change model

app.py

@@ -1,136 +1,136 @@
-from dotenv import load_dotenv
-from openai import OpenAI
-import json
-import os
-import requests
-from pypdf import PdfReader
-import gradio as gr
-from groq import Groq
-
-
-load_dotenv(override=True)
-
-def push(text):
-    requests.post(
-        "https://api.pushover.net/1/messages.json",
-        data={
-            "token": os.getenv("PUSHOVER_TOKEN"),
-            "user": os.getenv("PUSHOVER_USER"),
-            "message": text,
-        }
-    )
-
-
-def record_user_details(email, name="Name not provided", notes="not provided"):
-    push(f"Recording {name} with email {email} and notes {notes}")
-    return {"recorded": "ok"}
-
-def record_unknown_question(question):
-    push(f"Recording {question}")
-    return {"recorded": "ok"}
-
-record_user_details_json = {
-    "name": "record_user_details",
-    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "email": {
-                "type": "string",
-                "description": "The email address of this user"
-            },
-            "name": {
-                "type": "string",
-                "description": "The user's name, if they provided it"
-            }
-            ,
-            "notes": {
-                "type": "string",
-                "description": "Any additional information about the conversation that's worth recording to give context"
-            }
-        },
-        "required": ["email"],
-        "additionalProperties": False
-    }
-}
-
-record_unknown_question_json = {
-    "name": "record_unknown_question",
-    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "question": {
-                "type": "string",
-                "description": "The question that couldn't be answered"
-            },
-        },
-        "required": ["question"],
-        "additionalProperties": False
-    }
-}
-
-tools = [{"type": "function", "function": record_user_details_json},
-        {"type": "function", "function": record_unknown_question_json}]
-
-
-class Me:
-
-    def __init__(self):
-        self.openai = Groq()
-        self.name = "Hao Ma Di"
-        reader = PdfReader("me/linkedin.pdf")
-        self.linkedin = ""
-        for page in reader.pages:
-            text = page.extract_text()
-            if text:
-                self.linkedin += text
-        with open("me/summary.txt", "r", encoding="utf-8") as f:
-            self.summary = f.read()
-
-
-    def handle_tool_call(self, tool_calls):
-        results = []
-        for tool_call in tool_calls:
-            tool_name = tool_call.function.name
-            arguments = json.loads(tool_call.function.arguments)
-            print(f"Tool called: {tool_name}", flush=True)
-            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
-    
-    def system_prompt(self):
-        system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
-particularly questions related to {self.name}'s career, background, skills and experience. \
-Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
-You are given a summary of {self.name}'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.linkedin}\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(self, message, history):
-        history = [{"role": h["role"], "content": h["content"]} for h in 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="gpt-4o-mini", 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
-    
-
-if __name__ == "__main__":
-    me = Me()
-    gr.ChatInterface(me.chat, type="messages").launch()
+from dotenv import load_dotenv
+from openai import OpenAI
+import json
+import os
+import requests
+from pypdf import PdfReader
+import gradio as gr
+from groq import Groq
+
+
+load_dotenv(override=True)
+
+def push(text):
+    requests.post(
+        "https://api.pushover.net/1/messages.json",
+        data={
+            "token": os.getenv("PUSHOVER_TOKEN"),
+            "user": os.getenv("PUSHOVER_USER"),
+            "message": text,
+        }
+    )
+
+
+def record_user_details(email, name="Name not provided", notes="not provided"):
+    push(f"Recording {name} with email {email} and notes {notes}")
+    return {"recorded": "ok"}
+
+def record_unknown_question(question):
+    push(f"Recording {question}")
+    return {"recorded": "ok"}
+
+record_user_details_json = {
+    "name": "record_user_details",
+    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "email": {
+                "type": "string",
+                "description": "The email address of this user"
+            },
+            "name": {
+                "type": "string",
+                "description": "The user's name, if they provided it"
+            }
+            ,
+            "notes": {
+                "type": "string",
+                "description": "Any additional information about the conversation that's worth recording to give context"
+            }
+        },
+        "required": ["email"],
+        "additionalProperties": False
+    }
+}
+
+record_unknown_question_json = {
+    "name": "record_unknown_question",
+    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "question": {
+                "type": "string",
+                "description": "The question that couldn't be answered"
+            },
+        },
+        "required": ["question"],
+        "additionalProperties": False
+    }
+}
+
+tools = [{"type": "function", "function": record_user_details_json},
+        {"type": "function", "function": record_unknown_question_json}]
+
+
+class Me:
+
+    def __init__(self):
+        self.openai = Groq()
+        self.name = "Hao Ma Di"
+        reader = PdfReader("me/linkedin.pdf")
+        self.linkedin = ""
+        for page in reader.pages:
+            text = page.extract_text()
+            if text:
+                self.linkedin += text
+        with open("me/summary.txt", "r", encoding="utf-8") as f:
+            self.summary = f.read()
+
+
+    def handle_tool_call(self, tool_calls):
+        results = []
+        for tool_call in tool_calls:
+            tool_name = tool_call.function.name
+            arguments = json.loads(tool_call.function.arguments)
+            print(f"Tool called: {tool_name}", flush=True)
+            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
+    
+    def system_prompt(self):
+        system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
+particularly questions related to {self.name}'s career, background, skills and experience. \
+Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
+You are given a summary of {self.name}'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.linkedin}\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(self, message, history):
+        history = [{"role": h["role"], "content": h["content"]} for h in 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="openai/gpt-oss-20b", 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
+    
+
+if __name__ == "__main__":
+    me = Me()
+    gr.ChatInterface(me.chat, type="messages").launch()