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README.md

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 ---
 title: ProFileGuide
-emoji: ⚡
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 5.34.1
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.33.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from dotenv import load_dotenv
+from openai import OpenAI
+import json
+import os
+import requests
+from pypdf import PdfReader
+import gradio as gr
+from pydantic import BaseModel
+
+# Load the environment variables from the .env file. and override the existing ones to avoid any conflicts
+load_dotenv(override=True)
+
+# create a pydantic model for the evaluation.
+# It has two attributes 
+# - is_acceptable: bool : To check if the response is acceptable or not
+# - feedback: str : To give the feedback on the response
+class Evaluation(BaseModel):
+    is_acceptable: bool
+    feedback: str
+
+
+
+# This functionn sends a push notification to user's mobile through pushover application along with the test
+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,
+        }
+    )
+
+
+# ----- TOOLS--------------------------------
+# This function records the user details and sends a push notification to the user
+# If the end user is interested to get in touch then this function is called
+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"}
+
+
+# This function records the unknown question and sends a push notification to the user
+# If the LLM is not able to answer the question then this function will be called
+def record_unknown_question(question):
+    push(f"Recording {question}")
+    return {"recorded": "ok"}
+
+
+# Define a JSON schema to tell the LLM how to use tool "record_user_details"
+record_user_details_json = {
+    "name": "record_user_details", # tool name
+    "description": "Use this tool to record that a user is interested in being in touch and provided an email address", # when to use tool
+    "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
+    }
+}
+
+# This is a JSON schema to tell LLM how to use tool "record_unknown_question"
+record_unknown_question_json = {
+    "name": "record_unknown_question", # tool name
+    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", # when to use tool
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "question": {
+                "type": "string",
+                "description": "The question that couldn't be answered"
+            },
+        },
+        "required": ["question"],
+        "additionalProperties": False
+    }
+}
+
+# Defines list of tools to be passed to LLM
+tools = [{"type": "function", "function": record_user_details_json}, # This says tool is a function and "record_user_details_json" describing the tool "record_user_details"
+        {"type": "function", "function": record_unknown_question_json}]   # This says tool is a function and "record_unknown_question_json" describing the tool "record_unknown_questions"
+
+
+class Me:
+
+    def __init__(self):
+        self.openai = OpenAI()
+        self.gemini = OpenAI(api_key=os.getenv("GOOGLE_API_KEY"), base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+        self.name = "Arpan Bandyopadhyay"
+        # Prepare the resources for LLM
+        reader = PdfReader("me/linkedin_arpan.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()
+
+
+    # This function handles the tool calls requested by LLM
+    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)
+            # get the tool name from global scope
+            tool = globals().get(tool_name)
+            # execute the tool if it exists
+            result = tool(**arguments) if tool else {}
+            results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id})
+        return results
+    
+
+    # This function defines system prompt for LLM (How to act while providing the answer)
+    # System prompt is equipped with the resources - summary and linkedin profile
+    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
+    
+
+
+    # Cobstruct the full message list to send to the LLM -It includes 
+    # - System prompt
+    # - History of conversation
+    # - users message
+    def chat(self, message, history):
+        # Construct messages list (conversation)
+        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 )# tools=tools ->list of tools that LLM can use
+            # finish_reason is the reason what API tells us that why the LLM stopped generating the response
+            if response.choices[0].finish_reason=="tool_calls": # finish_reason is "tool_calls" means LLM requested for tool calls
+                message = response.choices[0].message
+                tool_calls = message.tool_calls # Extract list of tool calls what LLM wants to invoke
+                results = self.handle_tool_call(tool_calls) # Invoke the tool and get the result
+                messages.append(message)
+                messages.extend(results)
+            else:
+                done = True
+        # reply from LLM against users message        
+        reply= response.choices[0].message.content
+        # Evaluate the reply from the LLM using evaluator LLM
+        evaluation = self.evaluate(reply, message, history)
+        # Check if the reply is acceptable or not
+        if evaluation.is_acceptable:
+            print("Passed evaluation - returning reply")
+        else:
+            print("Failed evaluation - retrying")
+            print(evaluation.feedback)
+            # If the reply is not acceptable then retry it again and get the improved reply from the LLM
+            reply = self.rerun(reply, message, history, evaluation.feedback)       
+        return reply
+    
+
+
+    # This function retries a previous LLM response that was rejected by a quality evaluator. It sends a new prompt to the LLM with:
+    # the previous answer,
+    # the reason it was rejected,
+    # and asks it to try again.
+    def rerun(self,reply, message, history, feedback):
+        updated_system_prompt = self.system_prompt() + f"\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n"
+        updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n" #Add the previous (bad) answer to the system prompt
+        updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n"  # Add the reason why it was rejected
+        messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}]
+        response = self.gemini.chat.completions.create(model="gemini-2.0-flash", messages=messages)
+        return response.choices[0].message.content
+    
+
+
+    # This function defines evaluator system prompt on how to evaluate the reply from the LLM
+    def evaluator_system_prompt(self):
+        evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \
+    You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \
+    The Agent is playing the role of {self.name} and is representing {self.name} on their website. \
+    The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \
+    The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:"
+
+        evaluator_system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
+        evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback."
+        return evaluator_system_prompt
+    
+
+
+    # Define a function to construct a user prompt for an evaluator LLM
+    def evaluator_user_prompt(self,reply, message, history):
+        # Start by including the full conversation history between the user and the agent
+        user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n"
+        
+        # Add the latest message that the user just asked
+        user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n"
+        
+        # Add the latest reply that the agent gave
+        user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n"
+        
+        # Ask the evaluator to review the agent's response
+        user_prompt += f"Please evaluate the response, replying with whether it is acceptable and your feedback."
+        
+        # Return the complete constructed prompt for the evaluator LLM
+        return user_prompt
+    
+
+    
+    # This function evaluates the reply from the LLM using evaluator LLM
+    # It includes :
+    # - evaluator System prompt
+    # - history of conversation
+    # - LLM's reply
+    # - user's original question
+    def evaluate(self,reply, message, history) -> Evaluation:
+        messages = [{"role": "system", "content": self.evaluator_system_prompt()}] + [{"role": "user", "content": self.evaluator_user_prompt(reply, message, history)}]
+        response = self.gemini.beta.chat.completions.parse(model="gemini-2.0-flash", messages=messages, response_format=Evaluation) # This says it should generate response as per Evaludation class formats
+        return response.choices[0].message.parsed
+
+
+
+if __name__ == "__main__":
+    me = Me()
+    gr.ChatInterface(me.chat, type="messages").launch()
+    

me/summary.txt

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+At HCLSoftware, my journey as a Senior Software Engineer III is centered around integrating AI/ML capabilities into high-quality JEE-based applications. With a deep understanding of machine learning algorithms, data analytics, and statistical modeling, I focus on developing intelligent solutions that enhance decision-making processes, optimize performance, and drive innovation for the banking and financial services sector.
+
+My strong foundation in Computer Science from the prestigious Dream Institute of Technology empowers me to design and implement advanced AI/ML models, leveraging tools such as predictive analytics, natural language processing, and data mining to solve complex business problems. I am committed to ensuring our applications not only meet but exceed client expectations by embedding cutting-edge AI/ML techniques that enable scalable, data-driven solutions.

requirements.txt

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+requests
+python-dotenv
+gradio
+pypdf
+openai
+openai-agents