app.py

# ===========================================
# Medini Autonomous Agent - app.py
# Compatible with LangChain 0.3.x + langchain-community
# ===========================================

import os
import json
import threading
import re
from typing import Dict, Any

import gradio as gr
from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
import uvicorn

from transformers import pipeline

# ===== LangChain Community Imports =====
from langchain_community.llms import HuggingFacePipeline
from langchain_community.utilities import SerpAPIWrapper
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings

from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent, Tool
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.docstore.document import Document

# ✅ Updated Python REPL tool import
from langchain.tools.python.tool import PythonAstREPLTool

# ===========================================
# ENVIRONMENT VARIABLES
# ===========================================
HF_TOKEN = os.getenv("HF_TOKEN")
SERPAPI_KEY = os.getenv("SERPAPI_API_KEY")
JWT_SECRET = os.getenv("JWT_SECRET", "changeme123")

# ===========================================
# AUTH
# ===========================================
security = HTTPBearer()

def verify_jwt(credentials: HTTPAuthorizationCredentials = Depends(security)):
    token = credentials.credentials
    if token != JWT_SECRET:
        raise HTTPException(status_code=403, detail="Invalid token")
    return True

# ===========================================
# MODEL LOADER
# ===========================================
MODEL_ID = "PuruAI/Medini_Intelligence"
FALLBACK_MODEL = "gpt2"

def load_llm():
    pipeline_kwargs = {"max_new_tokens": 512, "temperature": 0.7}
    try:
        model_pipeline = pipeline("text-generation", model=MODEL_ID, use_auth_token=HF_TOKEN, **pipeline_kwargs)
    except Exception:
        print(f"Warning: Failed to load {MODEL_ID}. Falling back to {FALLBACK_MODEL}.")
        model_pipeline = pipeline("text-generation", model=FALLBACK_MODEL, **pipeline_kwargs)

    return HuggingFacePipeline(pipeline=model_pipeline)

llm = load_llm()

# ===========================================
# VECTOR MEMORY
# ===========================================
embeddings = HuggingFaceEmbeddings()
chroma_db = Chroma(persist_directory="./medini_memory", embedding_function=embeddings)
retriever = chroma_db.as_retriever()

qa_prompt_template = """
You are a question-answering system. Use the following context, which contains information retrieved from memory, to answer the user's question. 
If the context is empty or does not contain the answer, state clearly that the information is not in memory.
Context:
{context}
Question: {question}
Answer:
"""
QA_PROMPT = PromptTemplate(template=qa_prompt_template, input_variables=["context", "question"])
qa_chain = LLMChain(llm=llm, prompt=QA_PROMPT)

def retrieve_and_answer(question: str) -> str:
    docs = retriever.get_relevant_documents(question)
    context = "\n---\n".join([d.page_content for d in docs])
    return qa_chain.run(context=context, question=question)

# ===========================================
# TOOLS
# ===========================================
search = SerpAPIWrapper(serpapi_api_key=SERPAPI_KEY)
python_tool = PythonAstREPLTool()

tools = [
    Tool(name="Knowledge Recall", func=retrieve_and_answer, description="Retrieve info from Medini memory (Chroma DB)."),
    Tool(name="Web Search", func=search.run, description="Search the web for up-to-date information."),
    Tool(name="Python REPL", func=python_tool.run, description="Execute Python code for math/data manipulation."),
]

TOOL_MAP = {tool.name.lower().replace(" ", ""): tool.func for tool in tools}

# ===========================================
# AGENT
# ===========================================
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent="conversational-react-description",
    memory=memory,
    verbose=True
)

# ===========================================
# PLANNER
# ===========================================
plan_prompt = PromptTemplate(
    input_variables=["goal"],
    template="""
You are Medini Planner. Decompose the high-level goal into a JSON object containing a 'steps' array (max 6 steps). Each step must have: id (integer), name (short string), description (detailed instruction), and tool_hint (either 'recall', 'search', 'python', or 'agent').
Return JSON only.
Goal: {goal}
"""
)
planner_chain = LLMChain(llm=llm, prompt=plan_prompt)

def create_plan(goal: str) -> Dict[str, Any]:
    raw = planner_chain.run(goal=goal)
    m = re.search(r"\{.*\}", raw, flags=re.DOTALL)
    json_str = m.group(0) if m else raw
    json_str = json_str.replace("```json", "").replace("```", "").strip()

    try:
        plan = json.loads(json_str)
        if 'steps' not in plan:
            raise ValueError("Parsed JSON missing 'steps'.")
        return plan
    except json.JSONDecodeError as e:
        print(f"JSON Parsing Error: {e} in string: {json_str[:200]}...")
        raise ValueError("Planner returned malformed JSON.") from e

def execute_step(step: Dict[str, Any]) -> Dict[str, Any]:
    hint = (step.get("tool_hint") or "").lower()
    input_text = step.get("description")
    output, status = "Execution skipped.", "error"

    try:
        tool_func = None
        if "recall" in hint:
            tool_func = TOOL_MAP.get("knowledgerecall")
        elif "search" in hint:
            tool_func = TOOL_MAP.get("websearch")
        elif "python" in hint:
            tool_func = TOOL_MAP.get("pythonrepl")

        if tool_func:
            output = tool_func(input_text)
        else:
            output = agent.run(input_text)

        status = "ok"
    except Exception as e:
        output = f"Execution Error: {str(e)}"

    chroma_db.add_documents([Document(page_content=f"Step {step['id']} - {step['name']} Result: {output}")])
    return {"id": step['id'], "name": step['name'], "status": status, "output": output}

def execute_plan(goal: str) -> Dict[str, Any]:
    try:
        plan = create_plan(goal)
    except ValueError as e:
        return {"goal": goal, "error": str(e)}

    results = [execute_step(step) for step in plan.get("steps", [])]
    return {"goal": goal, "plan": plan, "results": results}

# ===========================================
# FASTAPI BACKEND
# ===========================================
app = FastAPI(title="Medini Agent API")

@app.post("/chat")
def chat_endpoint(message: str, auth: bool = Depends(verify_jwt)):
    response = agent.run(message)
    return {"response": response}

@app.post("/goal")
def goal_endpoint(goal: str, auth: bool = Depends(verify_jwt)):
    return execute_plan(goal)

# ===========================================
# GRADIO FRONTEND
# ===========================================
def gradio_chat(message, history):
    try:
        response = agent.run(message)
        history.append((message, response))
    except Exception as e:
        history.append((message, f"Error: {str(e)}"))
    return history, ""

def gradio_execute_plan(goal):
    try:
        return execute_plan(goal)
    except Exception as e:
        return {"error": f"Failed to execute plan: {str(e)}"}

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 Medini Autonomous Agent")
    gr.Markdown("Chat or submit high-level goals. Agentic AI handles reasoning, memory, and tool use.")

    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("## Conversational Chat")
            chatbot = gr.Chatbot(height=400)
            msg = gr.Textbox(placeholder="Type your message...", label="Chat Input")
            clear_btn = gr.Button("Clear Chat")
            msg.submit(gradio_chat, [msg, chatbot], [chatbot, msg])
            clear_btn.click(lambda: [], None, chatbot, queue=False)

        with gr.Column(scale=1):
            gr.Markdown("## Autonomous Goal Planner")
            goal_input = gr.Textbox(placeholder="Enter high-level goal...", label="Goal")
            run_goal_btn = gr.Button("Run Goal", variant="primary")
            gr.Markdown("---")
            gr.Markdown("### Execution Report")
            goal_output = gr.JSON(label="Plan and Results")
            run_goal_btn.click(gradio_execute_plan, [goal_input], goal_output)

# ===========================================
# LAUNCH
# ===========================================
if __name__ == "__main__":
    def start_api():
        uvicorn.run(app, host="0.0.0.0", port=8000, log_level="critical")
    threading.Thread(target=start_api, daemon=True).start()
    demo.launch(share=False)