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app.py

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+import os
+import streamlit as st
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from groq import Groq
+from dotenv import load_dotenv
+
+# Initialize Streamlit page configuration
+st.set_page_config(page_title="Medical Knowledge Assistant", layout="wide")
+
+# Add API key input in sidebar
+with st.sidebar:
+    st.header("API Key Configuration")
+    api_key = st.text_input("Enter your Groq API Key:", type="password")
+    if api_key:
+        os.environ['GROQ_API_KEY'] = api_key
+    else:
+        # Try loading from .env file
+        load_dotenv()
+        api_key = os.getenv("GROQ_API_KEY")
+        if api_key:
+            st.success("API Key loaded from .env file")
+
+# Check for API key before proceeding
+if not api_key:
+    st.warning("Please enter your Groq API key in the sidebar to continue.")
+    st.stop()
+
+# Initialize the app
+st.title("Medical Knowledge Assistant")
+
+try:
+    # Set up the embeddings
+    model_name = "BAAI/bge-large-en"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': False}
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+
+    # Load the vector store from the local drive
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    persist_directory = os.path.join(script_dir, 'Embedded_Med_books')
+    
+    # Debug information
+    st.sidebar.header("Debug Information")
+    st.sidebar.write("Vector store path:", persist_directory)
+    
+    with st.sidebar:
+        st.write("API Key Loaded:", "Yes" if api_key else "No")
+
+    # Check vector store directory
+    if not os.path.exists(persist_directory):
+        st.error(f"Vector store directory not found at: {persist_directory}")
+        if st.button("Create Directory"):
+            os.makedirs(persist_directory)
+            st.success("Directory created!")
+    
+    # Load the vector store
+    vector_store = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embeddings
+    )
+    retriever = vector_store.as_retriever(search_kwargs={'k': 1})
+    
+    # Initialize Groq client
+    client = Groq(api_key=api_key)
+
+    # Streamlit input
+    query = st.text_input("Enter your medical question here:")
+
+    def query_with_groq(query, retriever):
+        try:
+            # Retrieve relevant documents
+            docs = retriever.get_relevant_documents(query)
+            context = "\n".join([doc.page_content for doc in docs])
+
+            # Call the Groq API with the query and context
+            completion = client.chat.completions.create(
+                model="llama3-70b-8192",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": (
+                            "You are a knowledgeable medical assistant. For any medical term or disease, include comprehensive information covering: "
+                            "definitions, types, historical background, major theories, known causes, and contributing risk factors. "
+                            "Explain the genesis or theories on its origin, if applicable. Use a structured, thorough approach and keep language accessible. "
+                            "provide symptoms, diagnosis, and treatment and post operative care , address all with indepth explanation , with specific details and step-by-step processes where relevant. "
+                            "If the context does not adequately cover the user's question, respond with: 'I cannot provide an answer based on the available medical dataset.'"
+                        )
+                    },
+                    {
+                        "role": "system",
+                        "content": (
+                            "If the user asks for a medical explanation, ensure accuracy, don't include layman's terms if complex terms are used, "
+                            "and organize responses in a structured way."
+                        )
+                    },
+                    {
+                        "role": "system",
+                        "content": (
+                            "When comparing two terms or conditions, provide a clear, concise, and structured comparison. Highlight key differences in their "
+                            "definitions, symptoms, causes, diagnoses, and treatments with indepth explanation of each. If relevant, include any overlapping characteristics."
+                        )
+                    },
+                    {
+                        "role": "user",
+                        "content": f"{context}\n\nQ: {query}\nA:"
+                    }
+                ],
+                temperature=0.7,
+                max_tokens=3000,
+                stream=True
+            )
+
+            # Create a placeholder for streaming response
+            response_container = st.empty()
+            response = ""
+            
+            # Stream the response
+            for chunk in completion:
+                if chunk.choices[0].delta.content:
+                    response += chunk.choices[0].delta.content
+                    response_container.markdown(response)
+            
+            return response
+            
+        except Exception as e:
+            st.error(f"Error during query processing: {str(e)}")
+            return None
+
+    if st.button("Get Answer"):
+        if query:
+            with st.spinner("Processing your query..."):
+                answer = query_with_groq(query, retriever)
+                if answer:
+                    st.success("Query processed successfully!")
+        else:
+            st.warning("Please enter a query.")
+
+except Exception as e:
+    st.error(f"Initialization error: {str(e)}")

requirements.txt

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+streamlit==1.31.0
+langchain-community==0.0.16
+groq==0.4.5
+python-dotenv==1.0.1
+chromadb==0.4.22
+sentence-transformers==2.5.1
+transformers==4.37.2
+torch==2.2.0
+pydantic==2.6.1
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+numpy==1.26.3
+pandas==2.2.0
+tqdm==4.66.1
+requests==2.31.0