updated database_response page

.dockerignore

@@ -0,0 +1,6 @@
+.venv
+.env
+vector-db
+src/backend/data/dataset.csv
+project_requirement
+logs

README.md

@@ -79,7 +79,7 @@ pip install -r requirements.txt
 To run yuvabe_care_companion_ai, execute the following command:
 
 ```bash
-streamlit run yuvabe_care_companion_ai.py
+streamlit run src/frontend/home.py
 ```
 
 This will start the Streamlit server, and you should see output indicating the local URL where the app is being served, typically `http://localhost:8501`.

src/backend/data/chroma_db.py

@@ -7,10 +7,10 @@ logger = logger.get_logger()
 
 # Constants
 COLLECTION_NAME = "care_companion_ai_vectors"
-DB_PATH = "vector-db"
+DB_PATH = "./src/backend/vector-db"
 
 # Initialize ChromaDB Client
-client = chromadb.PersistentClient(path="src/backend/vector-db")
+client = chromadb.PersistentClient(path=DB_PATH)
 
 collection = client.get_or_create_collection(
     name=COLLECTION_NAME,

src/backend/models/llm_model.py

@@ -22,12 +22,12 @@ SYSTEM_PROMPT = """You are Yuvabe Care Companion AI, an advanced healthcare assi
 ⚠️ *Important*: You are not a certified doctor. Always remind users to consult a healthcare professional for medical decisions.
 """
 
-def get_medical_assistant_response(prompt: str):
+def get_medical_assistant_response(prompt: list):
     try:
-        if not prompt or len(prompt.strip()) < 5:
+        if not prompt or len(prompt[0]) < 5:
             return "⚠️ Your question seems too short. Please provide more details so I can assist you better."
-
-        response = chroma_db.search_vector_store(prompt)
+        querry = prompt[-1]
+        response = chroma_db.search_vector_store(querry)
         
         if response and "metadatas" in response and response["metadatas"]:
             retrieved_contexts = [metadata['answer'] for metadata in response["metadatas"][0]]
@@ -35,7 +35,7 @@ def get_medical_assistant_response(prompt: str):
         else:
             context = "No relevant information found in the database."
 
-        system_prompt = f"""
+        rag_prompt = f"""
         You are a helpful medical assistant. Use the provided context to answer the question as accurately as possible.
         If the context is not relevant, rely on your knowledge to answer.
         
@@ -44,11 +44,13 @@ def get_medical_assistant_response(prompt: str):
         
         User Question: {prompt}
         """
+        # messages.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
+        # messages[-1]["content"] = rag_prompt
 
         chat_completion = client.chat.completions.create(
             messages=[
                 {"role": "system", "content": SYSTEM_PROMPT},
-                {"role": "user", "content": system_prompt},
+                {"role": "user", "content": rag_prompt},
             ],
             model=LLM_MODEL_NAME,
         )

src/backend/models/schemas.py

@@ -2,5 +2,5 @@ from pydantic import BaseModel
 from typing import List,Dict, Optional
 
 class Chat_Response(BaseModel):
-    prompt: Optional[str] = None
+    prompt: Optional[List] = None
     response: Optional[Dict] = None

src/backend/routes/chat_api.py

@@ -21,7 +21,10 @@ async def get_assistant_response(chat_request: Chat_Response):
 @router.post("/db_response")
 async def get_db_response(chat_request: Chat_Response):
     try:
-        response_text  = chroma_db.search_vector_store(chat_request.prompt)
+        logger.info(f"Received user prompt: {chat_request.prompt}")
+        query = chat_request.prompt[-1]
+        response_text  = chroma_db.search_vector_store(query)
+        logger.info(f"Retrieved context for user prompt: {chat_request.prompt[:50]}...")
         return {"status": "success", "response": response_text}
     except Exception as e:
         logger.exception("Unexpected error occurred while processing the request.")

src/frontend/app/common_fuctions.py

@@ -19,7 +19,7 @@ def img_to_base64(image_path):
 API_URL = os.getenv("API_URL", "http://127.0.0.1:8000")
 
 
-def get_api_response(endpoint:str, prompt: str):
+def get_api_response(endpoint:str, prompt: list):
     try:
         response = requests.post(f"{API_URL}/{endpoint}", json={"prompt": prompt})
         if response.status_code == 200:

src/frontend/app/homepage.py

@@ -51,7 +51,7 @@ def handle_user_input():
             with st.spinner("Processing your query..."):
                 try:
                     endpoint = "/chat/agent_response"
-                    response = common_fuctions.get_api_response(endpoint, prompt)
+                    response = common_fuctions.get_api_response(endpoint, [prompt])
                 except Exception as e:
                     logger.error(f"AI response generation failed: {str(e)}")
                     response = "⚠️ Sorry, I couldn't process your request. Please try again later."

src/frontend/pages/database_response_page.py

@@ -2,15 +2,28 @@ import streamlit as st
 from app import homepage
 from app import common_fuctions
 
+SAMPLE_QUESTION_1 = "Last night I broke out in a sweat and got shaky then I felt like I was getting the flu so I went to bed at 7pm. I dont think I have the flu but I just ate two cookies and i got sweaty again. Im not feeling myself at all. I am 47 on hormones with a long history of family heart disease."
+SAMPLE_QUESTION_2 = " I am feeling really weak today I was vomiting yesterday all day with a high heart rate and sweating like crazy. I thought I was just hungover but I don t think that s the case. I also have a small bump on my forehead that is swollen and tender to the touch."
+SAMPLE_QUESTION_3 = "I just woke up now, It s 7pm. It was a bit of an offsleep, but when i woke up i felt really weak, shaky, almost lighter. I m still shaking now. I tried just waking up a bit, Drinking some water and milk, Went to the bathroom. But i still fell weak shaky and light"
+
 homepage.config_homepage()
 st.title("Database Response")
 st.write("This page is used to get the response from the database")
 prompt = st.text_input("Enter your prompt")
-if st.button("Get DB response"):
-    endpoint = "/chat/db_response"
-    response = common_fuctions.get_api_response(endpoint, prompt)
-    for metadata_group in response["metadatas"]:
-        for entry in metadata_group:
-            st.write("Question:", entry["question"])
-            st.write("Answer:", entry["answer"])
-            st.write("-" * 80) 
+if prompt == "":
+    selected_option = st.selectbox("Select a sample question", ["",SAMPLE_QUESTION_1, SAMPLE_QUESTION_2, SAMPLE_QUESTION_3])
+    if selected_option not in ["", None]:
+        prompt = selected_option
+if prompt:
+    if st.button("Get DB response"):
+        endpoint = "/chat/db_response"
+        response = common_fuctions.get_api_response(endpoint, [prompt])
+        st.subheader("✅ Relevant question and answer pair found in the database.")
+        for metadata_group in response["metadatas"]:
+            for entry in metadata_group:
+                st.write("Question:", entry["question"])
+                st.write("Answer:", entry["answer"])
+                st.write("-" * 80) 
+
+        if st.button("Clear chat"):
+            st.rerun()