Update app.py

app.py

@@ -25,6 +25,10 @@ qdrant_api_key = os.getenv('qdrant_api_key')
 
 
 
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import AIMessage, HumanMessage
+
+
 #csv loader
 loader = CSVLoader(file_path='data.csv')
 data=loader.load()
@@ -37,6 +41,7 @@ texts = text_splitter.split_documents(data)
 #embeding
 embeding=OpenAIEmbeddings(openai_api_key=openai_api_key, model="text-embedding-3-small")
 
+
 #import quantization
 
 from langchain.vectorstores import Qdrant
@@ -52,7 +57,7 @@ qdrant = Qdrant.from_documents(
             url=qdrant_url,
             prefer_grpc=True,
             api_key=qdrant_api_key,
-            collection_name="llm_app_01",
+            collection_name="llm_app",
             quantization_config=models.BinaryQuantization(
                 binary=models.BinaryQuantizationConfig(
                     always_ram=True,
@@ -60,6 +65,7 @@ qdrant = Qdrant.from_documents(
             )
 )
 
+
 #qdrant client
 qdrant_client = QdrantClient(
     url=qdrant_url,
@@ -72,12 +78,17 @@ from re import search
 retriver=qdrant.as_retriever( search_type="similarity", search_kwargs={"k":2})
 
 
+#search query
+query="show me a best darmatology doctor in peshawar  "
+docs=retriver.get_relevant_documents(query)
+
+
 from langchain import PromptTemplate
 
 prompt = PromptTemplate(
     template="""
         # Your Role
-        You are a highly skilled AI specialized in healthcare and medical information retrieval. Your expertise lies in understanding the medical needs of patients and accurately matching them with the most suitable healthcare professionals based on the given context.
+        You are a highly skilled AI specialized in healthcare and medical information retrieval. Your expertise lies in understanding the medical needs of patients and accurately matching them with the most suitable healthcare professionals, including but not limited to surgeons, dentists, dermatologists, cardiologists, neurologists, etc., based on the user's query and the provided context.
 
         # Instruction
         Your task is to answer the question using the following pieces of retrieved context delimited by XML tags.
@@ -88,26 +99,26 @@ prompt = PromptTemplate(
         </retrieved context>
 
         # Constraint
-        1. Carefully consider the user's question:
+        1. Carefully analyze the user's question:
         User's question:\n{question}\n
-        Analyze the intent behind the question, particularly in relation to the medical context, and provide a precise and helpful answer.
-        - Reflect on why the question was asked and provide an appropriate response based on the context you understand.
+        Your goal is to understand the user's needs and match them with the most relevant healthcare professional(s) from the provided context.
+        - Reflect on why the question was asked, and deliver an appropriate response based on the context you understand.
         2. Select the most relevant information (the key details directly related to the question) from the retrieved context and use it to formulate an answer.
-        3. Generate a concise, logical, and medically accurate answer. When generating the answer, include the following details about the doctor in a bulleted format:
-            • Doctor Name: Dr. Shahzad Rashid Awan
-            • City: Peshawar
-            • Specialization: Dermatologist
-            • Qualification: MBBS, MCPS (Dermatology)
-            • Experience: 12 years
-            • Patient Satisfaction Rate: 93%
-            • Avg Time to Patients: 13 mins
-            • Wait Time: 10 mins
-            • Hospital Address: Rahim Medical Center And Hospital, Hasht Nagri, Peshawar
-            • Fee: PKR 1000
-            • Profile Link: https://www.marham.pk/doctors/peshawar/dermatologist/dr-shahzad-rashid-awan#reviews-scroll
-        4. If the retrieved context does not contain information relevant to the question, or if the documents are irrelevant, respond with 'I can't find the answer to that question in the material I have'.
-        5. Limit the answer to five sentences maximum. Ensure the answer is concise, logical, and medically appropriate.
-        6. At the end of the response, provide the doctor's profile metadata as shown in the relevant documents, ensuring all bullet points are clearly mentioned.
+        3. Generate a comprehensive, logical, and medically accurate answer. When generating the answer, include the following details about the healthcare professional:
+            • Name of the Professional
+            • City
+            • Specialization (e.g., Surgeon, Dentist, Cardiologist, etc.)
+            • Qualification (e.g., MBBS, FCPS, etc.)
+            • Years of Experience
+            • Patient Satisfaction Rate (if available)
+            • Average Time Spent with Patients (if available)
+            • Wait Time (if available)
+            • Hospital/Clinic Address
+            • Consultation Fee
+            • Profile Link (if available)
+        4. If the retrieved context does not contain enough relevant information, or if the documents are irrelevant, respond with 'I can't find the answer to that question in the material I have'.
+        5. Provide a complete answer to the user. Do not limit the information if there is more useful data available in the retrieved context.
+        6. At the end of the response, do not include any unnecessary metadata (such as Source, Row, or _id). Only focus on the healthcare professional's information relevant to the user's query.
 
         # Question:
         {question}""",
@@ -115,8 +126,10 @@ prompt = PromptTemplate(
 )
 
 
-from langchain.chat_models import ChatOpenAI
-llm = ChatOpenAI(model_name="gpt-4o", temperature=0, openai_api_key=openai_api_key)
+#import ChatOpenAI
+# llm = ChatOpenAI(model_name="gpt-4o", temperature=0, openai_api_key=openai_api_key)
+
+
 
 def format_docs(docs):
         formatted_docs = []
@@ -133,7 +146,9 @@ def format_docs(docs):
         # Join all formatted documents with double newlines
         return "\n\n".join(formatted_docs)
 
-from langchain_core.output_parsers import StrOutputParser
+#import strw
+
+        from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
 rag_chain = (
             {"context":  retriver| format_docs, "question": RunnablePassthrough()}
@@ -142,17 +157,13 @@ rag_chain = (
             | StrOutputParser()
         )
 
-
-
-
-
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import AIMessage, HumanMessage
 import openai
 import os
 import gradio as gr
 
-llm = ChatOpenAI(temperature=1.0, model='gpt-4o', openai_api_key=openai_api_key)
+llm = ChatOpenAI(temperature=0.5, model='gpt-4o', openai_api_key=openai_api_key)
 
 def reg(message, history):
     history_langchain_format = []
@@ -162,6 +173,14 @@ def reg(message, history):
     history_langchain_format.append(HumanMessage(content=message))
     gpt_response = llm(history_langchain_format)
     return rag_chain.invoke(message)
+# Gradio ChatInterface
+demo = gr.ChatInterface(
+    fn=reg,
+    title="Doctors Appointments Assistant",
+    theme="soft",
+)
+
+demo.launch(show_api=False)
+
 
-gr.ChatInterface(reg).launch()