app.py

# %% [markdown]
# ## Academic Research Assistant

# %% [markdown]
# ## Import

# %%
from docling.document_converter import DocumentConverter
import tqdm as notebook_tqdm
from pydantic import BaseModel, Field
import os
from typing import Optional, Any, Literal, Dict, List, Tuple, Type, Annotated
from operator import add
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.types import Command
from langchain_openai import ChatOpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import PydanticOutputParser  # Add this import
# from langfuse.callback import CallbackHandler
import gradio as gr
import contextlib
from io import StringIO
import docx
from pathlib import Path
import re
from typing import Union
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Use environment variables for API keys
USE_GOOGLE = False
API_KEY = os.environ.get("NEBIUS_KEY")
MODEL_NAME = None
ENDPOINT_URL = None

# Try these models one by one to see which ones actually exist
NEBIUS_MODELS = [
    "meta-llama/Llama-2-7b-chat-hf",  # Try this first
    "mistralai/Mistral-7B-Instruct-v0.2",  # Then this
    "microsoft/DialoGPT-medium",  # Then this
    "openai/gpt-3.5-turbo",  # Or this
    "Qwen2.5-Coder-7B",  # Keep the original as fallback
    "QwQ-32B"
]

def list_nebius_models():
    """List all available models from Nebius API."""
    try:
        import requests
        
        headers = {
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        }
        
        # Try the models endpoint
        response = requests.get(
            f"{ENDPOINT_URL}models",
            headers=headers,
            timeout=10
        )
        
        if response.status_code == 200:
            models = response.json()
            print("Available models:")
            for model in models.get('data', []):
                print(f"  - {model.get('id', 'Unknown')}")
            return [model.get('id') for model in models.get('data', [])]
        else:
            print(f"Failed to fetch models: {response.status_code}")
            print(f"Response: {response.text}")
            return []
            
    except Exception as e:
        print(f"Error fetching models: {str(e)}")
        return []

def test_available_models():
    """Test which models are actually available."""
    
    # First try to get the actual model list
    available_models = list_nebius_models()
    
    if available_models:
        print(f"Found {len(available_models)} models from API")
        test_models = available_models[:6]  # Test first 6 models
    else:
        # Fallback to common model names that might work
        test_models = [
            "gpt-3.5-turbo",
            "gpt-4",
            "claude-3-haiku",
            "llama-2-7b-chat",
            "mistral-7b-instruct",
            "qwen-7b-chat"
        ]
    
    for model in test_models:
        try:
            print(f"Testing model: {model}")
            global MODEL_NAME
            MODEL_NAME = model
            
            # Simple test call
            llm = ChatOpenAI(
                model=model,
                api_key=API_KEY,
                base_url=ENDPOINT_URL,
                max_completion_tokens=50,
                timeout=10,
                temperature=0
            )
            
            response = llm.invoke("Hello")
            print(f"✅ {model} works!")
            return model  # Return the first working model
            
        except Exception as e:
            print(f"❌ {model} failed: {str(e)}")
            continue
    
    print("⚠️ No working models found")
    return None

# Call this function when setting up API key
def setup_api_key(nebius_key=None, model_name=None):
    global API_KEY, MODEL_NAME, ENDPOINT_URL, USE_GOOGLE
    
    # First try user-provided key (from UI)
    if nebius_key:
        API_KEY = nebius_key
        ENDPOINT_URL = "https://api.studio.nebius.com/v1/"
        
        # Test which model actually works
        if model_name:
            MODEL_NAME = model_name
        else:
            working_model = test_available_models()
            if working_model:
                MODEL_NAME = working_model
            else:
                print("No working models found")
                return False
                
        print(f"Using user-provided Nebius API key with model: {MODEL_NAME}")
        return True
    
    # Next try environment variable
    if API_KEY:
        ENDPOINT_URL = "https://api.studio.nebius.com/v1/"
        
        if model_name:
            MODEL_NAME = model_name
        else:
            working_model = test_available_models()
            if working_model:
                MODEL_NAME = working_model
            else:
                print("No working models found")
                return False
                
        print(f"Using Nebius API from environment variable with model: {MODEL_NAME}")
        return True
    
    print("No API key found. Please provide a Nebius API key.")
    return False

# Initialize with environment variables if available
setup_api_key()

# %% [markdown]
# ## Structured outputs

# %%
class ResearchSummary(BaseModel):
    key_findings: List[str] = Field(..., description="A list of the most important findings from the research paper.")
    methodology: str = Field(..., description="A brief description of the methodology used in the research.")
    limitations: List[str] = Field(..., description="A list of the limitations of the study as identified by the authors or the agent.")

class FutureScope(BaseModel):
    identified_gaps: List[str] = Field(..., description="List of identified research gaps based on the provided paper(s).")
    suggested_directions: List[str] = Field(..., description="Concrete suggestions for future research directions or next studies.")
    synthesis: str = Field(..., description="A brief synthesis of how these future directions build upon the provided literature.")

class MultiStepPlan(BaseModel):
    reasoning : str = Field("", description="The multi-step reasoning required to break down the user query in a plan.")
    plan : List[Literal["summary_agent", "synthesis_agent", "future_scope_agent", "critique_agent"]] = Field("END", description="The list of agents required to fulfill the user request determined by the Orchestrator.")

class PaperSummary(BaseModel):
    key_findings: List[str] = Field(
        default_factory=lambda: ["No key findings available due to processing error"],
        description="List of key findings from the paper"
    )
    methodology: str = Field(
        default="Methodology not available due to processing error",
        description="Summary of the methodology used in the paper"
    )
    conclusion: str = Field(
        default="Conclusion not available due to processing error", 
        description="Summary of the paper's conclusion"
    )

# %% [markdown]
# ## Agent state

# %%
class AgentDescription(TypedDict):
    "Agent description containing the title, system prompt and description."
    title : str
    description : str
    system_prompt : str

class ResearchAgentState(BaseModel):
    """State for the research agent."""
    research_papers: Annotated[List[Tuple[str, str]], add] = Field(default_factory=list)  # List of (filename, content) tuples
    summary: Annotated[List[Dict], add] = Field(default_factory=list)  # List of paper summaries
    user_query: str = Field(default="")  # Remove annotation - only set once
    phase: str = Field(default="PLAN")  # PLAN, EXECUTE, ANSWER
    plan: List[str] = Field(default_factory=list)  # List of agent names to call in order
    messages: Annotated[List[Tuple[str, str]], add] = Field(default_factory=list)  # List of (agent, message) tuples
    critique: Optional[str] = Field(default=None)  # Optional critique of the analysis
    available_agents: Dict[str, Dict] = Field(default_factory=dict)  # Mapping of agent name to agent description
    final_answer: Optional[str] = Field(default=None)  # Final answer to the user's query
    max_iterations: int = Field(default=1)  # Maximum number of iterations for processing
    synthesis_of_findings: Optional[str] = Field(default=None)  # Remove Annotated - only set once
    future_directions_report: Optional[str] = Field(default=None)  # Remove Annotated - only set once

# %% [markdown]
# ## System prompts

# %%
general_prefix = """
You are part of a collaborative multi-agent system called the *Academic Research Assistant*. 
This system consists of specialized agents working together to analyze, synthesize, and critique academic literature. 
Each agent has a distinct role. You are encouraged to build upon the work of other agents to produce a comprehensive and insightful analysis.
"""

summary_prompt = """
You are a diligent research assistant. Your task is to read the provided research paper and extract the most critical information.

Focus on the following key areas:
1.  **Key Findings:** What were the main results and conclusions of the study? List them as clear, concise bullet points.
2.  **Methodology:** Briefly describe the methodology, including the techniques, dataset, and experiments conducted.
3.  **Limitations:** Identify any limitations of the study that were mentioned by the authors.

Provide your output in a structured format. Do not add any interpretation; stick strictly to the information present in the paper.
"""

synthesis_prompt = """
You are a research analyst specializing in literature reviews. You have been provided with summaries from one or more research papers.

Your task is to synthesize this information into a cohesive narrative.
1.  Identify common themes, findings, and methodologies across the papers.
2.  Highlight any conflicting or divergent results.
3.  Create a single, flowing text that summarizes the current state of research based *only* on the provided information.

Do not introduce outside knowledge. Your synthesis should serve as a high-level overview for someone trying to understand the field as defined by these papers.
"""

future_scope_prompt = """
You are an experienced academic advisor with a knack for identifying promising research avenues. 
Based on the provided research summaries and synthesis, your goal is to propose a clear path for future work.

Follow these steps:
1.  **Identify Research Gaps:** Based on the limitations and findings of the papers, what questions remain unanswered? What are the clear gaps in the current body of knowledge?
2.  **Suggest Future Directions:** Propose 2-3 concrete, actionable research projects that could address these gaps. For each suggestion, briefly explain:
    - The research question.
    - A potential methodology.
    - The expected contribution to the field.
3.  **Write a Concluding Synthesis:** Briefly summarize why these future directions are a logical and important next step in this research area.

Your tone should be formal and academic. The suggestions must be directly inspired by the provided context.
"""

critique_prompt = """
You are a peer reviewer. Your task is to provide constructive feedback on the generated research analysis (synthesis and future scope).

Evaluate the analysis based on the following criteria:
- **Clarity and Cohesion:** Is the synthesis clear, well-structured, and easy to understand?
- **Logical Flow:** Do the suggested future directions logically follow from the identified gaps and the provided literature?
- **Actionability:** Are the future scope suggestions concrete and specific enough to be pursued?
- **Completeness:** Does the analysis seem to have missed any obvious connections or gaps present in the source material?

Provide brief, constructive feedback highlighting points of improvement.
Provide a quality flag:
- **EXCELLENT**: The analysis is clear, logical, and insightful.
- **NEEDS REVISION**: The analysis has flaws in logic, clarity, or completeness that need to be addressed.
"""

# %% [markdown]
# ### Available agents

# %%
summary_agent_description = AgentDescription(
    title="summary_agent",
    description="Summarizes the key findings, methodology, and limitations of a single research paper.",
    system_prompt=general_prefix + summary_prompt
)
synthesis_agent_description = AgentDescription(
    title="synthesis_agent",
    description="Synthesizes information from multiple paper summaries into a cohesive literature review.",
    system_prompt=general_prefix + synthesis_prompt
)
future_scope_agent_description = AgentDescription(
    title="future_scope_agent",
    description="Identifies research gaps and suggests concrete directions for future work based on the literature.",
    system_prompt=general_prefix + future_scope_prompt
)
critique_agent_description = AgentDescription(
    title="critique_agent",
    description="Provides peer-review style feedback on the generated synthesis and future scope analysis.",
    system_prompt=general_prefix + critique_prompt
)

available_agents = {
    "summary_agent": summary_agent_description,
    "synthesis_agent": synthesis_agent_description,
    "future_scope_agent": future_scope_agent_description,
    "critique_agent": critique_agent_description,
}

# %% [markdown]
# ## Utilities

# %% [markdown]
# ### General utilities

# %%
def read_file_content(file: Union[str, Path]) -> str:
    file_path = Path(file)
    suffix = file_path.suffix.lower()

    if suffix == ".txt" or suffix == ".md":
        return file_path.read_text(encoding="utf-8")
    
    elif suffix == ".pdf":
        converter = DocumentConverter()
        result = converter.convert(file_path)
        return result.document.export_to_markdown()

    elif suffix == ".docx":
        return "\n".join(p.text for p in docx.Document(file_path).paragraphs)

    else:
        return ""

# %% [markdown]
# ## LLM call

# %%
def call_llm(system_prompt, user_prompt, response_format=None):
    """Call LLM with system and user prompt, optionally parsing to a specific format"""
    global API_KEY, MODEL_NAME, ENDPOINT_URL
    
    if not API_KEY:
        print("Error: API key is not set")
        # Return a default instance for the response_format class
        if response_format and hasattr(response_format, "__name__"):
            try:
                if response_format.__name__ == "MultiStepPlan":
                    return MultiStepPlan(
                        reasoning="Error occurred: API key not set",
                        plan=["summary_agent", "synthesis_agent", "future_scope_agent"]
                    )
                elif response_format.__name__ == "PaperSummary":
                    return PaperSummary()  # Uses default values from Field definitions
                else:
                    # Generic attempt to create an instance with default values
                    return response_format()
            except Exception as e:
                print(f"Failed to create default instance: {str(e)}")
        return None
        
    try:
        if USE_GOOGLE:
            llm = ChatGoogleGenerativeAI(
                model=MODEL_NAME,
                google_api_key=API_KEY,
                temperature=0
            )
        else:
            llm = ChatOpenAI(
                model=MODEL_NAME,
                api_key=API_KEY,
                base_url=ENDPOINT_URL,
                max_completion_tokens=None,
                timeout=60,
                max_retries=2,
                temperature=0
            )
            
        if response_format is not None:  
            llm = llm.with_structured_output(response_format)

        prompt = ChatPromptTemplate.from_messages([
            ("system", "{system_prompt}"),
            ("user", "{user_prompt}")
        ])

        chain = prompt | llm

        print(f"Calling model: {MODEL_NAME}")
        response = chain.invoke({
            "system_prompt": system_prompt,
            "user_prompt": user_prompt
        })

        return response
        
    except Exception as e:
        print(f"Error in call_llm: {str(e)}")
        if hasattr(e, 'response') and hasattr(e.response, 'json'):
            try:
                error_details = e.response.json()
                print(f"API Error details: {error_details}")
            except:
                pass
                
        # Create a default response based on the response_format class
        if response_format and hasattr(response_format, "__name__"):
            try:
                if response_format.__name__ == "MultiStepPlan":
                    return MultiStepPlan(
                        reasoning="Error occurred while calling the LLM API. Using default plan.",
                        plan=["summary_agent", "synthesis_agent", "future_scope_agent"]
                    )
                elif response_format.__name__ == "PaperSummary":
                    return PaperSummary()  # Uses default values from Field definitions
                else:
                    # Generic attempt to create an instance with default values
                    return response_format()
            except Exception as e:
                print(f"Failed to create default instance: {str(e)}")
        return None
def serialize_messages(messages : List[Tuple[str,str]]) -> str:
    "Returns a formatted message history of previous messages"
    return "\n" +"\n".join(f"**{role}:**\n{content}" for role, content in messages)

def strip_think_blocks(text: str) -> str:
    return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL)

# %% [markdown]
# ### Gradio utilities

# %%
# Handle different result types cleanly
def type_conversion(obj : Any, type):
    "Return the object in a gradio compatible type"
    if isinstance(obj, type):
        result_dict = obj.model_dump()
    elif isinstance(obj, Dict):
        result_dict = obj
    else:
        # Handle possible dataclass or similar object
        try:
            result_dict = ResearchAgentState.model_validate(obj).model_dump()
        except Exception as e:
            print(f"Error converting output of type {type(obj)}")

    return result_dict

# %% [markdown]
# ## Agents

# %% [markdown]
# ### Orchestrator agent
# 
# 

# %%
def orchestrator_agent(state: ResearchAgentState) -> Command:
    """Central orchestration logic to determine the next agent to call."""
    
    if not state.research_papers:
        return Command(
            goto=END, 
            update={"final_answer": "### ❗️ The research assistant needs at least one research paper to begin.\n" \
                   "👈🏽 Please upload one or more research papers in the '📚 Research Materials' tab."}
        )
    
    if state.phase == "PLAN":
        agent_descriptions = "\n".join([
            f"**{agent.get('title')}**: {agent.get('description')}"
            for name, agent in state.available_agents.items()
        ])

        system_prompt = f"""You are an orchestrator for an academic research assistant. Your task is to create a plan to answer the user's query using a team of specialized agents.
        
        **Agents:**
        {agent_descriptions}
        
        Based on the user's query, create a logical sequence of agents to call. For example, to find future scope, you should first summarize the papers, then synthesize them, and then call the future_scope_agent.
        
        **IMPORTANT:** Always include the summary_agent as the first step when working with research papers. Every task requires proper paper summaries before analysis can begin.
        """

        user_prompt = state.user_query

        response = call_llm(system_prompt, user_prompt, MultiStepPlan)
        
        # Handle None response by providing a default plan
        if response is None:
            print("⚠️ Failed to get response from LLM. Using default plan.")
            plan = ["summary_agent", "synthesis_agent", "future_scope_agent"]
            print("="*40)
            print("🤖 DEFAULT ORCHESTRATOR PLAN (LLM call failed)")
            print("="*40)
            print("\n📝 Reasoning: Default plan due to LLM call failure\n")
            print("🔗 Planned Steps:")
            for i, step in enumerate(plan, 1):
                print(f"  {i}. {step}")
            print("="*40)
            print("⚙️ EXECUTE PLAN")
            print("="*40 + "\n")
            
            # Create update dict that only modifies necessary fields
            updates = {
                "plan": plan,
                "phase": "EXECUTE"
            }
            
            # Only add user_query to messages if it's not already there
            if not any(msg[0] == "user_query" for msg in state.messages):
                updates["messages"] = [("user_query", state.user_query)]
            
            return Command(goto=plan[0], update=updates)
        
        # If response exists but plan is empty, use default plan
        try:
            # Enforce summary_agent as the first step if not already included
            if not hasattr(response, 'plan') or not response.plan:
                print("⚠️ Response from LLM did not contain a valid plan. Using default plan.")
                response.plan = ["summary_agent", "synthesis_agent", "future_scope_agent"]
            elif response.plan[0] != "summary_agent":
                print("⚠️ Enforcing summary_agent as first step in the plan")
                response.plan.insert(0, "summary_agent")
            
            print("="*40)
            print("🤖 ORCHESTRATOR PLAN")
            print("="*40)
            print(f"\n📝 Reasoning:\n{getattr(response, 'reasoning', 'No reasoning provided')}\n")
            print("🔗 Planned Steps:")
            for i, step in enumerate(response.plan, 1):
                print(f"  {i}. {step}")
            print("="*40)
            print("⚙️ EXECUTE PLAN")
            print("="*40 + "\n")
            
            # Create update dict that only modifies necessary fields
            updates = {
                "plan": response.plan,
                "phase": "EXECUTE"
            }
            
            # Only add user_query to messages if it's not already there
            if not any(msg[0] == "user_query" for msg in state.messages):
                updates["messages"] = [("user_query", state.user_query)]
            
            return Command(goto=response.plan[0], update=updates)
            
        except Exception as e:
            # Final fallback if response processing fails
            print(f"⚠️ Error processing LLM response: {str(e)}. Using default plan.")
            plan = ["summary_agent", "synthesis_agent", "future_scope_agent"]
            
            # Create update dict that only modifies necessary fields
            updates = {
                "plan": plan,
                "phase": "EXECUTE"
            }
            
            # Only add user_query to messages if it's not already there
            if not any(msg[0] == "user_query" for msg in state.messages):
                updates["messages"] = [("user_query", state.user_query)]
            
            return Command(goto=plan[0], update=updates)
    
    if len(state.plan) == 0 and state.phase == "EXECUTE":
        return Command(
            goto="final_answer_tool",
            update={"phase": "ANSWER"}
        )
        
    if state.phase == "EXECUTE":
        next_agent = state.plan[0]
        remaining_plan = state.plan[1:]
        return Command(
            goto=next_agent,
            update={"plan": remaining_plan}
        )

    if state.phase == "ANSWER":
        return Command(
            goto=END, 
            update={
                "phase": "PLAN",
                "messages": [("orchestrator_agent", f"\n{state.final_answer}")]
            }
        )
        
    return Command(goto=END, update={})

# %% [markdown]
# ### Research Agents

# %%
def summary_agent(state : ResearchAgentState) -> Command:
    """Creates concise, structured summaries of research papers."""
    
    if not state.summary:
        # Initialize empty summaries
        print("The summary agent is processing the papers... 📝")
        research_findings = []
        
        for filename, content in state.research_papers:
            # Create a prompt for each paper
            system_prompt = f"""You are a research summarization expert. Please read the provided research paper content and create a clear, concise, and structured summary.
            Focus on extracting key findings, methodology, and conclusions.
            """
            user_prompt = f"""
            Paper: {filename}
            
            Content:
            {content[:5000]}  # Use first 5000 chars to avoid context limits
            
            Please provide a structured summary with key findings, methodology, and conclusions.
            """
            
            response = call_llm(system_prompt, user_prompt, PaperSummary)
            
            # Check if we got a valid response
            if response is None:
                print(f"⚠️ Failed to summarize paper {filename}. Creating default summary.")
                # Create a default summary
                finding = {
                    "title": filename,
                    "key_findings": ["Error: Could not summarize this paper due to API issues."],
                    "methodology": "Not available due to API error",
                    "conclusion": "Not available due to API error",
                    "source": filename
                }
                research_findings.append(finding)
            else:
                try:
                    # Extract the key findings from the response
                    finding = {
                        "title": filename,
                        "key_findings": response.key_findings if hasattr(response, 'key_findings') else ["No key findings extracted"],
                        "methodology": response.methodology if hasattr(response, 'methodology') else "Not provided",
                        "conclusion": response.conclusion if hasattr(response, 'conclusion') else "Not provided",
                        "source": filename
                    }
                    research_findings.append(finding)
                except Exception as e:
                    print(f"⚠️ Error processing summary for {filename}: {str(e)}")
                    finding = {
                        "title": filename,
                        "key_findings": ["Error processing paper summary."],
                        "methodology": "Error in processing",
                        "conclusion": "Error in processing",
                        "source": filename
                    }
                    research_findings.append(finding)
        
        print("Paper summaries complete.")
        
        # Add the summaries to the message history
        formatted_summaries = []
        for paper in research_findings:
            findings_text = "\n".join([f"- {finding}" for finding in paper['key_findings']])
            formatted_summary = f"""
        ## {paper['title']}
        
        ### Key Findings:
        {findings_text}
        
        ### Methodology:
        {paper['methodology']}
        
        ### Conclusion:
        {paper['conclusion']}
        """
            formatted_summaries.append(formatted_summary)
        
        combined_summary = "\n\n".join(formatted_summaries)
        
        agent_contribution = ("summary_agent", combined_summary)
        
        # Return updates for both summary and messages
        return Command(
            goto="orchestrator_agent", 
            update={
                "summary": research_findings,
                "messages": [agent_contribution]
            }
        )
    else:
        # Summaries already exist, just proceed
        return Command(goto="orchestrator_agent", update=state)
def synthesis_agent(state : ResearchAgentState) -> Command:
    """Synthesizes the summaries into a cohesive narrative."""
    
    agent_description = state.available_agents.get("synthesis_agent", {})
    system_prompt = agent_description.get("system_prompt")
    
    previous_messages = serialize_messages(state.messages)
    user_prompt = f"Please synthesize the following research summaries:\n{previous_messages}"
    
    print("The synthesis agent is creating a literature review...")
    response = call_llm(system_prompt, user_prompt)
    
    # Handle None response
    if response is None:
        response_text = "Error: Could not generate synthesis due to API issues."
        print("⚠️ Synthesis agent failed - using default response")
    else:
        response_text = response.content if hasattr(response, 'content') else str(response)
    
    print("Synthesis complete.")

    # Only update messages, don't update synthesis_of_findings
    return Command(
        goto="orchestrator_agent", 
        update={
            "messages": [("synthesis_agent", response_text)]
        }
    )

def future_scope_agent(state : ResearchAgentState) -> Command:
    """Identifies research gaps and suggests future work."""
    
    agent_description = state.available_agents.get("future_scope_agent", {})
    system_prompt = agent_description.get("system_prompt")
    
    previous_messages = serialize_messages(state.messages)
    user_prompt = f"Based on the following literature analysis, please identify gaps and suggest future research directions:\n{previous_messages}"

    print("The future scope agent is identifying research gaps...")
    response = call_llm(system_prompt, user_prompt, FutureScope)
    
    # Handle None response
    if response is None:
        print("⚠️ Future scope agent failed - using default response")
        report_text = "### Identified Research Gaps\n- Error: Could not identify gaps due to API issues.\n\n### Suggested Future Directions\n- Error: Could not suggest directions due to API issues.\n\n### Concluding Synthesis\nError: Could not generate synthesis due to API issues."
    else:
        try:
            report_text = "### Identified Research Gaps\n"
            for gap in response.identified_gaps:
                report_text += f"- {gap}\n"
            report_text += "\n### Suggested Future Directions\n"
            for direction in response.suggested_directions:
                report_text += f"- {direction}\n"
            report_text += f"\n### Concluding Synthesis\n{response.synthesis}"
        except Exception as e:
            print(f"⚠️ Error processing future scope response: {str(e)}")
            report_text = "### Error\nCould not process future scope analysis due to response format issues."

    print("Future scope analysis complete.")
    
    # Only update messages, don't update future_directions_report
    return Command(
        goto="orchestrator_agent", 
        update={
            "messages": [("future_scope_agent", report_text)]
        }
    )

def critique_agent(state: ResearchAgentState) -> Command:
    """Provides feedback on the generated analysis."""

    agent_description = state.available_agents.get("critique_agent", {})
    system_prompt = agent_description.get("system_prompt")

    previous_messages = serialize_messages(state.messages)
    user_prompt = f"Please critique the following research analysis:\n{previous_messages}"
    
    print("The critique agent is reviewing the analysis... 🔎")
    response = call_llm(system_prompt, user_prompt)
    
    # Handle None response
    if response is None:
        response_text = "Error: Could not generate critique due to API issues."
        print("⚠️ Critique agent failed - using default response")
    else:
        response_text = response.content if hasattr(response, 'content') else str(response)
    
    print("Critique complete.")
    
    # Only update the fields that need updating - avoid updating user_query
    return Command(
        goto="orchestrator_agent", 
        update={
            "critique": response_text,
            "messages": [("critique_agent", response_text)]
        }
    )
def final_answer_tool(state : ResearchAgentState) -> Command[Literal["orchestrator_agent"]]:
    "Final answer tool is invoked to formulate a final answer based on the agent message history"

    system_prompt = f"""
    You're a helpful research assistant and your role is to provide a concise final answer with all the relevant details to answer the user query, based on the provided agent message history.

    Structure your response clearly. Use markdown headings for different sections (e.g., ## Synthesized Findings, ## Future Research Directions).
    """

    formatted_history = serialize_messages(state.messages)
    
    user_prompt = f"""
    ---
    **Original Task:**
    {state.user_query}
    ---
    **Agent Execution History:**
    {formatted_history}
    ---
    Compile the final, comprehensive answer for the user based on the history.
    """

    response = call_llm(system_prompt, user_prompt)
    
    # Handle None response
    if response is None:
        final_answer = "Error: Could not generate final answer due to API issues. Please check the logs and try again."
        print("⚠️ Final answer tool failed - using default response")
    else:
        final_answer = response.content if hasattr(response, 'content') else str(response)

    if isinstance(final_answer, str):
        final_answer = strip_think_blocks(final_answer)
    
    # Only update the final_answer field, not the entire state
    return Command(
        goto="orchestrator_agent",
        update={"final_answer": final_answer}
    )


# %% [markdown]
# ## Graph Definition

# %%
def init_state():
    """Initialize the state with default values."""
    return ResearchAgentState(available_agents=available_agents)

graph = StateGraph(ResearchAgentState)
graph.add_node("orchestrator_agent", orchestrator_agent)
graph.add_node("summary_agent", summary_agent)
graph.add_node("synthesis_agent", synthesis_agent)
graph.add_node("future_scope_agent", future_scope_agent)
graph.add_node("critique_agent", critique_agent)
graph.add_node("final_answer_tool", final_answer_tool)

# Define the edges
graph.add_edge(START, "orchestrator_agent")

# Fix the parameter name from 'router' to the correct parameter name
graph.add_conditional_edges(
    "orchestrator_agent",
    lambda state: (
        state.plan[0] if state.phase == "EXECUTE" and state.plan 
        else "final_answer_tool" if state.phase == "ANSWER"
        else END
    )
)

graph.add_edge("summary_agent", "orchestrator_agent")
graph.add_edge("synthesis_agent", "orchestrator_agent")
graph.add_edge("future_scope_agent", "orchestrator_agent")
graph.add_edge("critique_agent", "orchestrator_agent")
graph.add_edge("final_answer_tool", "orchestrator_agent")

# Compile the graph
graph = graph.compile()

# %% [markdown]
# ## Gradio functions

# %%
def extract_research_papers(
    state_dict,
    paper_files,
    max_iterations: int
) -> tuple[str, Dict, bool]:
    """Extract text from research papers and update state."""
    
    # Create a new ResearchAgentState or update existing one
    if isinstance(state_dict, dict):
        state = ResearchAgentState(**state_dict)
    else:
        state = ResearchAgentState()
    
    # Set max_iterations safely
    state.max_iterations = max_iterations
    
    if not paper_files:
        return "Please upload at least one research paper to analyze.", state.model_dump(), False
    
    console_output = StringIO()
    with contextlib.redirect_stdout(console_output):
        papers = []
        
        for file in paper_files:
            try:
                filename = file.name.split("/")[-1]
                print(f"📄 Processing {filename}...")
                
                if filename.lower().endswith(".pdf"):
                    # Fix DocumentConverter usage - it likely uses a different method name
                    try:
                        converter = DocumentConverter()
                        # Try different method names that might exist
                        if hasattr(converter, 'pdf_to_text'):
                            content = converter.pdf_to_text(file.name)
                        elif hasattr(converter, 'extract_text'):
                            content = converter.extract_text(file.name)
                        else:
                            # Fallback to PyPDF2 if available
                            import PyPDF2
                            content = ""
                            with open(file.name, 'rb') as pdf_file:
                                pdf_reader = PyPDF2.PdfReader(pdf_file)
                                for page_num in range(len(pdf_reader.pages)):
                                    content += pdf_reader.pages[page_num].extract_text()
                    except ImportError:
                        print("⚠️ PDF conversion libraries not available. Please install PyPDF2.")
                        continue
                        
                elif filename.lower().endswith(".docx"):
                    doc = docx.Document(file.name)
                    content = "\n".join([p.text for p in doc.paragraphs])
                elif filename.lower().endswith((".txt", ".md")):
                    with open(file.name, "r") as f:
                        content = f.read()
                else:
                    print(f"⚠️ Unsupported file format: {filename}")
                    continue
                
                papers.append((filename, content))
                print(f"✅ Successfully extracted {len(content)} characters from {filename}")
                
            except Exception as e:
                print(f"❌ Error processing {file.name}: {str(e)}")
        
        state.research_papers = papers
        print(f"📊 Extracted content from {len(papers)} files.")
        
    return console_output.getvalue(), state.model_dump(), len(papers) > 0
def call_orchestrator(state_dict : Dict, user_query : str):
    "Function prototype to call the orchestrator agent"
    state = ResearchAgentState.model_validate(state_dict)

    state.user_query = user_query
    buffer = StringIO()
    with contextlib.redirect_stdout(buffer):
        config = {}  # Use empty config for now
        
        try:
            result = graph.invoke(input=state, config=config)
            output_text = buffer.getvalue()
            result_dict = type_conversion(result, ResearchAgentState)
            return output_text, result_dict, True
        except Exception as e:
            error_msg = f"An error occurred during processing: {str(e)}"
            output_text = buffer.getvalue() + "\n" + error_msg
            return output_text, state_dict, False

    result_dict = type_conversion(result, ResearchAgentState)

    output_text = buffer.getvalue()

    return output_text, result_dict, True

# %% [markdown]
# ## Gradio Interface

# %%
with gr.Blocks() as research_assistant_server:
    gr.Markdown("# 🏆 Academic Research Assistant")

    with gr.Row():
        with gr.Column(scale=1):  # Image on the far left
            try:
                gr.Image(value="research_assistant.png", container=False, show_download_button=False, show_fullscreen_button=False)
            except Exception:  # Broader exception handling
                gr.Markdown("*Research Assistant Image*")

        with gr.Column(scale=4):  # Markdown starts next to the image
            gr.Markdown("## Your AI partner for literature reviews and research discovery.")
            gr.Markdown("Upload one or more research papers, ask a question, and let the assistant synthesize findings and identify future research directions.")

    state_dict = gr.State(value=ResearchAgentState(available_agents=available_agents).model_dump())
    extraction_successful = gr.State(value=False)
    api_key_set = gr.State(value=API_KEY is not None)
    
    with gr.Tabs():
        with gr.TabItem("🔑 API Key Setup"):
            gr.Markdown("### Set up your Nebius API Key")
            gr.Markdown("A valid API key is required to use this research assistant. You can either provide it here or set it as an environment variable.")
            
            with gr.Row():
                nebius_key_input = gr.Textbox(
                    label="Nebius API Key", 
                    placeholder="Enter your Nebius API key here...",
                    type="password",
                    value=""
                )
            
            # Add model discovery section
            with gr.Row():
                discover_models_button = gr.Button("🔍 Discover Available Models", variant="secondary")
                test_model_input = gr.Textbox(
                    label="Or manually test a model name:",
                    placeholder="e.g., gpt-3.5-turbo"
                )
            
            available_models_display = gr.Textbox(
                label="Available Models",
                lines=5,
                interactive=False
            )
            
            with gr.Row():
                model_dropdown = gr.Dropdown(
                    choices=NEBIUS_MODELS,
                    value=MODEL_NAME or NEBIUS_MODELS[0],
                    label="Select Nebius Model",
                    allow_custom_value=True
                )
            
            api_key_status = gr.Markdown("⚠️ **No API key detected.** Please enter your Nebius API key." if API_KEY is None else "✅ **API key configured.** You're ready to use the assistant.")
            
            save_key_button = gr.Button("Save API Key", variant="primary")
            
            def discover_models(key):
                if not key:
                    return "Please enter an API key first."
                
                global API_KEY, ENDPOINT_URL
                API_KEY = key
                ENDPOINT_URL = "https://api.studio.nebius.com/v1/"
                
                models = list_nebius_models()
                if models:
                    return "Available models:\n" + "\n".join([f"- {model}" for model in models])
                else:
                    return "Could not fetch models. Please check your API key."
            
            discover_models_button.click(
                fn=discover_models,
                inputs=[nebius_key_input],
                outputs=[available_models_display]
            )
            
            def save_api_key(key, model):
                success = setup_api_key(key, model)
                if success:
                    return f"✅ **API key saved successfully!** Using model: {MODEL_NAME}", True
                else:
                    return "❌ **Invalid API key.** Please check and try again.", False
            
            save_key_button.click(
                fn=save_api_key,
                inputs=[nebius_key_input, model_dropdown],
                outputs=[api_key_status, api_key_set]
            )
            
        with gr.TabItem("📚 Research Materials"):
            gr.Markdown("### 🍉 Feed the assistant with the research papers you want to analyze.")

            with gr.Row():
                research_papers_files = gr.File(
                    label="Upload Research Paper(s)", 
                    file_count="multiple", 
                    file_types=[".pdf", ".txt", ".docx", ".md"], 
                    height=200
                )

            with gr.Accordion("Advanced options", open=False):
                max_iterations = gr.Number(label="Number of refinement iterations", value=1, precision=0)

            extract_button = gr.Button("Process Papers", variant="primary")

            extract_console_output = gr.Textbox(label="Logs / Console Output")
            
            # Modify extract_research_papers to check for API key
            def extract_with_api_check(state_dict, paper_files, max_iterations, api_key_set):
                if not api_key_set:
                    return "⚠️ Please set up your API key in the 'API Key Setup' tab first.", state_dict, False
                return extract_research_papers(state_dict, paper_files, max_iterations)

            extract_button.click(
                fn=extract_with_api_check,
                inputs=[state_dict, research_papers_files, max_iterations, api_key_set],
                outputs=[extract_console_output, state_dict, extraction_successful]
            )

        # Rest of your tabs remain the same, but with API key checks for Q&A
        with gr.TabItem("🤖 Q&A Chatbot"):
            examples = """ℹ️ **Example Queries**
- Summarize the key findings from these papers.
- After synthesizing these articles, what are the main research gaps?
- Propose three future studies based on the provided research.
            """
            gr.Markdown(examples)
            user_query = gr.Textbox(label="Ask your research question", value="Identify the main gaps and suggest future work.", interactive=True)
            button = gr.Button("Ask the Research Assistant 🔬🧠", variant="primary")
            
            # Replace the @gr.render with a proper output textbox
            qa_output = gr.Markdown(
                label="Research Assistant Response",
                value="### 📝 Upload papers and ask a question to get started.",
                elem_id="qa_output"
            )
            
            output_logs = gr.Textbox(label="Logs/ Console Output", lines=10)

            def call_with_api_check(state_dict, user_query, api_key_set):
                """Wrapper to check API key before calling orchestrator."""
                if not API_KEY:
                    error_msg = "⚠️ Please set up your API key in the 'API Key Setup' tab first."
                    return error_msg, error_msg, state_dict
                
                if not state_dict.get("research_papers"):
                    error_msg = "### ❗️ No Research Papers Found\n\n👈🏽 Please upload research papers in the '📚 Research Materials' tab first."
                    return error_msg, error_msg, state_dict
                
                try:
                    logs, updated_state, success = call_orchestrator(state_dict, user_query)
                    
                    if success and updated_state.get("final_answer"):
                        final_answer = updated_state.get("final_answer")
                        return final_answer, logs, updated_state
                    else:
                        error_msg = f"### ❗️ Processing Failed\n\n{logs}\n\nPlease check the logs above for details."
                        return error_msg, logs, state_dict
                        
                except Exception as e:
                    error_msg = f"### ❗️ An Error Occurred\n\n```\n{str(e)}\n```\n\nPlease check your API key and try again."
                    return error_msg, f"Error: {str(e)}", state_dict

            def reset_output():
                """Reset the output when starting a new query."""
                return "### 🤖 Processing your request...\n\nPlease wait while the research assistant analyzes your papers and generates a response.", "Generating response..."

            button.click(
                fn=reset_output,
                outputs=[qa_output, output_logs]
            ).then(
                fn=call_with_api_check,
                inputs=[state_dict, user_query, api_key_set],
                outputs=[qa_output, output_logs, state_dict]
            )

        # with gr.TabItem("🔎 What's under the hood?"):
        #     gr.Markdown("## Details")
        #     with gr.Row():
        #         with gr.Column(scale=1):  # Image on the far left
        #             try:
        #                 gr.Image(value="mas_architecture.png", container=False, label="Architecture")
        #             except Exception:  # Broader exception handling
        #                 gr.Markdown("*Architecture diagram*")

                # with gr.Column(scale=2):  # Markdown starts next to the image
                #     gr.Markdown("There is a LangGraph-powered multi-agent system under the hood using an orchestrator approach to plan and route the requests.")
                #     gr.Markdown("Each agent is specialized in performing academic analysis tasks like summarizing, synthesizing, and identifying future research directions.")

if __name__ == "__main__":
    research_assistant_server.launch(mcp_server=True)