Update app.py

app.py

@@ -1,65 +1,65 @@
-import streamlit as st
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-import os
-from dotenv import load_dotenv
-load_dotenv()
-
-def initialize_groq_llm():
-    return ChatGroq(
-        groq_api_key=os.getenv("GROQ_API_KEY"),
-        model_name="llama-3.3-70b-versatile",
-        max_tokens=512
-    )
-
-embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-faiss_index = FAISS.load_local(
-    "medical_faiss_index",
-    embedding_model,
-    allow_dangerous_deserialization=True
-)
-
-prompt_template = """
-You are a healthcare professional built by Parthib, and you can assist users with health-related issues.
-Use the following pieces of information along with the LLM's knowledge to answer the user's question about diseases or healthcare.
-If the following pieces provide some information, combine it with your existing knowledge to craft the most accurate and helpful response.
-Include relevant details such as home remedies, medications, and other necessary actions in a clear, point-wise manner for quick readability.
-If any other related questions arise, just say, "I am a healthcare professional."
-If you don't know the answer, just say that you don't know. Don't try to make up an answer.
-
-Context: {context}
-Question: {question}
-
-Only return the helpful answer below and nothing else.
-Helpful answer:
-"""
-
-def generate_response(question):
-
-    retriever = faiss_index.as_retriever(search_kwargs={'k': 1})
-    docs = retriever.get_relevant_documents(question)
-    context = "\n".join([doc.page_content for doc in docs])
-
-    llm = initialize_groq_llm()
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=prompt_template
-    )
-    formatted_prompt = prompt.format(context=context, question=question)
-
-    response = llm.invoke(formatted_prompt)
-    return response.content
-
-st.set_page_config(page_title="HealthCare ChatBot", page_icon="🤖", layout="centered")
-st.header("HealthCare ChatBot 🤖")
-
-user_input = st.text_input("Ask a Healthcare related question:", "")
-st.button("Generate Response")
-st.spinner('Processing')
-
-if user_input:
-    user_input = user_input.lower().strip()
-    response = generate_response(user_input)
-    st.write(f"Response: {response}")
+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+def initialize_groq_llm():
+    return ChatGroq(
+        groq_api_key=os.getenv("GROQ_API_KEY"),
+        model_name="llama-3.3-70b-versatile",
+        max_tokens=512
+    )
+
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+faiss_index = FAISS.load_local(
+    "medical_faiss_index",
+    embedding_model,
+    allow_dangerous_deserialization=True
+)
+
+prompt_template = """
+You are a healthcare professional built by Parthib Karak an AI engineering from Institute of Engineering and management,kolkata, and you can assist users with health-related issues.
+Use the following pieces of information along with the LLM's knowledge to answer the user's question about diseases or healthcare.
+If the following pieces provide some information, combine it with your existing knowledge to craft the most accurate and helpful response.
+Include relevant details such as home remedies, medications, and other necessary actions in a clear, point-wise manner for quick readability.
+If any other related questions arise, just say, "I am a healthcare professional.How may i assist you today?"
+If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def generate_response(question):
+
+    retriever = faiss_index.as_retriever(search_kwargs={'k': 1})
+    docs = retriever.get_relevant_documents(question)
+    context = "\n".join([doc.page_content for doc in docs])
+
+    llm = initialize_groq_llm()
+    prompt = PromptTemplate(
+        input_variables=["context", "question"],
+        template=prompt_template
+    )
+    formatted_prompt = prompt.format(context=context, question=question)
+
+    response = llm.invoke(formatted_prompt)
+    return response.content
+
+st.set_page_config(page_title="HealthCare ChatBot", page_icon="🤖", layout="centered")
+st.header("HealthCare ChatBot 🤖")
+
+user_input = st.text_input("Ask a Healthcare related question:", "")
+st.button("Generate Response")
+st.spinner('Processing')
+
+if user_input:
+    user_input = user_input.lower().strip()
+    response = generate_response(user_input)
+    st.write(f"Response: {response}")