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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+medical_faiss_index/index.faiss filter=lfs diff=lfs merge=lfs -text

medical_faiss_index/index.faiss

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f279feeaec484136ccbf7a2ae999f1eb5cc1d597db08e31bd432fa7cc86de9c
+size 12602925

medical_faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a74b92117a85caa468fbdae68669546319387f579bf3d03b8bcf85a7281284a
+size 4653572

requirements.txt

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+streamlit
+langchain
+langchain_groq
+sentence-transformers
+faiss-cpu
+python-dotenv
+langchain-community

streamlitapp.py

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+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}")