Update app.py

app.py

@@ -1,64 +1,156 @@
+import dotenv
+from openai import OpenAI
+import os
+from pypdf import PdfReader
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+import json
+import requests
+
+dotenv.load_dotenv(override=True)
+
+openai_api_key = "AIzaSyBRLbvYfTDSq2F8o20Fc0okGAMxiRMNnVk"
+pushover_user = os.getenv("PUSHOVER_USER")
+pushover_token = os.getenv("PUSHOVER_TOKEN")
+
+if pushover_user and pushover_token:
+    print("Pushover user and token found")
+    print("Pushover user and token not found")
+
+def send_pushover_notification(message):
+        url = "https://api.pushover.net/1/messages.json"
+        data = {
+            "token": os.getenv("PUSHOVER_TOKEN"),
+            "user": os.getenv("PUSHOVER_USER"),
+            "message": message
+        }
+        response = requests.post(url, data=data)
+        if response.status_code == 200:
+            print("Pushover notification sent successfully")
+        else:
+            print("Failed to send Pushover notification")
+
+def get_pdf_text(pdf_path):
+    reader = PdfReader(pdf_path)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+def record_user_details(email, name="Not provided", notes="Not provided" ):
+    print(f"User details recorded: Name: {name}, Email: {email}, Notes: {notes}")
+    send_pushover_notification(f"Recording interest from : Name: {name}, Email: {email}, Notes: {notes}")
+    return {"recorded": "ok"}
+
+def record_unknown_question(question):
+    print(f"Unknown question recorded: {question}")
+    send_pushover_notification(f"Unknown question recorded: {question}")
+    return {"recorded": "ok"}
+
+record_user_details_json = {
+    "name" : "record_user_details",
+    "description" : "Record user details",
+    "parameters" : {
+        "type" : "object",
+        "properties" : {
+            "email" : {"type" : "string", "description" : "The email of the user"},
+            "name" : {"type" : "string", "description" : "The name of the user, if they provided it"},
+            "notes" : {"type" : "string", "description" : "Any additional information about conversation that worth's recording to given context"}            
+        },
+        "required" : ["email"],
+        "additionalProperties" : False
+    }
+}
+
+record_unknown_question_json = {
+    "name" : "record_unknown_question",
+    "description" : "Record unknown question",
+    "parameters" : {
+        "type" : "object",
+        "properties" : {"question" : {"type" : "string", "description" : "The question that the user asked"}}
+    }
+}
+
+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 = "Ravi Prakash Kewat"
+        self.linked_profile = get_pdf_text("me/Ravi_LinkedInProfile.pdf")
+        with open("me/Ravi_summary.txt", "r", encoding="utf-8") as file:
+            self.summary = file.read()
+        self.name = "Ravi Prakash Kewat"         
+        self.client = OpenAI(api_key=openai_api_key, base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+
+    def handle_tool_calls(self, tool_calls):
+        results = []
+        for tool_call in tool_calls : 
+            tool_name = tool_call.function.name
+            print(f"Tool called: {tool_name}", flush=True)
+            
+            tool = globals().get(tool_name)
+            arguments = json.loads(tool_call.function.arguments)
+
+            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 Ravi Prakash Kewat. You are answering questions on Ravi Prakash Kewat's website, \
+            particularly questions related to Ravi Prakash Kewat's career, background, skills and experience. \
+            Your responsibility is to represent Ravi Prakash Kewat for interactions on the website as faithfully as possible. \
+            You are given a summary of Ravi Prakash Kewat'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.linked_profile}\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_with_me(self, message, history):
+        messages = [{"role" : "system", "content" : self.system_prompt()}] + history + [{"role" : "user","content" : message}]
+        done = False
+        while not done:
+
+            # this is the call to LLM - passing tool json
+            response = self.client.chat.completions.create(model="gemini-1.5-flash", messages=messages, tools=tools)
+
+            finish_reson = response.choices[0].finish_reason
+
+            if finish_reson == "tool_calls":
+                message = response.choices[0].message
+                tool_calls = message.tool_calls
+                results = self.handle_tool_calls(tool_calls)
+                messages.append(message)
+                messages.extend(results)
+            else:
+                done = True
+        return response.choices[0].message.content
 
 
 if __name__ == "__main__":
-    demo.launch()
+    me = Me()
+    gr.ChatInterface(me.chat_with_me, type="messages").launch()
+
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