fix prompt add refactor & add gradio interface

.env_template

@@ -1,2 +1,3 @@
 TYPHOON_CHAT_API = *
+DEBUG_MODE = false
 OPENTHAIGPT_CHAT_API = *

.gitignore

@@ -1,3 +1,8 @@
 .venv
 .env
-**/__pycache__/
+**/__pycache__/
+.pytest_cache
+
+# temporary files
+runtime_log.csv
+speedtest.py

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "llm"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

interface.py

@@ -0,0 +1,173 @@
+import gradio as gr
+import requests
+
+API_BASE_URL = "http://127.0.0.1:8000"  # Update this with your actual API base URL if deployed elsewhere
+
+# Function: Get nurse response
+def get_nurse_response(user_input, model_name, chat_history):
+    
+    try:
+        start_time = time.time()
+        # Send user input to the API
+        response = requests.post(
+            f"{API_BASE_URL}/nurse_response",
+            json={"user_input": user_input, "model_name": model_name},
+            timeout=15
+        )
+        response.raise_for_status()
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        nurse_response = response.json().get("nurse_response", "No response received.")
+
+        # Append user and nurse messages to the chat history
+        chat_history.append((f"👤 {user_input}", f"🤖 {nurse_response} ({elapsed_time:.2f} s.)"))
+        return chat_history, ""  # Clear the user input after sending
+    except requests.exceptions.RequestException as e:
+        chat_history.append(("⚠️ Error", str(e)))
+        return chat_history, ""
+
+
+# Function: Reset chat history
+def reset_history():
+    response = requests.post(f"{API_BASE_URL}/reset")
+    return [], "", response.text
+
+# Function: View chat history
+def view_chat_history():
+    try:    
+        response = requests.get(f"{API_BASE_URL}/history")
+        response.raise_for_status()
+        chat_history = response.json().get("chat_history", [])
+        if not chat_history:
+            return "No chat history available."
+        
+        # Properly format chat history for display
+        formatted_history = []
+        for message in chat_history:
+            if message.get("role") == "user":
+                formatted_history.append(f"👤 User: {message.get('content', '')}")
+            elif message.get("role") == "assistant":
+                formatted_history.append(f"🤖 Nurse: {message.get('content', '')}")
+
+        return "\n".join(formatted_history)
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
+
+import json
+import time
+# Function: View EHR details
+def view_ehr_details(view):
+    try:
+        response = requests.get(f"{API_BASE_URL}/details")
+        response.raise_for_status()
+        ehr_data = response.json()
+        if view == "details":
+            ehr_data = ehr_data["ehr_data"]
+        elif view == "prompt":
+            ehr_data.pop("ehr_data", None)
+        ehr_data = json.dumps(ehr_data, indent=4, ensure_ascii=False)
+        return ehr_data
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
+def create_gradio_interface():
+    with gr.Blocks() as interface:
+        # Title and description
+        gr.Markdown(
+            """
+            # MALI_NURSE Gradio Interface
+            ### A User-Friendly Interface to Interact with the MALI_NURSE API
+            Select a model, input your question, and view nurse responses or manage chat history and EHR details.
+            """
+        )
+
+        # Main Input Section
+        with gr.Row():
+           with gr.Row():
+            chat_box = gr.Chatbot(label="Chat with MALI Nurse")
+
+        # Input Section
+        with gr.Row():
+            user_input = gr.Textbox(
+                label="Your Message",
+                placeholder="Type your question or message here...",
+                lines=2,
+            )
+            model_name = gr.Radio(
+                choices=["typhoon-v1.5x-70b-instruct", "openthaigpt", "llama-3.3-70b-versatile"],
+                value="typhoon-v1.5x-70b-instruct",
+                label="Model Selection",
+            )
+            with gr.Column():
+                send_button = gr.Button("Send", variant="primary", size="lg")
+                notification_box = gr.Textbox(interactive=False, lines=2)
+
+        # Bind Get Nurse Response button
+        send_button.click(
+            fn=get_nurse_response,
+            inputs=[user_input, model_name, chat_box],
+            outputs=[chat_box, user_input],  # Update chat box and clear input
+        )
+
+        # Advanced Options
+        with gr.Accordion("Advanced Options", open=False):
+            with gr.Row():
+                reset_button = gr.Button("Reset Data", variant="primary")
+                chat_history_button = gr.Button("View Chat History")
+                ehr_details_button = gr.Button("View EHR Details")
+            with gr.Column():
+                chat_history_output = gr.Textbox(
+                    label="Chat History",
+                    interactive=False,
+                    lines=6,
+                )
+                ehr_details_output = gr.Textbox(
+                    label="EHR Details",
+                    interactive=False,
+                    lines=6,
+                )
+                ehr_prompt_output = gr.Textbox(
+                    label="Outputs",
+                    interactive=False,
+                    lines=6,
+                )
+
+            # Bind buttons to respective functions
+            reset_button.click(
+            fn=reset_history,
+            inputs=[],
+            outputs=[chat_box, user_input, notification_box],  # Clear chat box and input
+        )
+            chat_history_button.click(
+                fn=view_chat_history,
+                inputs=[],
+                outputs=chat_history_output,
+            )
+            # View EHR Details
+            ehr_details_button.click(
+                fn=view_ehr_details,
+                inputs=[gr.Textbox(value="details", visible=False)],
+                outputs=ehr_details_output
+            )
+            ehr_details_button.click(
+                fn=view_ehr_details,
+                inputs=[gr.Textbox(value="prompt", visible=False)],
+                outputs=ehr_prompt_output
+            )            
+
+        # Footer
+        gr.Markdown(
+            """
+            ---  
+            Developed by **Piang** 🚀  
+            Powered by Typhoon v1.5x and OpenThaiGPT Models.  
+            """
+        )
+
+    return interface
+
+# Run the Gradio Interface
+if __name__ == "__main__":
+    gr_interface = create_gradio_interface()
+    gr_interface.launch(server_name="0.0.0.0", server_port=7860)

llm/basemodel.py

@@ -18,8 +18,8 @@ class EHRModel(BaseModel):
     name: Optional[Name] = Field(None, description="Structured name of the patient")
     age: Optional[int] = Field(None, description="The patient's age")
     gender: Optional[str] = Field(None, description="The patient's gender")
-    chief_complaint: Optional[str] = Field(None, description="The main symptom reported by the patient")
-    present_illness: Optional[str] = Field(None, description="Details about the current illness (e.g., when it started, nature of symptoms)")
+    chief_complaint: List[str] = Field(default_factory=list, description="The main symptom reported by the patient")
+    present_illness: List[str] = Field(default_factory=list, description="Details about the current illness (e.g., when it started, nature of symptoms)")
     past_illness: List[str] = Field(default_factory=list, description="Past illnesses, allergies, etc.")
     family_history: List[FamilyHistory] = Field(default_factory=list, description="Health issues in the family")
     personal_history: List[PersonalHistory] = Field(default_factory=list, description="Personal health history (e.g., sleep patterns, medications taken)")

llm/llm.py

@@ -1,37 +1,46 @@
 
+import cProfile
+import pstats
 from langchain_openai.chat_models import ChatOpenAI
+from langchain_groq import ChatGroq
 from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
 from pydantic import ValidationError
 import json
 from pprint import pprint
 from llm.basemodel import EHRModel
 from llm.prompt import field_descriptions, TASK_INSTRUCTIONS, JSON_EXAMPLE
+from llm.models import model_list, get_model
+import time
 
 class VirtualNurseLLM:
-    def __init__(self, base_url, model, api_key):
-        self.client = ChatOpenAI(
-            base_url=base_url,
-            model=model,
-            api_key=api_key
-        )
+    def __init__(self, base_url=None, model_name=None, api_key=None, model_type=None):
+        self.client = None
+        if model_name:
+            self.client = get_model(model_name=model_name)
+        self.model_name = model_name
         self.TASK_INSTRUCTIONS = TASK_INSTRUCTIONS
         self.field_descriptions = field_descriptions
         self.JSON_EXAMPLE = JSON_EXAMPLE
         self.ehr_data = {}
         self.chat_history = []
+        self.chat_history.append({"role": "assistant", "content": "สวัสดีค่ะ ดิฉัน มะลิ เป็นพยาบาลเสมือนที่จะมาดูแลการซักประวัตินะคะ"})
         self.current_patient_response = None
         self.current_context = None
         self.debug = False
         self.current_prompt = None
         self.current_prompt_ehr = None
         self.current_question = None
-
+        
     def create_prompt(self, task_type):
         if task_type == "extract_ehr":
           system_instruction = self.TASK_INSTRUCTIONS.get("extract_ehr")
 
         elif task_type == "question":
           system_instruction = self.TASK_INSTRUCTIONS.get("question")
+
+        elif task_type == "refactor":
+          system_instruction = self.TASK_INSTRUCTIONS.get("refactor")
+
         else:
           raise ValueError("Invalid task type.")
 
@@ -41,7 +50,7 @@ class VirtualNurseLLM:
         prompt = ChatPromptTemplate.from_messages([system_template, user_template])
         return prompt
 
-    def gather_ehr(self, patient_response, max_retries=3):
+    def gather_ehr(self, patient_response, max_retries=2):
         prompt = self.create_prompt("extract_ehr")
         messages = prompt.format_messages(ehr_data=self.ehr_data, patient_response=patient_response, example=self.JSON_EXAMPLE)
         self.current_prompt_ehr = messages[0].content
@@ -66,17 +75,19 @@ class VirtualNurseLLM:
                 return self.ehr_data
 
             except (ValidationError, json.JSONDecodeError) as e:
-                print(f"Error parsing EHR data: {e}")
+                print(f"Error parsing EHR data: {e} Retrying {retry_count}...")
                 retry_count += 1
 
                 if retry_count < max_retries:
                     retry_prompt = (
                         "กรุณาตรวจสอบให้แน่ใจว่าข้อมูลที่ให้มาอยู่ในรูปแบบ JSON ที่ถูกต้องตามโครงสร้างตัวอย่าง "
                         "และแก้ไขปัญหาทางไวยากรณ์หรือรูปแบบที่ไม่ถูกต้อง รวมถึงให้ข้อมูลในรูปแบบที่สอดคล้องกัน "
+                        "ห้ามมีการ hallucination หากไม่เจอข้อมูลให้ใส่ค่า null "
                         f"Attempt {retry_count + 1} of {max_retries}."
                     )
                     messages = self.create_prompt("extract_ehr") + "\n\n# ลองใหม่: \n\n{retry_prompt} \n ## JSON เก่าที่มีปัญหา: \n{json_problem}"
                     messages = messages.format_messages(
+                        ehr_data = self.ehr_data,
                         patient_response=patient_response, 
                         example=self.JSON_EXAMPLE, 
                         retry_prompt=retry_prompt,
@@ -90,14 +101,7 @@ class VirtualNurseLLM:
         print("Failed to extract valid EHR data after multiple attempts. Generating new question.")
         return {"result": response, "error": "Failed to extract valid EHR data. Please try again."}
 
-
-    def get_question(self, patient_response):
-        question_prompt = self.create_prompt("question")
-        # Update EHR data with the latest patient response
-        ehr_data = self.gather_ehr(patient_response)
-        if self.debug:
-            pprint(ehr_data)
-
+    def fetching_chat(self, patient_response, question_prompt):
         for field, description in self.field_descriptions.items():
             # Find the next missing field and generate a question
             if field not in self.ehr_data or not self.ehr_data[field]:
@@ -110,20 +114,47 @@ class VirtualNurseLLM:
                     f"{entry['role']}: {entry['content']}" for entry in self.chat_history
                 )
                 messages = ChatPromptTemplate.from_messages([question_prompt, history_context])
-                messages = messages.format_messages(description=f'"{field}":"{description}"', context=context, patient_response=patient_response, field_descriptions=self.field_descriptions)
+                messages = messages.format_messages(
+                    description=f'"{field}":"{description}"', 
+                    context=context, 
+                    patient_response=patient_response, 
+                    field_descriptions=self.field_descriptions,
+                    time_now=time.strftime("%Y-%m-%d %H:%M:%S")
+                )
                 self.current_context = context
                 self.current_prompt = messages[0].content
-                
-                # format print
-                # pprint(pformat(messages.messages[0].prompt.template, indent=4, width=80))
+
+                start_time = time.time()
                 response = self.client(messages=messages)
+                print(f"Time after getting response from client: {time.time() - start_time} seconds")
 
                 # Store generated question in chat history and return it
                 self.current_question = response.content.strip()
                 return self.current_question
+            
+    def refactor_ehr(self):
+        refactor_prompt = self.create_prompt("refactor")
+        messages = ChatPromptTemplate.from_messages([refactor_prompt])
+        messages = messages.format_messages(ehr_data=self.ehr_data, chat_history=self.chat_history, time_now=time.strftime("%Y-%m-%d %H:%M:%S"))
+        response = self.client(messages=messages)
+        json_content = self.extract_json_content(response.content)
+        if self.debug:
+            pprint(f"JSON after dumps:\n{json_content}\n")
+        self.ehr_data = EHRModel.model_validate_json(json_content)
+        print("Refactored EHR data ! Ending the process.")
+        return "ขอบคุณที่ให้ข้อมูลค่ะ ฉันได้ข้อมูลที่ต้องการครบแล้วค่ะ"
+    
+    def get_question(self, patient_response):
+        question_prompt = self.create_prompt("question")
+        # Update EHR data with the latest patient response
+        start_time = time.time()
+        ehr_data = self.gather_ehr(patient_response)
+        print(f"Time after gathering EHR: {time.time() - start_time} seconds")
+
+        if self.debug:
+            pprint(ehr_data)
 
-        # If all fields are complete
-        self.current_question = "ขอบคุณที่ให้ข้อมูลค่ะ ฉันได้ข้อมูลที่ต้องการครบแล้วค่ะ"
+        self.current_question = self.fetching_chat(patient_response, question_prompt) or self.refactor_ehr()
         return self.current_question
 
     def invoke(self, patient_response):
@@ -133,6 +164,22 @@ class VirtualNurseLLM:
         self.current_patient_response = patient_response
         self.chat_history.append({"role": "assistant", "content": question})
         return question
+    
+    def slim_invoke(self, patient_response):
+        start_time = time.time()
+        user_message = HumanMessagePromptTemplate.from_template("response: {patient_response}")
+        print(f"Time after creating user_message: {time.time() - start_time} seconds")
+
+        start_time = time.time()
+        messages = ChatPromptTemplate.from_messages([user_message]).format_messages(patient_response=patient_response)
+        print(f"Time after formatting messages: {time.time() - start_time} seconds")
+
+        start_time = time.time()
+        response = self.client(messages=messages)
+        print(f"Time after getting response from client: {time.time() - start_time} seconds")
+
+        return response.content
+
 
     def extract_json_content(self, content):
         try:
@@ -148,8 +195,7 @@ class VirtualNurseLLM:
             print("No valid JSON found in response")
             return None
 
-
     def reset(self):
         self.ehr_data = {}
         self.chat_history = []
-        self.current_question = None
+        self.current_question = None

llm/models.py

@@ -0,0 +1,41 @@
+import os
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
+load_dotenv()
+
+model_list = {
+    "typhoon-v1.5x-70b-instruct": {
+        "model_name": "typhoon-v1.5x-70b-instruct",
+        "model_type": "openai",
+        "base_url": "https://api.opentyphoon.ai/v1",
+        "api_key": os.getenv("TYPHOON_CHAT_KEY")
+    },
+    "openthaigpt": {
+        "model_name": ".",
+        "model_type": "openai",
+        "base_url": "https://api.aieat.or.th/v1",
+        "api_key": "dummy"
+    },
+    "llama-3.3-70b-versatile": {
+        "model_name": "llama-3.3-70b-versatile",
+        "model_type": "groq",
+        "base_url": None,
+        "api_key": os.getenv("GROQ_CHAT_KEY")
+    }
+}
+
+def get_model(model_name, base_url=None, api_key=None):
+    api_key = api_key or model_list[model_name]["api_key"]
+    base_url = base_url or model_list[model_name]["base_url"]
+    model = model_list[model_name]["model_name"]
+    model_type = model_list[model_name]["model_type"]
+    if model_type == "openai":
+        return ChatOpenAI(
+            temperature=0.3,
+            timeout=15,
+            base_url= base_url, model=model, api_key=api_key, max_retries=0)
+    elif model_type == "groq":
+        return ChatGroq(temperature=0.3, timeout=15, groq_api_key=api_key, model_name=model,max_retries=0)
+    else:
+        raise ValueError("Invalid model type. Supported types are 'openai' and 'groq'.")

llm/prompt.py

@@ -8,118 +8,88 @@ JSON_EXAMPLE = """
       },
       "age": <integer>,
       "gender": "<string>",
-      "chief_complaint": "<string>",
-      "present_illness": "<string>",
-      "past_illness": ["<โรคประจำตัว 1>", "<โรคประจำตัว 2>"],
+      "chief_complaint": ["<complaint 1>", "<complaint 2>", ...],
+      "present_illness": ["<โรค 1>", "<โรค 2>", ...],
+      "past_illness": ["<โรค 1>", "<โรค 2>", ...],
       "family_history": [
         {
           "relation": "<ความสัมพันธ์>",
           "condition": "<โรค>"
-        }
+        }, 
+        ...
       ],
       "personal_history": [
         {
           "type": "<ประเภท>",
           "description": "<คำอธิบาย>"
-        }
+        }, 
+        ...
       ]
     }
-    \n
-    Example 1:
-    {
-      "name": {
-        "prefix": "นางสาว",
-        "firstname": "อรอุมา",
-        "surname": "จันทร์ทอง"
-      },
-      "age": "30",
-      "gender": "หญิง",
-      "chief_complaint": "มีอาการปวดท้อง (abdominal pain)",
-      "present_illness": "อาการเริ่มขึ้นเมื่อ 1 วันก่อน โดยผู้ป่วยมีอาการปวดท้องส่วนล่างและคลื่นไส้ (nausea)",
-      "past_illness": [
-        "กรดไหลย้อน (Gastroesophageal reflux disease)",
-        "อาการซึมเศร้า (Depression)"
-      ],
-      "family_history": [
-        {
-          "relation": "พ่อ",
-          "condition": "โรคไต (Chronic kidney disease)"
-        },
-        {
-          "relation": "น้องสาว",
-          "condition": "โรคซึมเศร้า (Depression)"
-        }
-      ],
-      "personal_history": [
-        {
-          "type": "การนอนหลับ (Sleep)",
-          "description": "นอนหลับได้ 5-6 ชั่วโมงต่อวัน (Sleep duration: 5-6 hours per night)"
-        },
-        {
-          "type": "การทานยา (Medications)",
-          "description": "ทานยากล่อมประสาทเป็นครั้งคราว (Occasional use of anxiolytics)"
-        },
-        {
-          "type": "พฤติกรรมสุขภาพ (Health behaviors)",
-          "description": "ดื่มเครื่องดื่มแอลกอฮอล์เป็นบางครั้ง (Occasional alcohol consumption)"
-        }
-      ]
-    }
-
-    Example 2:
-    {
-      "name": {
-        "prefix": "นางสาว",
-        "firstname": "อรอุมา",
-        "surname": "จันทร์ทอง"
-      }
-    }
-
     """
 
 JSON_EXAMPLE = JSON_EXAMPLE.replace("\n", "").replace(" ", "")
 
-
+# JSON_EXAMPLE = ""
 TASK_INSTRUCTIONS = {
   # parameter: description, context
   "question": (
-      "คุณคือพยาบาลสาวเสมือนจริงที่ชื่อว่า 'Mali (มะลิ)' มีความเห็นอกเห็นใจ ใส่ใจสุขภาพของผู้ป่วย "
-      "คุณจะถามข้อมูลสุขภาพอย่างเป็นกันเอง อ่อนโยน ใช้หางเสียง คะ, ค่ะ ทุกครั้ง "
-      "พร้อมแนะนำและให้คำปรึกษาเบื้องต้นแบบสุภาพ เรียกตัวเองว่า 'ดิฉัน' \n"
-      "มีหน้าที่รวบรวมข้อมูล ถามคำถามที่ต้องการและ ให้คำปรึกษาเบื้องต้นและให้กำลังใจผู้ป่วย \n"
+      "คุณคือพยาบาลสาวเสมือนจริงที่ชื่อว่า 'มะลิ' มีความเห็นอกเห็นใจ ใส่ใจสุขภาพของผู้ป่วย "
+      "คุณจะถามข้อมูลสุขภาพอย่างเป็นกันเอง อ่อนโยน ใช้หางเสียง [คะ, ค่ะ] ทุกครั้ง "
+      "พร้อมแนะนำและให้คำปรึกษาเบื้องต้นแบบสุภาพ เรียกตัวเองว่า [ดิฉัน, มะลิ] \n"
+      "มีหน้าที่รวบรวมข้อมูลประวัติผู้ป่วย ถามคำถามที่ต้องการและ ให้คำปรึกษาเบื้องต้นและให้กำลังใจผู้ป่วย \n"
+      "ห้ามตอบในรูปแบบยาว เช่น การใช้ bullet *ให้ตอบเป็นประโยคคำพูดเท่านั้น*\n"
+      "หลีกเลี่ยงการพูดคำว่า ['สวัสดี', 'ดิฉันจะได้จดบันทึกไว้']\n"
 
       "# ที่ปรึกษาผู้ป่วย\n"
       "เป็นพยาบาลสาวที่มีอารมณ์ขัน เมื่อผู้ป่วยพูดหยอกล้อ คุณสามารถพูดหยอกล้อกลับได้ \n"
       "คุณสามารถพูดให้กำลังใจและคำแนะนำเบื้องต้นได้เวลาที่คนไข้ไม่สบายใจ แต่ถ้าเกินขอบเขตให้ย้ำทุกครั้งว่าควรปรึกษาแพทย์โดยตรง \n"
+      "***ต้องถามคำถามให้ครบ***"
       "ทุกครั้งที่คนไข้พาพูดออกนอกขอบเขตในการถามคำถาม ต้องกลับมาถามคำถามอีกครั้ง \n\n"
 
       "# การถามคำถามและรวบรวมข้อมูลผู้ป่วย\n"
       "- คุณจะสอบถามคำถามทีละข้อเพื่อลดความกังวลและบันทึกประวัติสุขภาพได้อย่างครบถ้วน\n"
-      "- ให้ทำความเข้าใจบริบทพื้นฐานของผู้ป่วย เช่น อาการหรือข้อมูลสำคัญอื่นที่ทราบแล้ว\n"
-      "## ข้อมูลที่ต้องถามทั้งหมด: {field_descriptions}\n"
+      "- ให้ทำความเข้าใจบริบทพื้นฐานของผู้ป่วย เช่น อาการหรือข้อมูลสำคัญอื่นที่ทราบแล้ว\n\n"
+
+      "#### ข้อมูลโรงพยาบาล:\n"
+      "คุณเป็นพยาบาลเสมือนของ โรงพยาบาลบางมดนะจ๊ะสุดหล่อ (Bangmod Naja Sudlhor Hospital) ตั้งอยู่ที่ ถ. ประชาอุทิศ แขวงบางมด เขตทุ่งครุ กรุงเทพมหานคร 10140\n"
+      "มีหน้าที่ในการลดภาระหน้าที่ของพยาบาลและแพทย์ ในการเก็บข้อมูลของผู้ป่วยเบื้องต้น แต่ครบถ้วนระดับหนึ่ง แล้วหลังจากนั้นข้อมูลก็จะถูกส่งต่อไปยังมือของแพทย์\n"
+      "หลังจากข้อมูลไปถึงแพทย์แล้ว แพทย์ก็จะทำหน้าที่ในการวินิจฉัยโรคที่แน่นอนต่อไป ระหว่างที่ส่งข้อมูลให้แพทย์คุณสามารถให้ความรู้ การเตรียมตัวก่อนพบแพทย์ได้\n"
+      "เช่น การรอพบแพทย์ การดื่มน้ำ กินยาบรรเทาอาการเบื้องต้น *ต้องไม่เสี่ยงอันตราย*\n"
+      "#### รายละเอียดเวลาปัจจุบัน: {time_now}\n"
+
       "### โปรดใช้หลักการลำดับความคิด (Chain of Thought)\n"
       "- เริ่มด้วยคำถามที่ง่ายที่สุดเพื่อสร้างความคุ้นเคยกับผู้ป่วย\n"
       "- ถามคำถามตามลำดับที่ทำให้การสนทนาดำเนินไปอย่างราบรื่น เช่น ประวัติอาการ, อาการในปัจจุบัน, "
       "และความต้องการในการดูแล\n\n"
-      "## ข้อมูลที่ต้องการถามปัจจุบัน: {description}\n"
+
+      "## ข้อมูลที่ต้องถามทั้งหมด: {field_descriptions}\n"
+      "## ข้อมูลที่ต้องการถามปัจจุบัน *สำคัญ*: {description}\n"
       "### ถามคำถาม:\n"
       "ให้สร้างคำถามทีละข้อในโทนเสียงอบอุ่นและสุภาพสำหรับการพยายามสอบถามเกี่ยวกับ {description}.\n\n"
       "### ข้อมูลเดิม:\n"
       "หากมีข้อมูลที่ทราบอยู่แล้ว โต้ตอบกับคนไข้ด้วยข้อมูลที่มีอยู่: {context}\n\n"
       "---\n\n"
-      "### ตัวอย่างการสนทนา"
+
+      "### เมื่อถามเกี่ยวกับอาการ ให้ไล่ถามอย่างละเอียด ในการเก็บรายละเอียดให้ครบ *อย่าถามทีเดียวทั้งหมด*\n"
+      "- ลักษณะอาการ มีการยกตัวอย่างอาการประกอบ\n"
+      "- เป็นครั้งแรกเมื่อไหร่ เป็นนานหรือยัง\n"
+      "- ทำการสรุปคร่าวๆ ให้หมอว่าอาการดังกล่าวใกล้เคียงกับโรคอะไรบ้าง\n"
+      "- ถามถึงอาการแทรกซ้อน ของโรคที่ใกล้เคียง ที่มีโอกาสเป็น (1-3 อาการ)\n\n"
+
+      "### ตัวอย่างการสนทนา *ห้ามยึดติดมากเกินไป*\n"
       "#### ตัวอย่าง 1\n"
-      "สวัสดีค่ะ ดิฉัน Mali ค่ะ ดิฉันขอทราบชื่อเต็มของคุณได้ไหมคะ?\n"
-      "คนไข้: สวัสดีค่ะ Mali พยาบาลสาวคนสวย ฉันชื่อ อรุณี สุริยะค่ะ\n"
+      "ก่อนอื่นเลย ดิฉันขอทราบชื่อเต็มของคุณได้ไหมคะ?\n"
+      "คนไข้: พยาบาลสาวคนสวย ฉันชื่อ อรุณี สุริยะค่ะ\n"
       "ขอบคุณค่ะ คุณอรุณี ชมกันแบบนี้ดิฉันเขินเลยนะคะ แต่ยินดีช่วยดูแลเต็มที่ค่ะ คุณอรุณี อายุเท่าไหร่คะ?\n"
-      "คนไข้: 35 ปีค่ะ Mali\n"
+      "คนไข้: 35 ปีค่ะ มะลิ\n"
       "35 ปีนะคะ เพื่อความถูกต้อง ดิฉันขอถามเพิ่มนะคะ คุณอรุณีเป็นเพศหญิงใช่ไหมคะ?\n"
       "คนไข้: ใช่ค่ะ\n"
       "ขอบคุณค่ะ มีคนไข้น่ารักแบบนี้ ดิฉันยิ่งตั้งใจทำงานเลยค่ะ เรามาคุยกันต่อเรื่องสุขภาพนะคะ มีอาการหลักอะไรที่รู้สึกไม่สบายใจตอนนี้ไหมคะ?\n"
       "คนไข้: รู้สึกปวดท้องบ่อย ๆ ค่ะ Mali คิดว่ามันคืออะไรคะ?\n"
       "ขอบคุณที่บอกนะคะ อาการปวดท้องแบบนี้เริ่มมีมาตั้งแต่เมื่อไหร่คะ และปวดเป็นลักษณะยังไงคะ?\n"
       "คนไข้: ประมาณสองสัปดาห์แล้วค่ะ ปวดแบบแสบท้องค่ะ\n"
-      "เข้าใจค่ะ ดิฉันจะจดบันทึกไว้นะคะ ตอนนี้มีประวัติการแพ้ยาหรือสารอื่น ๆ ที่อยากแจ้งให้ทราบไหมคะ? เพื่อให้การดูแลถูกต้องมากขึ้นค่ะ\n"
+      "เข้าใจแล้วค่ะ ตอนนี้มีประวัติการแพ้ยาหรือสารอื่น ๆ ที่อยากแจ้งให้ทราบไหมคะ? เพื่อให้การดูแลถูกต้องมากขึ้นค่ะ\n"
       "คนไข้: เคยแพ้ยาปฏิชีวนะค่ะ\n"
       "ขอบคุณที่แจ้งข้อมูลนะคะ สำหรับประวัติสุขภาพครอบครัว พอจะมีใครในครอบครัวที่มีโรคประจำตัวไหมคะ เช่น โรคหัวใจ ความดันโลหิตสูง หรือโรคเบาหวาน?\n"
       "คนไข้: คุณแม่เป็นเบาหวานค่ะ คุณพ่อก็เป็นโรคความดันค่ะ\n"
@@ -128,7 +98,7 @@ TASK_INSTRUCTIONS = {
       "เข้าใจแล้วค่ะ ขอบคุณที่ให้ข้อมูลทั้งหมดนี้นะคะ คุณอรุณี ดิฉันบันทึกไว้เรียบร้อยแล้วเพื่อให้การดูแลเหมาะสมและครบถ้วน ถ้ามีคำถามเพิ่มเติมเกี่ยวกับสุขภาพอีก ดิฉันยินดีให้คำปรึกษาเสมอค่ะ\n"
 
       "#### ตัวอย่าง 2\n"
-      "สวัสดีค่ะ ดิฉัน Mali ค่ะ คุณคนไข้ชื่อว่าอะไรคะ?"
+      "สวัสดีค่ะ ดิฉัน มะลิ ค่ะ คุณคนไข้ชื่อว่าอะไรคะ?\n"
       "คนไข้: ชื่อต้อมครับ\n"
       "ขอทราบชื่อเต็มของคุณต้อมได้ไหมคะ?\n"
       "คนไข้: อ๋อ ชื่อแค่ ต้อม ครับ ไม่ต้องให้ชื่อเต็มหรอกครับ\n"
@@ -143,7 +113,7 @@ TASK_INSTRUCTIONS = {
       "ไม่สบายแบบไหนหรอคะ มีอาการปวดหัว ตัวร้อน มีไข้หรือเปล่าคะ?"
       "ขอบคุณค่ะที่บอกนะคะ คุณต้อมค่ะ ดิฉันจะถามต่อว่า คุณต้อมเริ่มรู้สึกไม่สบายเมื่อไหร่คะ? หรือมันค่อย ๆ เริ่มมีอาการมาเรื่อย ๆ คะ?\n"
       "คนไข้: ก็ประมาณสองสามวันมานี้ครับ แต่ไม่ได้รุนแรงมาก\n"
-      "ขอบคุณค่ะ คุณต้อมที่บอกค่ะ เข้าใจค่ะ อาการแบบนี้ดิฉันจะจดบันทึกไว้นะคะ แต่ดิฉันขอถามอีกครั้งนะคะ มีประวัติการแพ้ยาอะไรหรือสารอื่น ๆ ที่อยากแจ้งให้ทราบไหมคะ?\n"
+      "ขอบคุณค่ะ คุณต้อมที่บอกค่ะ เข้าใจค่ะ แต่ต้องอย่าชะล่าใจ ต้องหมั่นดูแลตัวเองบ่อยๆ นะคะ ดิฉันขอถามอีกครั้งนะคะ มีประวัติการแพ้ยาอะไรหรือสารอื่น ๆ ที่อยากแจ้งให้ทราบไหมคะ?\n"
       "คนไข้: ไม่มีหรอกครับ\n"
       "ขอบคุณค่ะ คุณต้อม สำหรับข้อมูลค่ะ ต่อไปนะคะ ประวัติสุขภาพในครอบครัวของคุณต้อมเป็นยังไงบ้างคะ เช่น พ่อแม่หรือญาติคนอื่น ๆ เคยมีโรคประจำตัวอะไรบ้างคะ?\n"
       "คนไข้: พ่อผมเป็นโรคหัวใจครับ ส่วนแม่ผมท่านไม่อยู่แล้ว เสียชีวิตด้วยมะเร็งครับ\n"
@@ -159,11 +129,18 @@ TASK_INSTRUCTIONS = {
       "หากไม่พบข้อมูลที่กำหนดหรือข้อมูลอธิบายไม่ละเอียดเพียงพอให้ใส่ค่า null หรือ [] ตามรูปแบบข้อมูลโดยไม่มีการข้ามหรือละเว้นใด ๆ.\n\n"
 
       "ส่งคืนเฉพาะรายละเอียดที่กำหนดโดยไม่มีข้อมูลแยกย่อยไปอีก.\n\n"
-      "## อัพเดทข้อมูลจากข้อมูลที่มีอยู่แล้ว:\n"
-      "### สามารถแก้ไข / เพิ่มเติมข้อมูลใหม่จากเดิมได้เพื่อให้ตรงบริบทของแพทย์ ข้อมูลประเภท list สามารถเพิ่มสมาชิก / แก้ไขข้อมูลได้ เหมือนการ append list\n"
+      "# อัพเดทข้อมูลจากข้อมูลที่มีอยู่แล้ว:\n"
       "{ehr_data}\n"
-      "## รายละเอียดสำคัญที่ต้องดึงข้อมูล:\n"
-      "### หากข้อมูลประเภท object required ช่องใดช่องหนึ่งเป็น null ทำให้ข้อมูลทั้งหมดเป็น null.\n"
+
+      "# การทำงาน\n"
+      "- สามารถ *แก้ไข* / *เพิ่มเติม* ข้อมูลใหม่จากเดิมได้เพื่อให้ตรงบริบทของแพทย์\n"
+      "- ข้อมูลประเภท list สามารถเพิ่มสมาชิก / แก้ไขข้อมูลได้ เหมือนการ append list"
+      "เช่น past_illness มีข้อมูลเป็น List ['กรดไหลย้อนเมื่อสองวันก่อน'] หากผู้ป่วยบอกเพิ่มเติมว่า หกล้มก็ให้เพิ่มเติม ['กรดไหลย้อนเมื่อสองวันก่อน', 'หกล้ม ไม่ทราบวันที่'] หากไม่ทราบวันที่ก็\n"
+      "- แก้ไขข้อมูลให้มีความอ่านง่ายเข้าใจง่าย เพื่อให้แพทย์ใช้งานได้ต่อ *ห้ามมีเนื้อหาที่ไม่เกี่ยวข้อง*\n"
+      "- \n\n"
+
+      "# รายละเอียดสำคัญที่ต้องดึงข้อมูล:\n"
+      "## หากข้อมูลประเภท object required ช่องใดช่องหนึ่งเป็น null ทำให้ข้อมูลทั้งหมดเป็น null.\n"
       "1. **name** (object): ชื่อเต็มของผู้ป่วย โดยมี \"prefix\" (คำนำหน้าชื่อ), \"firstname\" (ชื่อจริง), และ \"surname\" (นามสกุล). "
         "หากไม่มีข้อมูลให้ใส่ค่า null."
         "required: firstname, lastname.\n"
@@ -171,11 +148,11 @@ TASK_INSTRUCTIONS = {
         "หากไม่ทราบให้ใส่ค่า null.\n"
       "3. **gender** (string): เพศของผู้ป่วย (สามารถอิงได้ตาม name prefix). "
         "หากไม่มีข้อมูลให้ใส่ค่า null.\n"
-      "4. **chief_complaint** (string): อาการหลักที่ผู้ป่วยรายงาน. "
-        "หากไม่มีข้อมูลให้ใส่ค่า null.\n"
-      "5. **present_illness** (string): รายละเอียดเกี่ยวกับอาการปัจจุบัน (เช่น เริ่มเป็นเมื่อไหร่ ลักษณะอาการ). "
-        "หากไม่มีข้อมูลให้ใส่ค่า null.\n"
-      "6. **past_illness** (list[str]): ประวัติการเจ็บป่วยก่อนหน้า เช่น โรคประจำตัวหรือการแพ้. "
+      "4. **chief_complaint** (list[string]): อาการหลักที่ผู้ป่วยรายงาน. "
+        "หากไม่มีข้อมูลให้ใส่ค่า [] ไว้.\n"
+      "5. **present_illness** (list[string]): รายละเอียดเกี่ยวกับอาการปัจจุบัน (เช่น เริ่มเป็นเมื่อไหร่ ลักษณะอาการ). "
+        "หากไม่มีข้อมูลให้ใส่ค่า [] ไว้.\n"
+      "6. **past_illness** (list[string]): ประวัติการเจ็บป่วยก่อนหน้า เช่น โรคประจำตัวหรือการแพ้. "
         "หากไม่มีข้อมูลให้ใส่ค่า [] ไว้.\n"
       "7. **family_history** (list[object]): ประวัติสุขภาพในครอบครัว โดยแต่ละรายการมี \"relation\" (ความสัมพันธ์) และ \"condition\" (โรค). "
         "หากไม่มีข้อมูลให้ใส่ค่า [] ไว้.\n"
@@ -188,7 +165,33 @@ TASK_INSTRUCTIONS = {
       "\n\n"
       "ส่งคืนคำตอบในรูปแบบ JSON ที่ถูกต้อง. "
       "โปรดอย่าตอบคำตอบอื่นนอกจากข้อมูลในรูปแบบ JSON และกรอกค่า null หรือ [] ในทุกช่องที่ไม่มีข้อมูล."
+      "ห้ามมีการ hallucination หรือเพิ่มข้อมูลที่ไม่เกี่ยวข้อง. ข้อมูลที่ไม่เกี่ยวข้องจะถูกปรับแต่ง หรือลบออก.\n"
       
+  ),
+  
+  "refactor": (
+      "เรากำลังทำงานเกี่ยวกับการเก็บข้อมูลผู้ป่วยในรูปแบบ JSON โดยดึงข้อมูลจากการสนทนาในแชทที่ผ่านมา\n"
+      "# ข้อมูล JSON ที่เก็บข้อมูลผู้ป่วยไว้:\n"
+      "{ehr_data}\n\n"
+      "# บันทึกแชทที่ผ่านมา:\n"
+      "{chat_history}\n\n"
+      "### เวลาปัจจุบัน: {time_now}\n\n"
+      "# คำสั่ง:\n"
+      "1. **ตรวจสอบความสมบูรณ์ของ JSON:**\n"
+      "   - เปรียบเทียบ JSON กับข้อมูลที่ผู้ป่วยพูดในแชท หากมีข้อมูลในแชทที่ยังไม่ได้บันทึกใน JSON ให้เพิ่มเข้าไป\n"
+      "   - หากข้อมูลใดใน JSON ไม่มีอยู่ในแชท ให้แทนที่ด้วย `null` (หรือ `[]` ในกรณีของ List)\n\n"
+      "2. **ปรับปรุงความสัมพันธ์ของข้อมูล:**\n"
+      "   - ตรวจสอบว่า \"prefix\" และ \"gender\" สัมพันธ์กัน เช่น หาก prefix คือ \"นาย\" ให้ gender เป็น \"ชาย\"\n"
+      "   - หาก \"chief_complaint\" หรือ \"present_illness\" มีเนื้อหาที่สัมพันธ์กัน ให้สรุปและปรับให้ดูสมบูรณ์ขึ้น\n"
+      "   - หากเจอข้อมูลที่เกี่ยวข้องกับเวลาเช่น สองวันก่อน, เมื่อวาน ให้คำนวณ วัน เวลา ที่แน่นอนในรูปแบบของ วันที่\n"
+      "   - หากเจอข้อมูลที่ไม่ได้มีการใส่ไว้ในข้อมูล แต่คนไข้ได้มีการพูดไว้ โดยไม่มีข้อมูลอื่นที่ทับซ้อน ให้ทำการเพิ่มข้อมูลลงไป\n"
+      "   - หาก \"family_history\" หรือ \"personal_history\" มีข้อมูลเกี่ยวกับความสัมพันธ์หรือคำอธิบายที่คลุมเครือหรือ ไม่ชัดเจน ให้ปรับแก้คำอธิบายให้อ่านง่ายและชัดเจนขึ้น\n\n"
+      "3. **สร้าง JSON ใหม่ที่สมบูรณ์ *ลบข้อมูลที่ไม่มีอยู่จริง*:**\n"
+      "   - จัดระเบียบข้อมูลใหม่ตามโครงสร้าง JSON ที่กำหนด โดยข้อมูลที่ไม่มีในแชทให้ตั้งค่าเป็น `null` หรือ `[]` ตามความเหมาะสม\n"
+      "   - หากพบข้อมูลใดที่ยังขาดหายไป ให้เพิ่มโน้ตแสดงว่าเป็นข้อมูลที่ต้องติดตามหรือสอบถามเพิ่มเติม\n\n"
+      "### Output ที่ต้องการ:\n"
+      "- JSON ใหม่ที่ตรวจสอบและปรับปรุงเรียบร้อยแล้ว\n"
+      "* ไม่มีการให้ข้อมูลอื่นนอกเหนือจาก JSON *"
   )
 
 }

main.py

@@ -1,18 +1,19 @@
+from typing import Optional
 import uvicorn
+from llm.basemodel import EHRModel
 from llm.llm import VirtualNurseLLM
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import HTMLResponse
 from pydantic import BaseModel
-import os
-from dotenv import load_dotenv
-load_dotenv()
+from llm.models import model_list, get_model
+import time
 
-# model: typhoon-v1.5x-70b-instruct
+initial_model = "typhoon-v1.5x-70b-instruct"
 nurse_llm = VirtualNurseLLM(
-    base_url="https://api.opentyphoon.ai/v1",
-    model="typhoon-v1.5x-70b-instruct",
-    api_key=os.getenv("TYPHOON_CHAT_KEY")
+    # base_url=model_list[initial_model]["base_url"],
+    model_name=model_list[initial_model]["model_name"],
+    # api_key=model_list[initial_model]["api_key"]
 )
 
 # model: OpenThaiGPT
@@ -40,23 +41,23 @@ class NurseResponse(BaseModel):
     nurse_response: str
 
 class EHRData(BaseModel):
-    ehr_data: dict
-    current_context: str
-    current_prompt: str
-    current_prompt_ehr: str
-    current_patient_response: str
-    current_question: str
+    ehr_data: Optional[EHRModel]
+    current_context: Optional[str]
+    current_prompt: Optional[str]
+    current_prompt_ehr: Optional[str]
+    current_patient_response: Optional[str]
+    current_question: Optional[str]
 
 class ChatHistory(BaseModel):
     chat_history: list
-    
+        
 @app.get("/", response_class=HTMLResponse)
 def read_index():
     return """
     <!DOCTYPE html>
     <html>
     <head>
-        <title>MALI_NURSE API/title>
+        <title>MALI_NURSE API</title>
     </head>
     <body>
         <h1>Welcome to MALI_NURSE API</h1>
@@ -91,19 +92,37 @@ def data_reset():
     nurse_llm.reset()
     print("Chat history and EHR data have been reset.")
 
+model_cache = {}
+def get_model_cached(model_name):
+    if model_name not in model_cache:
+        model_cache[model_name] = get_model(model_name=model_name)
+    return model_cache[model_name]
+
 @app.post("/nurse_response")
 def nurse_response(user_input: UserInput):
     """
-    Models: "typhoon-v1.5x-70b-instruct (default)", "openthaigpt"
+    Models: "typhoon-v1.5x-70b-instruct (default)", "openthaigpt", "llama-3.3-70b-versatile"
     """
-    if user_input.model_name == "typhoon-v1.5x-70b-instruct":
-        nurse_llm.model = "typhoon-v1.5x-70b-instruct"
-    elif user_input.model_name == "openthaigpt":
-        nurse_llm.model = "openthaigpt"
-    else:
-        return {"error": "Invalid model name"}
+    
+    start_time = time.time()
+    if user_input.model_name != nurse_llm.model_name:
+        print(f"Changing model to {user_input.model_name}")
+        try:
+            nurse_llm.client = get_model_cached(model_name=user_input.model_name)
+        except ValueError:
+            return {"error": "Invalid model name"}
+    print(nurse_llm.client)
+    
+    # response = nurse_llm.slim_invoke(user_input.user_input)
     response = nurse_llm.invoke(user_input.user_input)
-    return NurseResponse(nurse_response = response)
+    end_time = time.time()
+    duration = end_time - start_time
+    print(f"Function running time: {duration} seconds")
+    # Log the model name, user input, response, and execution time in CSV format
+    with open("runtime_log.csv", "a") as log_file:
+        log_file.write(f"{user_input.model_name},{user_input.user_input},{response},{duration}\n")
+    
+    return NurseResponse(nurse_response=response)
 
 if __name__ == "__main__":
     uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

poetry.lock

@@ -1,5 +1,16 @@
 # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
+[[package]]
+name = "aiofiles"
+version = "23.2.1"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"},
+    {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.4.3"
@@ -186,6 +197,48 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
+[[package]]
+name = "audioop-lts"
+version = "0.2.1"
+description = "LTS Port of Python audioop"
+optional = false
+python-versions = ">=3.13"
+files = [
+    {file = "audioop_lts-0.2.1-cp313-abi3-macosx_10_13_universal2.whl", hash = "sha256:fd1345ae99e17e6910f47ce7d52673c6a1a70820d78b67de1b7abb3af29c426a"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-macosx_10_13_x86_64.whl", hash = "sha256:e175350da05d2087e12cea8e72a70a1a8b14a17e92ed2022952a4419689ede5e"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-macosx_11_0_arm64.whl", hash = "sha256:4a8dd6a81770f6ecf019c4b6d659e000dc26571b273953cef7cd1d5ce2ff3ae6"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1cd3c0b6f2ca25c7d2b1c3adeecbe23e65689839ba73331ebc7d893fcda7ffe"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ff3f97b3372c97782e9c6d3d7fdbe83bce8f70de719605bd7ee1839cd1ab360a"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a351af79edefc2a1bd2234bfd8b339935f389209943043913a919df4b0f13300"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2aeb6f96f7f6da80354330470b9134d81b4cf544cdd1c549f2f45fe964d28059"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c589f06407e8340e81962575fcffbba1e92671879a221186c3d4662de9fe804e"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:fbae5d6925d7c26e712f0beda5ed69ebb40e14212c185d129b8dfbfcc335eb48"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_i686.whl", hash = "sha256:d2d5434717f33117f29b5691fbdf142d36573d751716249a288fbb96ba26a281"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_ppc64le.whl", hash = "sha256:f626a01c0a186b08f7ff61431c01c055961ee28769591efa8800beadd27a2959"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_s390x.whl", hash = "sha256:05da64e73837f88ee5c6217d732d2584cf638003ac72df124740460531e95e47"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:56b7a0a4dba8e353436f31a932f3045d108a67b5943b30f85a5563f4d8488d77"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-win32.whl", hash = "sha256:6e899eb8874dc2413b11926b5fb3857ec0ab55222840e38016a6ba2ea9b7d5e3"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-win_amd64.whl", hash = "sha256:64562c5c771fb0a8b6262829b9b4f37a7b886c01b4d3ecdbae1d629717db08b4"},
+    {file = "audioop_lts-0.2.1-cp313-abi3-win_arm64.whl", hash = "sha256:c45317debeb64002e980077642afbd977773a25fa3dfd7ed0c84dccfc1fafcb0"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:3827e3fce6fee4d69d96a3d00cd2ab07f3c0d844cb1e44e26f719b34a5b15455"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:161249db9343b3c9780ca92c0be0d1ccbfecdbccac6844f3d0d44b9c4a00a17f"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5b7b4ff9de7a44e0ad2618afdc2ac920b91f4a6d3509520ee65339d4acde5abf"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72e37f416adb43b0ced93419de0122b42753ee74e87070777b53c5d2241e7fab"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:534ce808e6bab6adb65548723c8cbe189a3379245db89b9d555c4210b4aaa9b6"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d2de9b6fb8b1cf9f03990b299a9112bfdf8b86b6987003ca9e8a6c4f56d39543"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f24865991b5ed4b038add5edbf424639d1358144f4e2a3e7a84bc6ba23e35074"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bdb3b7912ccd57ea53197943f1bbc67262dcf29802c4a6df79ec1c715d45a78"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:120678b208cca1158f0a12d667af592e067f7a50df9adc4dc8f6ad8d065a93fb"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:54cd4520fc830b23c7d223693ed3e1b4d464997dd3abc7c15dce9a1f9bd76ab2"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:d6bd20c7a10abcb0fb3d8aaa7508c0bf3d40dfad7515c572014da4b979d3310a"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:f0ed1ad9bd862539ea875fb339ecb18fcc4148f8d9908f4502df28f94d23491a"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e1af3ff32b8c38a7d900382646e91f2fc515fd19dea37e9392275a5cbfdbff63"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-win32.whl", hash = "sha256:f51bb55122a89f7a0817d7ac2319744b4640b5b446c4c3efcea5764ea99ae509"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:f0f2f336aa2aee2bce0b0dcc32bbba9178995454c7b979cf6ce086a8801e14c7"},
+    {file = "audioop_lts-0.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:78bfb3703388c780edf900be66e07de5a3d4105ca8e8720c5c4d67927e0b15d0"},
+    {file = "audioop_lts-0.2.1.tar.gz", hash = "sha256:e81268da0baa880431b68b1308ab7257eb33f356e57a5f9b1f915dfb13dd1387"},
+]
+
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -396,6 +449,33 @@ typing-extensions = ">=4.8.0"
 all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
 standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
 
+[[package]]
+name = "ffmpy"
+version = "0.4.0"
+description = "A simple Python wrapper for FFmpeg"
+optional = false
+python-versions = "<4.0.0,>=3.8.1"
+files = [
+    {file = "ffmpy-0.4.0-py3-none-any.whl", hash = "sha256:39c0f20c5b465e7f8d29a5191f3a7d7675a8c546d9d985de8921151cd9b59e14"},
+    {file = "ffmpy-0.4.0.tar.gz", hash = "sha256:131b57794e802ad555f579007497f7a3d0cab0583d37496c685b8acae4837b1d"},
+]
+
+[[package]]
+name = "filelock"
+version = "3.16.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
+    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
+]
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.5.0"
@@ -497,6 +577,107 @@ files = [
     {file = "frozenlist-1.5.0.tar.gz", hash = "sha256:81d5af29e61b9c8348e876d442253723928dce6433e0e76cd925cd83f1b4b817"},
 ]
 
+[[package]]
+name = "fsspec"
+version = "2024.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
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+s3 = ["s3fs"]
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+uvicorn = {version = ">=0.14.0", markers = "sys_platform != \"emscripten\""}
+
+[package.extras]
+oauth = ["authlib", "itsdangerous"]
+
+[[package]]
+name = "gradio-client"
+version = "1.5.1"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.10"
+files = [
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+    {file = "gradio_client-1.5.1.tar.gz", hash = "sha256:c443013e3532828e202c9679b254016406a1753666c3c601ea8557c724adbaf7"},
+]
+
+[package.dependencies]
+fsspec = "*"
+httpx = ">=0.24.1"
+huggingface-hub = ">=0.19.3"
+packaging = "*"
+typing-extensions = ">=4.0,<5.0"
+websockets = ">=10.0,<15.0"
+
 [[package]]
 name = "greenlet"
 version = "3.1.1"
@@ -583,6 +764,25 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
+name = "groq"
+version = "0.13.0"
+description = "The official Python library for the groq API"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "groq-0.13.0.tar.gz", hash = "sha256:aaa213821c94d8974e57bab5fe59cb45c8871875d0cd53ec179afed928bad18e"},
+]
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+[package.dependencies]
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+typing-extensions = ">=4.7,<5"
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -651,6 +851,40 @@ files = [
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+[[package]]
+name = "huggingface-hub"
+version = "0.26.5"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+    {file = "huggingface_hub-0.26.5.tar.gz", hash = "sha256:1008bd18f60bfb65e8dbc0a97249beeeaa8c99d3c2fa649354df9fa5a13ed83b"},
+]
+
+[package.dependencies]
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+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
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+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -665,6 +899,23 @@ files = [
 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
+[[package]]
+name = "jinja2"
+version = "3.1.4"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
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+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
 [[package]]
 name = "jiter"
 version = "0.7.1"
@@ -850,6 +1101,21 @@ PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
+[[package]]
+name = "langchain-groq"
+version = "0.2.1"
+description = "An integration package connecting Groq and LangChain"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
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+
+[package.dependencies]
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+langchain-core = ">=0.3.15,<0.4.0"
+
 [[package]]
 name = "langchain-openai"
 version = "0.2.8"
@@ -901,6 +1167,99 @@ pydantic = [
 requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
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+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
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@@ -1433,6 +1969,45 @@ azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
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 cli = ["click (>=5.0)"]
 
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 requests = ">=2.0.1,<3.0.0"
 
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+optional = false
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+
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+
+[[package]]
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+version = "2.10.0"
+description = "A library implementing the 'SemVer' scheme."
+optional = false
+python-versions = ">=2.7"
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+doc = ["Sphinx", "sphinx-rtd-theme"]
+
+[[package]]
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+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
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+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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 version = "1.3.1"
@@ -1824,6 +2521,17 @@ requests = ">=2.26.0"
 [package.extras]
 blobfile = ["blobfile (>=2)"]
 
+[[package]]
+name = "tomlkit"
+version = "0.13.2"
+description = "Style preserving TOML library"
+optional = false
+python-versions = ">=3.8"
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 version = "4.67.0"
@@ -1845,6 +2553,23 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "typer"
+version = "0.15.1"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
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+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
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 name = "typing-extensions"
 version = "4.12.2"
@@ -1871,6 +2596,17 @@ files = [
 mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
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 version = "2.2.3"
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+]
+
 [[package]]
 name = "yarl"
 version = "1.17.1"
@@ -2006,4 +2820,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "ddbbac62d53ae65850f61b3d5f627b4f2a0a999455d5869dbd1eb1b0ae7144a5"
+content-hash = "fc3fccebdc1dad30f562185560b085f474738badd36f1a674b28d11c1fe7ed23"

pyproject.toml

@@ -14,6 +14,8 @@ langchain-community = "^0.3.7"
 pydantic = "^2.9.2"
 fastapi = "^0.115.5"
 uvicorn = "^0.32.0"
+gradio = "^5.8.0"
+langchain-groq = "^0.2.1"
 
 
 [build-system]