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README.md

@@ -1,12 +1,6 @@
 ---
-title: My Profile Snapshot
-emoji: 🐨
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 6.6.0
+title: My_profile_snapshot
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.49.1
 ---
-
-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_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="Notes not provided"):
+    push(f"Recording interest from {name} having email id {email} and notes : {notes}")
+    return{"recorded" : "ok"}
+
+def record_unknown_questions(question):
+    push(f"Unknwon question{question} has been asked, which I can not answer")
+    return{"recorded" : "ok"}
+
+record_user_details_json = {
+"name" : "record_user_details",
+"description" : "Use this tool to record the user name and emial if that neing is interested in connecting",
+"parameters" : {
+    "type" : "object",
+    "properties":{
+        "email" : {
+            "type" : "string",
+            "description" : "The Users email id"
+        },
+        "name":{
+            "type" : "string",
+            "description" : "the name of the User, if they provide it"
+        },
+        "notes" :{
+            "type" : "string",
+            "descrioption" : "Any additonal information about the conversation that worth noting, for more context"
+        }
+    },
+    "required" : ["email"],
+    "additionalproperties" : False
+    }
+}
+
+record_unknown_questions_json ={
+    "name" : "record_unknown_questions",
+    "description" : "Use this tool to capture any unknown question which you are unable to answer",
+    "Parameters" : {
+        "type" : "object",
+        "properties" : {
+            "question" :{
+                "type" : "string",
+                "description" : "The User asks the question which could't be answered"
+            },
+            "required" : ["question"],
+            "additionalpropertise" : False
+        }
+    }
+}
+
+tools = [
+    {"type" : "function", "function" : record_user_details_json},
+    {"type" : "function", "function" : record_unknown_questions_json}
+]
+
+
+
+class Evaluation(BaseModel):
+    is_acceptable: bool
+    feedback: str
+
+class Me :
+
+    def __init__(self) :
+        self.openai = OpenAI()
+        self.trinity = OpenAI(api_key=os.getenv("OPENROUTER_API_KEY"), base_url="https://openrouter.ai/api/v1")
+        self.name = "Sourav"
+        reader = PdfReader("me/Sourav_profile.pdf")
+        self.linkdin = ""
+        for page in reader.pages :
+            text = page.extract_text()
+            if text :
+                self.linkdin += text
+
+        with open("me/sourav_summary.txt","r", encoding="utf-8") as f:
+            self.summary = f.read()
+
+
+
+
+    def handle_tool_calls(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.linkdin}\n\n"
+        system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
+        return system_prompt
+
+
+
+
+
+    def evaluator_system_prompt(self):
+        evaluator_system_prompt = f"You are an evaluator and Moderator 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 also You can suggest some improvements also  \
+                                    You are the Manager who oversees the communications and PR for the {self.name} \
+                                    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.linkdin}\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
+
+    def evaluator_user_prompt(self,reply, message,history):
+        user_prompt = f"Here is the converstation between user and Agent: \n\n{history}\n\n"
+        user_prompt += f"Here is the latest message from the user: \n\n{message}\n\n"
+        user_prompt += f"Here is the latest reply from the Agent: \n\n{reply}\n\n"
+        user_prompt += f"Please evaluate whether the response from Agent is acceptable or not"
+        return user_prompt
+
+
+    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.trinity.beta.chat.completions.parse(model="arcee-ai/trinity-mini:free", messages=messages, response_format=Evaluation)
+        return response.choices[0].message.parsed
+
+
+
+
+    def rerun(self,reply, message, history, feedback):
+        updated_system_prompt = self.system_prompt() + "\n\n## Previous answer rejected\n you just tried to reply but the quality control rejected your reply\n"
+        updated_system_prompt += f"## You attempted answer :\n{reply}\n\n"
+        updated_system_prompt += f"## The answer is rejected because of :\n{feedback}\n\n"
+        messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role":"user", "content" : message}]
+        response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
+        return response.choices[0].message.content
+
+
+
+    def chat(self,message, history):
+        messages = [{"role" : "system", "content" : self.system_prompt()}] + history + [{"role" : "user", "content" : message}]
+        done = False
+
+        while not done:
+
+            # We stream the responses
+            stream = self.openai.chat.completions.create(model = "gpt-4o-mini", messages=messages, stream=True)
+            final_response = ""
+            for chunk in stream:
+                final_response += chunk.choices[0].delta.content or ''
+                yield final_response
+
+            if final_response == "tool_calls":
+                message = stream.choices[0].message
+                tool_calls = message.tool_calls
+                result = self.handle_tool_calls(tool_calls)
+                messages.append(message)
+                messages.extend(result)
+            else:
+                done = True
+                yield final_response
+            
+            # Intorducing Evaluation by LLM
+            evaluation = self.evaluate(final_response, message, history)
+        
+            if evaluation.is_acceptable:
+                print("Passed Evaluation - returning reply")
+            else:
+                print("failed evaluation- retrying")
+                print(evaluation.feedback)
+                final_response = self.rerun(final_response,message, history, evaluation.feedback)
+
+        # if not done:
+        yield final_response
+
+
+if __name__ == "__main__":
+    me = Me()
+    gr.ChatInterface(me.chat, type="messages").launch()
+

me/sourav_summary.txt

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+Hi My name is Sourav Sinha, I am a Data Scientist by profession , I like the AI and its usage. I have seen Anime on AI and the world of Sci-Fi right now, I am kind of building the Sci-Fi , with the tools and programming.
+i like food particularly Bengali and Andhra cuisine.
+I am a father to two beautiful daughters  

requirements.txt

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