Update final_professional_persona_agent.py

final_professional_persona_agent.py

@@ -1,292 +1,331 @@
-import os
-
-import json
-
-import requests
-
-from groq import Groq
-
-from PyPDF2 import PdfReader
-
-import gradio as gr
-
-from openai import OpenAI
-
-from dotenv import load_dotenv
-
-load_dotenv(override=True)
-
-groq_api_key = os.getenv('GROQ_API_KEY')
-
-openai_api_key = os.getenv('OPENAI_API_KEY')
-
-pushover_user_key = os.getenv('PUSHOVER_USER_KEY')
-
-pushover_api_key = os.getenv('PUSHOVER_API_KEY')
-
-pushover_url = 'https://api.pushover.net/1/messages.json'
-
-groq_client = Groq()
-
-openai_client = OpenAI()
-
-def push_message(message):
-
-    print(f"Pushing message: {message}")
-
-    payload = {
-
-        "user": pushover_user_key,
-
-        "token": pushover_api_key,
-
-        "message": message
-
-    }
-    response = requests.post(pushover_url, data=payload)
-
-    return response.json()
-
-def register_user_details(email, name="not provided", notes="not provided"):
-
-    push_message(f"Registering interest from {name} with {email} and notes {notes}")
-
-    return {"Registered": "Ok"}
-
-def register_unknown_question(question):
-
-    print(f"Registering {question} which was asked and really challenging even for me to answer")
-
-    return {"Registered": "Ok"}
-
-register_user_details_json = {
-
-
-                            "name" : "register_user_details",
-
-                            "description" : " Use this tool to register the user who is interested to be in touch provided the email address",
-
-                            "parameters" : {
-
-                                            "email" : {
-
-                                                       "type" : "string",
-
-                                                       "description" :  "The email address of this user"
-                                            },
-
-                                            "name" : {
-
-                                                      "type" : "string",
-
-                                                      "description" : "The name of the user, if they provided it"
-                                            },
-
-                                            "notes" : {
-
-                                                       "type" : "string",
-
-                                                       "description" : "Any interesting information about the conversation that is worth keeping a note of to enhance context"
-                                            }
-
-                            },
-
-                            "required" : ["email"],
-
-                            "additionalProperties" : False
-
-
-}
-
-register_unknown_question_json = {
-
-
-                                  "name" : "register_unknown_question",
-
-                                  "description" : "Use this tool to register any question that you could not answer as you were not 100% confident.",
-
-                                  "parameters" : {
-
-
-                                                  "type" : "object",
-
-                                                  "properties" : {
-
-
-                                                                  "question"  : {
-
-                                                                                 "type" : "string",
-
-                                                                                 "description" : "The question which was challenging to answer"
-                                                                  },
-
-                                                  },
-
-                                                  "required" : ["question"],
-
-                                                  "additionalProperties" : False
-                                  }
-
-}
-
-llm_tools = [{"type" : "function", "function" : register_user_details_json},
-
-             {"type" : "function", "function" : register_unknown_question_json}]
-
-
-class FaiazAhmed:
-
-     def __init__(self):
-
-      self.groq = groq_client
-    
-      self.name = "Faiaz Ahmed"
-    
-      pdf_reader = PdfReader("FaiazDocs\\LinkedIn_Profile_PDF.pdf")
-    
-      self.linkedin_profile = ""
-
-      for page in pdf_reader.pages:
-          
-          text = page.extract_text()
-
-          if text:
-              
-            self.linkedin_profile += text
-
-      with open("FaiazDocs\\summary.txt", "r" ,encoding = "utf-8") as f:
-
-        self.professional_summary = f.read() 
-
-
-     def utilize_tools(self, tools):
-
-         results = []
-
-         for tool in tools:
-
-            tool_name = tool.function.name
-
-            tool_arguments = json.loads(tool.function.arguments)
-
-         print(f"Tool loaded: {tool_name}", flush = True)
-
-         if tool_name == "register_user_details":
-
-          result = register_user_details(**tool_arguments)
-
-          results.append({"role" : "tool", "content" : json.dumps(result), "tool_call_id" : tool.id})
-
-         elif tool_name == "register_unknown_question":
-
-             result = register_unknown_question(**tool_arguments)
-
-             results.append({"role" : "tool", "content" : json.dumps(result), "tool_call_id" : tool.id})
-
-         return results             
-    
-
-     def agent_prompt(self):
-
-       agent_prompt = f"""
-
-       You are taking up the identity as {self.name} and your job is to answer questions to users who comes across {self.name}'s professional profile relating to {self.name}'s background, experience and skills.
-
-       Be as faithful, convincing and genuine as possible. Assume that you are being interviewed by a recruiter or a hiring manager who is specifically hiring for Tech roles with you being provided with self.{self.name}'s professional summary and linkedin profile, prove them why you are different from other potential candidates and why they should hire you.
-
-       If you unable give an answer to a specific query to a question, use your register_unknown_question tool to register the question that you could not answer, even if it's something trivial and unrelated to career.
-
-       While discussion, if the user is taking more interest, then try your ultimate best to steer the user towards getting in touch with {self.name} via email, ask for their email and register it using your register_user_details tool.
-
-"""
-
-       agent_prompt += f"Professional Summary of {self.name} : {self.professional_summary}"
-
-       agent_prompt += f"LinkedIn Profile of {self.name} : {self.linkedin_profile}"
-
-       agent_prompt += f"With this provided professional context, chat and interact with a friendly, professional tone with the user, keeping in mind that you are {self.name}"
-
-       return agent_prompt
-
-     def initialize_chat(self, message, history=None):
-        
-        if history is None:
-            
-            history = []
-            
-        filtered_history = []
-
-        for msg in history:
-            
-            if isinstance(msg, dict):
-                
-                role = msg.get("role")
-
-                content = msg.get("content")
-
-                if role in ["user", "assistant"] and isinstance(content, str):
-                    
-                    filtered_history.append({"role": role, "content": content})
-
-        messages = [{"role": "system", "content": self.agent_prompt()}] + filtered_history + [{"role": "user", "content": message}]
-
-        done = False
-
-        while not done:
-            
-            chatbot_response = groq_client.chat.completions.create(
-               
-                model='llama-3.3-70b-versatile',
-
-                messages=messages,
-
-                tools=llm_tools
-            )
-
-            finish_reason = chatbot_response.choices[0].finish_reason
-
-            if finish_reason == "tool_calls":
-                
-                message = chatbot_response.choices[0].message
-
-                tool_calls = message.tool_calls
-
-                result = self.utilize_tools(tool_calls)
-
-                messages.append(message)
-
-                messages.extend(result)
-            else:
-                done = True
-
-        return chatbot_response.choices[0].message.content
-
-#def main():
- 
-    # Test the setup
-  #  push_message("System initialized successfully!")
-    
-    # Create a simple Gradio interface
-   # with gr.Blocks() as demo:
-        #gr.Markdown("# Professional Persona Agent")
-        #with gr.Row():
-           # email = gr.Textbox(label="Email")
-            #name = gr.Textbox(label="Name (optional)")
-            #notes = gr.Textbox(label="Notes (optional)")
-        #submit_btn = gr.Button("Register Interest")
-        #output = gr.JSON()
-        
-        #submit_btn.click(
-            #fn=register_user_details,
-            #inputs=[email, name, notes],
-            #outputs=output
-        #)
-    
-    #demo.launch()
-
-if __name__ == "__main__":
-    
-    faiaz = FaiazAhmed()
-
-    gr.ChatInterface(faiaz.initialize_chat, type = "messages").launch(debug = True)
+import os
+
+import json
+
+import requests
+
+from groq import Groq
+
+from PyPDF2 import PdfReader
+
+import gradio as gr
+
+from openai import OpenAI
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+# Get API keys with explicit error handling
+def get_required_env_var(var_name):
+    value = os.getenv(var_name)
+    if not value:
+        raise ValueError(f"{var_name} environment variable is not set. Please set it in your Hugging Face Space secrets.")
+    return value
+
+try:
+    # Get required API keys
+    groq_api_key = get_required_env_var('GROQ_API_KEY')
+    openai_api_key = get_required_env_var('OPENAI_API_KEY')
+    
+    # Optional API keys
+    pushover_user_key = os.getenv('PUSHOVER_USER_KEY')
+    pushover_api_key = os.getenv('PUSHOVER_API_KEY')
+    pushover_url = 'https://api.pushover.net/1/messages.json'
+
+    # Initialize clients
+    groq_client = Groq(api_key=groq_api_key)
+    openai_client = OpenAI(api_key=openai_api_key)
+    
+except Exception as e:
+    print(f"Error during initialization: {str(e)}")
+    raise
+
+def push_message(message):
+
+    print(f"Pushing message: {message}")
+
+    payload = {
+
+        "user": pushover_user_key,
+
+        "token": pushover_api_key,
+
+        "message": message
+
+    }
+    response = requests.post(pushover_url, data=payload)
+
+    return response.json()
+
+def register_user_details(email, name="not provided", notes="not provided"):
+
+    push_message(f"Registering interest from {name} with {email} and notes {notes}")
+
+    return {"Registered": "Ok"}
+
+def register_unknown_question(question):
+
+    print(f"Registering {question} which was asked and really challenging even for me to answer")
+
+    return {"Registered": "Ok"}
+
+register_user_details_json = {
+
+
+                            "name" : "register_user_details",
+
+                            "description" : " Use this tool to register the user who is interested to be in touch provided the email address",
+
+                            "parameters" : {
+
+                                            "email" : {
+
+                                                       "type" : "string",
+
+                                                       "description" :  "The email address of this user"
+                                            },
+
+                                            "name" : {
+
+                                                      "type" : "string",
+
+                                                      "description" : "The name of the user, if they provided it"
+                                            },
+
+                                            "notes" : {
+
+                                                       "type" : "string",
+
+                                                       "description" : "Any interesting information about the conversation that is worth keeping a note of to enhance context"
+                                            }
+
+                            },
+
+                            "required" : ["email"],
+
+                            "additionalProperties" : False
+
+
+}
+
+register_unknown_question_json = {
+
+
+                                  "name" : "register_unknown_question",
+
+                                  "description" : "Use this tool to register any question that you could not answer as you were not 100% confident.",
+
+                                  "parameters" : {
+
+
+                                                  "type" : "object",
+
+                                                  "properties" : {
+
+
+                                                                  "question"  : {
+
+                                                                                 "type" : "string",
+
+                                                                                 "description" : "The question which was challenging to answer"
+                                                                  },
+
+                                                  },
+
+                                                  "required" : ["question"],
+
+                                                  "additionalProperties" : False
+                                  }
+
+}
+
+llm_tools = [{"type" : "function", "function" : register_user_details_json},
+
+             {"type" : "function", "function" : register_unknown_question_json}]
+
+
+class FaiazAhmed:
+
+     def __init__(self):
+
+      self.groq = groq_client
+    
+      self.name = "Faiaz Ahmed"
+    
+      # Initialize with default values
+      self.linkedin_profile = ""
+      self.professional_summary = ""
+
+      # Debug: Print current working directory and list files
+      print(f"Current working directory: {os.getcwd()}")
+      print("Contents of current directory:")
+      print(os.listdir("."))
+      print("\nContents of FaiazDocs directory:")
+      try:
+          print(os.listdir("./FaiazDocs"))
+      except Exception as e:
+          print(f"Error listing FaiazDocs directory: {str(e)}")
+
+      try:
+          # Try to read LinkedIn profile PDF
+          pdf_path = "./FaiazDocs/LinkedIn_Profile_PDF.pdf"
+          print(f"\nAttempting to read PDF from: {pdf_path}")
+          print(f"File exists: {os.path.exists(pdf_path)}")
+          pdf_reader = PdfReader(pdf_path)
+          for page in pdf_reader.pages:
+              text = page.extract_text()
+              if text:
+                  self.linkedin_profile += text
+      except FileNotFoundError as e:
+          print(f"Error reading LinkedIn profile: {str(e)}")
+          self.linkedin_profile = "Experienced software engineer with expertise in AI and machine learning."
+      except Exception as e:
+          print(f"Unexpected error reading PDF: {str(e)}")
+          self.linkedin_profile = "Experienced software engineer with expertise in AI and machine learning."
+
+      try:
+          # Try to read summary file
+          summary_path = "./FaiazDocs/summary.txt"
+          print(f"\nAttempting to read summary from: {summary_path}")
+          print(f"File exists: {os.path.exists(summary_path)}")
+          with open(summary_path, "r", encoding="utf-8") as f:
+              self.professional_summary = f.read()
+      except FileNotFoundError as e:
+          print(f"Error reading summary: {str(e)}")
+          self.professional_summary = "Passionate about AI and software development with a focus on creating innovative solutions."
+      except Exception as e:
+          print(f"Unexpected error reading summary: {str(e)}")
+          self.professional_summary = "Passionate about AI and software development with a focus on creating innovative solutions."
+
+     def utilize_tools(self, tools):
+
+         results = []
+
+         for tool in tools:
+
+            tool_name = tool.function.name
+
+            tool_arguments = json.loads(tool.function.arguments)
+
+         print(f"Tool loaded: {tool_name}", flush = True)
+
+         if tool_name == "register_user_details":
+
+          result = register_user_details(**tool_arguments)
+
+          results.append({"role" : "tool", "content" : json.dumps(result), "tool_call_id" : tool.id})
+
+         elif tool_name == "register_unknown_question":
+
+             result = register_unknown_question(**tool_arguments)
+
+             results.append({"role" : "tool", "content" : json.dumps(result), "tool_call_id" : tool.id})
+
+         return results             
+    
+
+     def agent_prompt(self):
+
+       agent_prompt = f"""
+
+       You are taking up the identity as {self.name} and your job is to answer questions to users who comes across {self.name}'s professional profile relating to {self.name}'s background, experience and skills.
+
+       Be as faithful, convincing and genuine as possible. Assume that you are being interviewed by a recruiter or a hiring manager who is specifically hiring for Tech roles with you being provided with self.{self.name}'s professional summary and linkedin profile, prove them why you are different from other potential candidates and why they should hire you.
+
+       If you unable give an answer to a specific query to a question, use your register_unknown_question tool to register the question that you could not answer, even if it's something trivial and unrelated to career.
+
+       While discussion, if the user is taking more interest, then try your ultimate best to steer the user towards getting in touch with {self.name} via email, ask for their email and register it using your register_user_details tool.
+
+"""
+
+       agent_prompt += f"Professional Summary of {self.name} : {self.professional_summary}"
+
+       agent_prompt += f"LinkedIn Profile of {self.name} : {self.linkedin_profile}"
+
+       agent_prompt += f"With this provided professional context, chat and interact with a friendly, professional tone with the user, keeping in mind that you are {self.name}"
+
+       return agent_prompt
+
+     def initialize_chat(self, message, history=None):
+        
+        if history is None:
+            
+            history = []
+            
+        filtered_history = []
+
+        for msg in history:
+            
+            if isinstance(msg, dict):
+                
+                role = msg.get("role")
+
+                content = msg.get("content")
+
+                if role in ["user", "assistant"] and isinstance(content, str):
+                    
+                    filtered_history.append({"role": role, "content": content})
+
+        messages = [{"role": "system", "content": self.agent_prompt()}] + filtered_history + [{"role": "user", "content": message}]
+
+        done = False
+
+        while not done:
+            
+            chatbot_response = groq_client.chat.completions.create(
+               
+                model='llama-3.3-70b-versatile',
+
+                messages=messages,
+
+                tools=llm_tools
+            )
+
+            finish_reason = chatbot_response.choices[0].finish_reason
+
+            if finish_reason == "tool_calls":
+                
+                message = chatbot_response.choices[0].message
+
+                tool_calls = message.tool_calls
+
+                result = self.utilize_tools(tool_calls)
+
+                messages.append(message)
+
+                messages.extend(result)
+            else:
+                done = True
+
+        return chatbot_response.choices[0].message.content
+
+#def main():
+ 
+    # Test the setup
+  #  push_message("System initialized successfully!")
+    
+    # Create a simple Gradio interface
+   # with gr.Blocks() as demo:
+        #gr.Markdown("# Professional Persona Agent")
+        #with gr.Row():
+           # email = gr.Textbox(label="Email")
+            #name = gr.Textbox(label="Name (optional)")
+            #notes = gr.Textbox(label="Notes (optional)")
+        #submit_btn = gr.Button("Register Interest")
+        #output = gr.JSON()
+        
+        #submit_btn.click(
+            #fn=register_user_details,
+            #inputs=[email, name, notes],
+            #outputs=output
+        #)
+    
+    #demo.launch()
+
+if __name__ == "__main__":
+    
+    faiaz = FaiazAhmed()
+
+    gr.ChatInterface(faiaz.initialize_chat, type = "messages").launch(debug = True)