Update app.py

app.py

@@ -38,107 +38,84 @@ def record_unknown_question(question):
         writer.writerow([question])
     return {"recorded": "ok"}
 
-# Define tools for the LLM
+
+    
+# JSON definitions for tools
+record_user_details_json = {
+    "name": "record_user_details",
+    "description": "Record user info when they provide email",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "email": {"type": "string", "description": "The user's email"},
+            "name": {"type": "string", "description": "User's name"},
+            "notes": {"type": "string", "description": "Extra info"}
+        },
+        "required": ["email"],
+        "additionalProperties": False
+    }
+}
+
+record_unknown_question_json = {
+    "name": "record_unknown_question",
+    "description": "Record any unanswered question",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "question": {"type": "string", "description": "The question not answered"},
+        },
+        "required": ["question"],
+        "additionalProperties": False
+    }
+}
+
 tools = [
-    {"name": "record_user_details", "description": "Record user info when they provide email", "func": record_user_details},
-    {"name": "record_unknown_question", "description": "Record any unanswered question", "func": record_unknown_question},
+    {"type": "function", "function": record_user_details_json},
+    {"type": "function", "function": record_unknown_question_json}
 ]
 
 class Me:
-    def __init__(self):
-        self.openai = gemini
-        self.name = "SnehaLeela"
-
-        # Load profile JSON
-        with open("profile.json", "r", encoding="utf-8") as f:
-            self.profile = json.load(f)
-
-        # Set attributes for easier access
-        self.personal_info = self.profile.get("personal_info", {})
-        self.expertise = self.profile.get("expertise", [])
-        self.experience = self.profile.get("experience", [])
-        self.education = self.profile.get("education", [])
-        self.friends = self.profile.get("friends", [])
-
     # Handle tool calls
     def handle_tool_call(self, tool_calls):
         results = []
-        for call in tool_calls:
-            # Find the corresponding Python function
-            func = next((t["func"] for t in tools if t["name"] == call.function.name), None)
-            if func:
-                args = json.loads(call.function.arguments)
-                result = func(**args)
-                results.append({"role": "tool", "content": json.dumps(result), "tool_call_id": call.id})
+        for tool_call in tool_calls:
+            tool_name = tool_call.function.name
+            arguments = json.loads(tool_call.function.arguments)
+            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
 
     # System prompt for LLM
-    def system_prompt(self):
-        # Combine experience into text
-        experience_text = ""
-        for company in self.experience:
-            experience_text += f"{company['company']}"
-            if 'location' in company:
-                experience_text += f" ({company['location']})"
-            for role in company.get('roles', []):
-                experience_text += f"\n- {role['title']} ({role.get('years', '')})"
-                for hl in role.get('highlights', []):
-                    experience_text += f"\n  • {hl}"
-            experience_text += "\n"
-
         expertise_text = ", ".join(self.expertise)
 
         education_text = ""
-        if self.education:
+        if hasattr(self, 'education') and self.education:
             highest = self.education[0].get("highest_degree", {})
             education_text = f"{highest.get('degree','')} in {highest.get('field_of_study','')} from {highest.get('university','')} ({highest.get('start_year','')}–{highest.get('end_year','')})"
-
+        
+        # Optional: prepare friends text for fun
         friends_text = ""
-        if self.friends:
+        if hasattr(self, 'friends') and self.friends:
             friends_list = []
             for f in self.friends:
                 friends_list.append(f"{f.get('Name','')} ({f.get('Company','')}): {f.get('Description','')}")
             friends_text = "\n".join(friends_list)
-
+        
         system_prompt = (
-            f"You are acting as {self.personal_info.get('name','')} (aka {self.personal_info.get('nickname','')}). "
-            f"Answer questions about {self.personal_info.get('name','')}'s career, background, skills, and experience. "
-            f"Represent {self.personal_info.get('name','')} faithfully. "
+            f"You are acting as {self.personal_info['name']} (aka {self.personal_info.get('nickname','')}). "
+            f"Answer questions about {self.personal_info['name']}'s career, background, skills, and experience. "
+            f"Represent {self.personal_info['name']} faithfully. "
             f"If you don't know an answer, use record_unknown_question tool. "
             f"If the user engages in discussion, try to steer them towards providing their email using record_user_details tool.\n\n"
-            f"## Summary:\n{self.personal_info.get('summary','')}\n\n"
+            f"## Summary:\n{self.personal_info['summary']}\n\n"
             f"## Interests:\n{', '.join(self.personal_info.get('personal_interests', []))}\n\n"
             f"## Travel History:\n{', '.join(self.personal_info.get('travel_history', []))}\n\n"
             f"## Education:\n{education_text}\n\n"
-            f"## Expertise:\n{expertise_text}\n\n"
             f"## Experience:\n{experience_text}\n\n"
             f"## Friends (for fun):\n{friends_text}\n\n"
             f"## LinkedIn Profile:\nhttps://www.linkedin.com/in/sneha-leela-0a450349/\n\n"
-            f"Chat with the user staying in character as {self.personal_info.get('name','')}."
+            f"Chat with the user staying in character as {self.personal_info['name']}."
         )
-        return system_prompt
-
-    # Main chat function
-    def chat(self, message, history):
-        messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
-        done = False
-        while not done:
-            response = self.openai.chat.completions.create(
-                model="gemini-2.5-flash-preview-05-20",
-                messages=messages,
-                tools=tools
-            )
-            if response.choices[0].finish_reason == "tool_calls":
-                message = response.choices[0].message
-                tool_calls = message.tool_calls
-                results = self.handle_tool_call(tool_calls)
-                messages.append(message)
-                messages.extend(results)
-            else:
-                done = True
-        return response.choices[0].message.content
-
-# Launch Gradio interface
-if __name__ == "__main__":
-    me = Me()
-    gr.ChatInterface(me.chat, type="messages").launch(share=True)
+        return system_prompt