Update app.py

app.py

@@ -1,6 +1,6 @@
 # app.py — NVIDIA NIM + Tool Calling + Gradio Chatbot
-# Model: meta/llama3-8b-instruct  (supports OpenAI-style tools)
-# Works on HuggingFace Spaces with your nvapi-... key.
+# Model: meta/llama3-8b-instruct (supports OpenAI-style tools)
+# Works with your nvapi-xxxx key on HuggingFace Spaces.
 
 import os
 import json
@@ -12,7 +12,7 @@ import gradio as gr
 # ===============================
 # ENVIRONMENT CONFIG
 # ===============================
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")   # Your nvapi-XXXX key
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")   # your nvapi-xxxx key
 BASE_URL = "https://integrate.api.nvidia.com/v1"
 MODEL = "meta/llama3-8b-instruct"
 
@@ -47,14 +47,19 @@ def push(text):
 # TOOL IMPLEMENTATIONS
 # ===============================
 def record_user_details(email, name="Name not provided", notes="not provided"):
-    push(f"New lead → {name} | {email} | Notes: {notes}")
+    push(f"Lead captured → {name} | {email} | Notes: {notes}")
     return {"status": "ok", "email": email, "name": name, "notes": notes}
 
 def record_unknown_question(question):
-    push(f"Unknown question recorded: {question}")
+    push(f"Unknown question: {question}")
     return {"status": "ok", "question": question}
 
 
+# Register tools globally
+globals()["record_user_details"] = record_user_details
+globals()["record_unknown_question"] = record_unknown_question
+
+
 # ===============================
 # TOOL JSON DEFINITIONS
 # ===============================
@@ -63,27 +68,27 @@ tools = [
         "type": "function",
         "function": {
             "name": "record_user_details",
-            "description": "Record user interest and their email.",
+            "description": "Record user's interest and email.",
             "parameters": {
                 "type": "object",
                 "properties": {
                     "email": {"type": "string"},
                     "name": {"type": "string"},
-                    "notes": {"type": "string"}
+                    "notes": {"type": "string"},
                 },
                 "required": ["email"]
             }
-        },
+        }
     },
     {
         "type": "function",
         "function": {
             "name": "record_unknown_question",
-            "description": "Record any question the assistant could not answer.",
+            "description": "Record any question the assistant cannot answer.",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "question": {"type": "string"}
+                    "question": {"type": "string"},
                 },
                 "required": ["question"]
             }
@@ -91,9 +96,6 @@ tools = [
     }
 ]
 
-globals()["record_user_details"] = record_user_details
-globals()["record_unknown_question"] = record_unknown_question
-
 
 # ===============================
 # MAIN ASSISTANT CLASS
@@ -104,11 +106,11 @@ class Me:
         self.summary = ""
         self.linkedin_text = ""
 
-        # Load summary text
+        # Load summary file
         if os.path.exists("me/summary.txt"):
             self.summary = open("me/summary.txt", "r", encoding="utf-8").read()
 
-        # Load LinkedIn PDF (optional)
+        # Load PDF if exists
         pdf_path = "me/Ayush_linkdin.pdf"
         if os.path.exists(pdf_path):
             text = []
@@ -121,21 +123,21 @@ class Me:
 
     def system_prompt(self):
         return f"""
-You are acting as {self.name}. Answer questions professionally about Ayush's skills,
-career, background, and experience.
+You are acting as {self.name}. You answer questions about his background,
+skills, experience, portfolio, and career.
 
-If you DON'T know something → call the tool:
-- record_unknown_question
+If you DO NOT know something:
+➡ Use the tool: record_unknown_question
 
-If the user shows interest → ask for an email and call:
-- record_user_details
+If the user is interested in working with Ayush:
+➡ Ask for their email and call: record_user_details
 
-Be polite, confident, friendly and helpful.
+Be friendly, confident, and professional.
 
 ### Summary:
 {self.summary}
 
-### LinkedIn Data:
+### LinkedIn Extract:
 {self.linkedin_text}
 """
 
@@ -145,12 +147,17 @@ Be polite, confident, friendly and helpful.
     def chat(self, message, history):
         messages = [{"role": "system", "content": self.system_prompt()}]
 
-        # Convert history from Gradio into chat API format
-        for user_msg, bot_msg in history:
-            if user_msg:
-                messages.append({"role": "user", "content": user_msg})
-            if bot_msg:
-                messages.append({"role": "assistant", "content": bot_msg})
+        # FIXED: Handle both tuple-history & dict-history
+        for item in history:
+            if isinstance(item, (list, tuple)) and len(item) == 2:
+                user_msg, bot_msg = item
+                if user_msg:
+                    messages.append({"role": "user", "content": user_msg})
+                if bot_msg:
+                    messages.append({"role": "assistant", "content": bot_msg})
+
+            elif isinstance(item, dict) and "role" in item and "content" in item:
+                messages.append({"role": item["role"], "content": item["content"]})
 
         messages.append({"role": "user", "content": message})
 
@@ -169,6 +176,8 @@ Be polite, confident, friendly and helpful.
 
             # ---- TOOL CALL ----
             if finish == "tool_calls":
+                messages.append(msg.dict())
+
                 for tool_call in msg.tool_calls:
                     func = tool_call.function
                     name = func.name
@@ -177,13 +186,14 @@ Be polite, confident, friendly and helpful.
                     tool_fn = globals().get(name)
                     result = tool_fn(**args)
 
-                    messages.append(msg.dict())  # append tool call request
                     messages.append({
                         "role": "tool",
                         "tool_call_id": tool_call.id,
                         "content": json.dumps(result)
                     })
-                continue  # loop again and let model respond
+
+                # Let model continue after tool execution
+                continue
 
             # ---- NORMAL RESPONSE ----
             return msg.content