)
-        return f"Error uploading documents: {str(e)}"
+        logger.error("Error uploading documents", exc_info=True)
+        return f"Error uploading documents: {e}"
 
 # ==================== GRADIO INTERFACE ====================
 def create_law_training_interface():
@@ -275,6 +314,7 @@ def create_law_training_interface():
                     label="Upload Case Documents",
                     file_count="multiple",
                     file_types=[".pdf", ".txt", ".docx"],
+                    type="filepath",   
                     elem_classes="upload-section"
                 )
                 upload_status = gr.Textbox(