app.py

# from dotenv import load_dotenv
# from openai import OpenAI
# import json
# import os
# import requests
# from pypdf import PdfReader
# import gradio as gr


# load_dotenv(override=True)

# def push(text):
#     requests.post(
#         "https://api.pushover.net/1/messages.json",
#         data={
#             "token": os.getenv("PUSHOVER_TOKEN"),
#             "user": os.getenv("PUSHOVER_USER"),
#             "message": text,
#         }
#     )


# def record_user_details(email, name="Name not provided", notes="not provided"):
#     push(f"Recording {name} with email {email} and notes {notes}")
#     return {"recorded": "ok"}

# def record_unknown_question(question):
#     push(f"Recording {question}")
#     return {"recorded": "ok"}

# record_user_details_json = {
#     "name": "record_user_details",
#     "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "email": {
#                 "type": "string",
#                 "description": "The email address of this user"
#             },
#             "name": {
#                 "type": "string",
#                 "description": "The user's name, if they provided it"
#             }
#             ,
#             "notes": {
#                 "type": "string",
#                 "description": "Any additional information about the conversation that's worth recording to give context"
#             }
#         },
#         "required": ["email"],
#         "additionalProperties": False
#     }
# }

# record_unknown_question_json = {
#     "name": "record_unknown_question",
#     "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "question": {
#                 "type": "string",
#                 "description": "The question that couldn't be answered"
#             },
#         },
#         "required": ["question"],
#         "additionalProperties": False
#     }
# }

# tools = [{"type": "function", "function": record_user_details_json},
#         {"type": "function", "function": record_unknown_question_json}]


# class Me:

#     def __init__(self):
#         self.openai = OpenAI()
#         self.name = "Ed Donner"
#         reader = PdfReader("me/linkedin.pdf")
#         self.linkedin = ""
#         for page in reader.pages:
#             text = page.extract_text()
#             if text:
#                 self.linkedin += text
#         with open("me/summary.txt", "r", encoding="utf-8") as f:
#             self.summary = f.read()


#     def handle_tool_call(self, tool_calls):
#         results = []
#         for tool_call in tool_calls:
#             tool_name = tool_call.function.name
#             arguments = json.loads(tool_call.function.arguments)
#             print(f"Tool called: {tool_name}", flush=True)
#             tool = globals().get(tool_name)
#             result = tool(**arguments) if tool else {}
#             results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id})
#         return results
    
#     def system_prompt(self):
#         system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
# particularly questions related to {self.name}'s career, background, skills and experience. \
# Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
# You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \
# Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
# If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
# If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. "

#         system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
#         system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
#         return system_prompt
    
#     def chat(self, message, history):
#         messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
#         done = False
#         while not done:
#             response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
#             if response.choices[0].finish_reason=="tool_calls":
#                 message = response.choices[0].message
#                 tool_calls = message.tool_calls
#                 results = self.handle_tool_call(tool_calls)
#                 messages.append(message)
#                 messages.extend(results)
#             else:
#                 done = True
#         return response.choices[0].message.content
    

# if __name__ == "__main__":
#     me = Me()
#     gr.ChatInterface(me.chat, type="messages").launch()




# from dotenv import load_dotenv
# from openai import OpenAI
# import json
# import os
# import requests
# from pypdf import PdfReader
# import gradio as gr

# load_dotenv(override=True)

# GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
# google_api_key = os.getenv("GOOGLE_API_KEY")

# # Initialize Gemini client
# gemini = OpenAI(
#     base_url=GEMINI_BASE_URL,
#     api_key=google_api_key
# )

# def push(text):
#     requests.post(
#         "https://api.pushover.net/1/messages.json",
#         data={
#             "token": os.getenv("PUSHOVER_TOKEN"),
#             "user": os.getenv("PUSHOVER_USER"),
#             "message": text,
#         }
#     )


# def record_user_details(email, name="Name not provided", notes="not provided"):
#     push(f"Recording {name} with email {email} and notes {notes}")
#     return {"recorded": "ok"}


# def record_unknown_question(question):
#     push(f"Recording {question}")
#     return {"recorded": "ok"}


# record_user_details_json = {
#     "name": "record_user_details",
#     "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "email": {
#                 "type": "string",
#                 "description": "The email address of this user"
#             },
#             "name": {
#                 "type": "string",
#                 "description": "The user's name, if they provided it"
#             },
#             "notes": {
#                 "type": "string",
#                 "description": "Any additional information about the conversation that's worth recording to give context"
#             }
#         },
#         "required": ["email"],
#         "additionalProperties": False
#     }
# }

# record_unknown_question_json = {
#     "name": "record_unknown_question",
#     "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "question": {
#                 "type": "string",
#                 "description": "The question that couldn't be answered"
#             },
#         },
#         "required": ["question"],
#         "additionalProperties": False
#     }
# }

# tools = [
#     {"type": "function", "function": record_user_details_json},
#     {"type": "function", "function": record_unknown_question_json}
# ]


# class Me:

#     def __init__(self):
#         self.openai = gemini     # REPLACED OpenAI WITH GEMINI
#         self.name = "AKASH M J"

#         reader = PdfReader("me/Profile.pdf")
#         self.linkedin = ""
#         for page in reader.pages:
#             text = page.extract_text()
#             if text:
#                 self.linkedin += text

#         with open("me/summary.txt", "r", encoding="utf-8") as f:
#             self.summary = f.read()

#     def handle_tool_call(self, tool_calls):
#         results = []
#         for tool_call in tool_calls:
#             tool_name = tool_call.function.name
#             arguments = json.loads(tool_call.function.arguments)
#             print(f"Tool called: {tool_name}", flush=True)
#             tool = globals().get(tool_name)
#             result = tool(**arguments) if tool else {}
#             results.append({
#                 "role": "tool",
#                 "content": json.dumps(result),
#                 "tool_call_id": tool_call.id
#             })
#         return results

#     def system_prompt(self):
#         system_prompt = (
#             f"You are acting as {self.name}. You are answering questions on {self.name}'s website, "
#             f"particularly questions related to {self.name}'s career, background, skills and experience. "
#             f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. "
#             f"You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. "
#             f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. "
#             f"If you don't know the answer to any question, use your record_unknown_question tool to record the question. "
#             f"If the user is engaging in discussion, try to steer them towards getting in touch via email."
#         )

#         system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
#         system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
#         return system_prompt

#     def chat(self, message, history):
#         messages = [
#             {"role": "system", "content": self.system_prompt()}
#         ] + history + [
#             {"role": "user", "content": message}
#         ]

#         done = False
#         while not done:
#             # ---- CHANGED TO USE GEMINI ----
#             response = self.openai.chat.completions.create(
#                 model="gemini-2.0-flash",
#                 messages=messages,
#                 tools=tools
#             )
#             # --------------------------------

#             if response.choices[0].finish_reason == "tool_calls":
#                 message = response.choices[0].message
#                 tool_calls = message.tool_calls
#                 results = self.handle_tool_call(tool_calls)
#                 messages.append(message)
#                 messages.extend(results)
#             else:
#                 done = True

#         return response.choices[0].message.content


# if __name__ == "__main__":
#     me = Me()
#     gr.ChatInterface(me.chat, type="messages").launch()
#     # gr.ChatInterface(me.chat).launch()


# app.py
from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr
import sqlite3
import time

load_dotenv(override=True)

# --- CONFIG ---
GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
google_api_key = os.getenv("GOOGLE_API_KEY")

# Initialize Gemini client (using OpenAI wrapper you used earlier)
gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)

# --- Pushover helper ---
def push(text):
    token = os.getenv("PUSHOVER_TOKEN")
    user = os.getenv("PUSHOVER_USER")
    if not token or not user:
        print("Pushover credentials not set. Skipping push.")
        return
    try:
        requests.post(
            "https://api.pushover.net/1/messages.json",
            data={"token": token, "user": user, "message": text},
            timeout=5
        )
    except Exception as e:
        print("Pushover error:", e)

# --- Tools (actual implementations) ---
def record_user_details(email, name="Name not provided", notes="not provided"):
    push(f"Recording contact: {name} <{email}> notes: {notes}")
    return {"recorded": "ok", "email": email, "name": name}

def record_unknown_question(question):
    push(f"Unknown question recorded: {question}")
    # Optionally write to a local file for audits
    os.makedirs("me/logs", exist_ok=True)
    with open("me/logs/unknown_questions.txt", "a", encoding="utf-8") as f:
        f.write(question.strip() + "\n")
    return {"recorded": "ok", "question": question}

def search_faq(query):
    db_path = os.path.join("me", "qa.db")
    if not os.path.exists(db_path):
        return {"answer": "FAQ database not found."}
    conn = sqlite3.connect(db_path)
    cur = conn.cursor()
    cur.execute("SELECT answer FROM faq WHERE question LIKE ? LIMIT 1", (f"%{query}%",))
    row = cur.fetchone()
    conn.close()
    return {"answer": row[0]} if row else {"answer": "not found"}

# --- Tool JSON metadata (for function-calling style) ---
record_user_details_json = {
    "name": "record_user_details",
    "description": "Record an interested user's email and optional name/notes.",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {"type": "string"},
            "name": {"type": "string"},
            "notes": {"type": "string"}
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Record any question the assistant could not answer.",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {"type": "string"}
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

search_faq_json = {
    "name": "search_faq",
    "description": "Search the FAQ database for a question.",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {"type": "string"}
        },
        "required": ["query"],
        "additionalProperties": False
    }
}

tools = [
    {"type": "function", "function": record_user_details_json},
    {"type": "function", "function": record_unknown_question_json},
    {"type": "function", "function": search_faq_json}
]

# --- The assistant class ---
class Me:
    def __init__(self):
        self.openai = gemini
        self.name = "AKASH M J"

        # Load profile PDF into self.linkedin
        self.linkedin = ""
        try:
            reader = PdfReader(os.path.join("me", "Profile.pdf"))
            for page in reader.pages:
                text = page.extract_text()
                if text:
                    self.linkedin += text + "\n"
        except Exception as e:
            print("Could not read Profile.pdf:", e)

        # Load summary
        try:
            with open(os.path.join("me", "summary.txt"), "r", encoding="utf-8") as f:
                self.summary = f.read()
        except Exception as e:
            print("Could not read summary.txt:", e)
            self.summary = ""

        # Load knowledge files (RAG-style simple concatenation)
        self.knowledge = ""
        kb_dir = os.path.join("me", "knowledge")
        if os.path.exists(kb_dir):
            for fn in sorted(os.listdir(kb_dir)):
                path = os.path.join(kb_dir, fn)
                try:
                    with open(path, "r", encoding="utf-8") as f:
                        self.knowledge += f"# {fn}\n" + f.read() + "\n\n"
                except Exception as e:
                    print("Error reading", path, e)

    def system_prompt(self):
        system_prompt = (
            f"You are acting as {self.name}. Answer questions about {self.name}'s background "
            "and experience using the context provided. Be professional and concise. "
            "If you don't know an answer, use the record_unknown_question tool."
        )
        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n"
        system_prompt += f"## LinkedIn profile (extracted):\n{self.linkedin}\n\n"
        system_prompt += f"## Knowledge base:\n{self.knowledge}\n\n"
        return system_prompt

    def handle_tool_call(self, tool_calls):
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call.function.name
            try:
                arguments = json.loads(tool_call.function.arguments)
            except Exception:
                arguments = {}
            print("Tool called:", tool_name, arguments, flush=True)
            tool = globals().get(tool_name)
            result = tool(**arguments) if tool else {}
            results.append({
                "role": "tool",
                "content": json.dumps(result),
                "tool_call_id": tool_call.id
            })
        return results

    # Simple router/orchestrator: route common queries to the FAQ or to the LLM
    def route_question(self, question):
        q = question.lower()
        # keywords that map to FAQ
        faq_keywords = ["project", "tech stack", "stack", "skill", "skills", "study", "education", "experience"]
        if any(k in q for k in faq_keywords):
            return "search_faq"
        return None

    def evaluate_answer(self, user_question, ai_answer):
        # Simple evaluator: ask the LLM to judge the quality
        eval_prompt = f"""
You are an evaluator. Judge whether the assistant reply is clear, correct, and complete for the user question.
Return exactly PASS or FAIL and a one-line reason.

User question:
{user_question}

Assistant reply:
{ai_answer}
"""
        try:
            ev = self.openai.chat.completions.create(
                model="gemini-2.0-flash",
                messages=[{"role":"system","content":"You are an evaluator."},
                          {"role":"user","content":eval_prompt}]
            )
            text = ev.choices[0].message.content.strip()
            # very simple parse
            if text.upper().startswith("PASS"):
                return {"result":"PASS", "note": text}
            else:
                return {"result":"FAIL", "note": text}
        except Exception as e:
            print("Evaluator failed:", e)
            return {"result":"UNKNOWN", "note": str(e)}

    def chat(self, message, history):
        # build messages with system prompt + history + user
        messages = [{"role":"system","content":self.system_prompt()}] + history + [{"role":"user","content":message}]

        # 1) Router: check if the question should use the FAQ tool
        tool_to_use = self.route_question(message)
        if tool_to_use == "search_faq":
            # call tool directly and return evaluated answer
            tool_result = search_faq(message)
            raw_answer = tool_result.get("answer", "I don't have that in my FAQ.")
            eval_res = self.evaluate_answer(message, raw_answer)
            if eval_res["result"] == "PASS":
                return raw_answer
            else:
                # fall back to LLM if FAIL
                pass

        # 2) Normal LLM flow with tools support (function-calling style)
        done = False
        while not done:
            response = self.openai.chat.completions.create(
                model="gemini-2.0-flash",
                messages=messages,
                tools=tools
            )

            finish = response.choices[0].finish_reason
            if finish == "tool_calls":
                # the LLM asked to call a tool
                message_obj = response.choices[0].message
                tool_calls = getattr(message_obj, "tool_calls", [])
                results = self.handle_tool_call(tool_calls)
                messages.append(message_obj)
                messages.extend(results)
                # loop again so the LLM can consume tool outputs
            else:
                done = True

        ai_answer = response.choices[0].message.content
        # 3) Evaluate the answer; if FAIL, ask LLM to improve
        eval_res = self.evaluate_answer(message, ai_answer)
        if eval_res["result"] == "FAIL":
            # ask the model to improve using the critique
            improve_prompt = f"User question:\n{message}\n\nAssistant previous reply:\n{ai_answer}\n\nEvaluator note:\n{eval_res['note']}\n\nPlease produce an improved concise answer."
            messages.append({"role":"user","content":improve_prompt})
            improved_resp = self.openai.chat.completions.create(model="gemini-2.0-flash", messages=messages)
            ai_answer = improved_resp.choices[0].message.content

        return ai_answer

# --- Launch ---
if __name__ == "__main__":
    me = Me()
    gr.ChatInterface(me.chat, type="messages").launch()