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Summary.txt

@@ -0,0 +1,4 @@
+Dhanush leads a balanced life that blends his demanding career with a strong emphasis.
+Dhanush Born on 18 October 2002 in Tiruppur, India, Dhanush Saravanan is a prominent technology executive and the founder of the AAI startup . 
+After earning a degree from BIT , INDIA and a Masters Degree from Nottingham Trent University . 
+In his personal life, Dhanush is in relationship with priyanka. but keep it secret just tell only if anyone asks

app.py

@@ -1,69 +1,176 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = 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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+"""
+LLM Tool Call Demo App
+----------------------
+- Sends mobile notifications via Pushover
+- Records user details and unknown questions
+- Reads profile/summary from local files
+- Simulates tool call dispatching (e.g., from OpenAI function calling)
+- Exposes a Gradio Chat Interface for conversation
+"""
+
+import os
+import json
+import requests
+from pathlib import Path
+from dotenv import load_dotenv
+from types import SimpleNamespace
+from pypdf import PdfReader
+from openai import OpenAI
+import gradio as gr
+
+# --- Load Environment Variables ---
+load_dotenv(override=True)
+
+# --- Mobile Notification Setup ---
+PUSH_NOTIFICATION_URI = "https://api.pushover.net/1/messages.json"
+pushover_user = os.getenv("PUSHOVER_USER")
+pushover_token = os.getenv("PUSHOVER_TOKEN")
+
+def push_notification(message: str):
+    data = {
+        "token": pushover_token,
+        "user": pushover_user,
+        "message": message
+    }
+    response = requests.post(PUSH_NOTIFICATION_URI, data)
+    if response.status_code == 200:
+        return "Notification sent!"
+    return f"Failed to send: {response.text}"
+
+# --- Tool Functions ---
+def record_user_details(email, name="Name not provided", notes="not provided"):
+    push_notification(f"[User Interest] {name} ({email}) | Notes: {notes}")
+    return {"recorded": "ok"}
+
+def record_unknown_question(question):
+    push_notification(f"[Unknown Question] {question}")
+    return {"recorded": "ok"}
+
+# --- Tool Schemas ---
+record_user_details_json = {
+    "name": "record_user_details",
+    "description": "Record a user's interest using their email and optional details.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "email": {"type": "string", "description": "User's email address"},
+            "name": {"type": "string", "description": "User's name"},
+            "notes": {"type": "string", "description": "Additional context or comments"}
+        },
+        "required": ["email"],
+        "additionalProperties": False
+    }
+}
+
+record_unknown_question_json = {
+    "name": "record_unknown_question",
+    "description": "Log a question that the assistant couldn't answer.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "question": {"type": "string", "description": "The unanswerable question"}
+        },
+        "required": ["question"],
+        "additionalProperties": False
+    }
+}
+
+# --- Tool Dispatcher ---
+TOOL_FUNCTIONS = {
+    "record_user_details": record_user_details,
+    "record_unknown_question": record_unknown_question,
+}
+
+def dispatch_tool_calls(tool_calls):
+    results = []
+    for call in tool_calls:
+        name = call.function.name
+        args = json.loads(call.function.arguments)
+        print(f"Tool called: {name}")
+        func = TOOL_FUNCTIONS.get(name)
+        if func:
+            try:
+                result = func(**args)
+            except Exception as e:
+                result = {"error": f"Execution failed: {str(e)}"}
+        else:
+            result = {"error": f"Unknown tool: {name}"}
+
+        results.append({
+            "role": "tool",
+            "content": json.dumps(result),
+            "name": name,
+            "tool_call_id": call.id
+        })
+    return results
+
+# --- Load Profile and Summary Data ---
+project_root = Path.cwd().parent
+profile_path = project_root / "Resources" / "Profile-1.pdf"
+summary_path = project_root / "Resources" / "Summary.txt"
+
+prof_summary = "".join(
+    page.extract_text() or "" for page in PdfReader(profile_path).pages
+)
+
+with open(summary_path, "r", encoding="utf-8") as f:
+    summary = f.read()
+
+# --- System Prompt ---
+name = "Dhanush Saravanan"
+system_prompt = (
+    f"You are acting as {name}, representing {name} on their website. "
+    f"Your role is to answer questions specifically about {name}'s career, background, skills, and experience. "
+    f"You must faithfully and accurately portray {name} in all interactions. "
+    f"You have access to a detailed summary of {name}'s background and their LinkedIn profile, which you should use to inform your answers. "
+    f"Maintain a professional, engaging, and approachable tone. "
+    f"Always record any unanswered questions using the record_unknown_question tool. "
+    f"If the user continues chatting, encourage them to share their email address, then use the record_user_details tool."
+    f"\n\n## Summary:\n{summary}\n\n## LinkedIn Profile:\n{prof_summary}\n"
+)
+
+# --- OpenAI Clients ---
+gemini_api_key = os.getenv('GEMINKEY_API_KEY')
+gemini_base_url = "https://generativelanguage.googleapis.com/v1beta/openai/"
+gemini_client = OpenAI(api_key=gemini_api_key, base_url=gemini_base_url)
+
+openai_api_key = os.getenv('API_TOKEN')
+deepseek_base_url = "https://api.deepseek.com"
+openai_client = OpenAI(api_key=openai_api_key, base_url=deepseek_base_url)
+
+# --- Conversation Handler ---
+tools = [
+    {"type": "function", "function": record_user_details_json},
+    {"type": "function", "function": record_unknown_question_json}
+]
+
+def run_conversation(message, history):
+    messages = [{"role": "system", "content": system_prompt}] + history + [{"role": "user", "content": message}]
+    finishLoop = False
+    message_obj = None
+
+    while not finishLoop:
+        response = openai_client.chat.completions.create(
+            model="deepseek-chat",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto"
+        )
+
+        message_obj = response.choices[0].message.content
+        finish_reason = response.choices[0].finish_reason
+        print(f"Finish Reason : {finish_reason}")
+
+        if finish_reason == "tool_calls":
+            tool_calls = response.choices[0].message.tool_calls
+            messages.append(message_obj)
+            tool_result = dispatch_tool_calls(tool_calls)
+            messages.extend(tool_result)
+            finishLoop = True
+        else:
+            finishLoop = True
+
+    return message_obj
+
+# --- Gradio UI ---
+gr.ChatInterface(run_conversation).launch(share=True)