Update app.py

app.py

@@ -1,79 +1,56 @@
-# import libraries
-import os
-from dotenv import load_dotenv
 import streamlit as st
-import pinecone
-from langchain.document_loaders import PyPDFDirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain_pinecone import PineconeVectorStore
-from langchain.prompts import PromptTemplate
-from langchain.chains.question_answering import load_qa_chain
-from langchain_community.llms import CTransformers
-from langchain_community.embeddings.huggingface import HuggingFaceBgeEmbeddings
-
-
-load_dotenv()
-
-embeddings = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
-                                          model_kwargs = {'device':'cpu'})
-
-os.environ['PINECONE_API_KEY'] = 'afb0bb4d-3c15-461b-91a4-fb12fb1f25f2'
-index_name = 'harisonvecot'
-
-vectorstore = PineconeVectorStore(index_name=index_name,embedding=embeddings)
-
-# Create the vector index from documents
-def create_index(documents):
-    vectorstore.add_documents(documents)
-
-# Retrieve query from Pinecone
-def retrieve_query(query, k=2):
-    matching_results = vectorstore.similarity_search(query, k=k)
-    return matching_results
-
-# Custom prompt template
-custom_prompt_template = '''
-use the following pieces of information to answer the user's questions.
-If you don't know the answer, please just say that you don't know the answer, don't try to make up an answer.
-Content : {context}
-Question : {question}
-only return the helpful answer below and nothing else.
-'''
-
-def set_custom_prompt():
-    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context', 'question'])
-    return prompt
-
-# Load LLM model
-llm_model = CTransformers(model='TheBloke/Llama-2-7B-Chat-GGML',
-                          model_type = 'llama',
-                          max_new_token = 512,
-                          temperature=0.5)
-
-# Create retrieval QA chain
-def retrieval_qa_chain():
-    prompt = set_custom_prompt()
-    chain = load_qa_chain(llm_model, chain_type='stuff', prompt=prompt)
-    return chain
-
-# Search answers from Vector DB
-def retrieve_answer(query):
-    doc_search = retrieve_query(query)
-    chain = retrieval_qa_chain()
-    response = chain.run(input_documents=doc_search, question=query)
-    return response
-
-queries = st.text_input('write a medical questions ?')
-# Example usage
-submit = st.button('submit')
-    # Read and process documents
-#     doc = read_doc('documents/')
-#     documents = chunk_data(docs=doc)
-#     create_index(documents)
-if submit :
-    if queries :
-    # Query and get answer
-    #our_query = 'What is cause of Eczema?'
-        answer = retrieve_answer(queries)
-        st.write(answer)
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load Data
+@st.cache_data
+def load_data():
+    return pd.read_csv("data/inbody_with_clusters.csv", parse_dates=["Test Date"])
+
+df = load_data()
+
+st.title("📊 InBody 270 Dashboard")
+st.markdown("Explore trends, clusters, and body composition metrics.")
+
+# Sidebar Filters
+with st.sidebar:
+    st.header("🔎 Filter")
+    id_options = df['ID'].unique()
+    selected_ids = st.multiselect("Select Patient ID(s)", id_options, default=list(id_options[:5]))
+
+    gender = st.radio("Gender", options=["All", "M", "F"])
+    bmi_range = st.slider("BMI Range", float(df["27. BMI (Body Mass Index)"].min()), 
+                          float(df["27. BMI (Body Mass Index)"].max()), (18.5, 35.0))
+
+# Apply Filters
+filtered = df[df["ID"].isin(selected_ids)]
+if gender != "All":
+    filtered = filtered[filtered["3. Gender"] == gender]
+filtered = filtered[(filtered["27. BMI (Body Mass Index)"] >= bmi_range[0]) & 
+                    (filtered["27. BMI (Body Mass Index)"] <= bmi_range[1])]
+
+# Line Chart: Weight/BMI/BFM over time
+st.subheader("📈 Trends Over Time")
+metric = st.selectbox("Select a Metric to Plot", ['6. Weight', '27. BMI (Body Mass Index)', '18. BFM (Body Fat Mass)'])
+
+fig, ax = plt.subplots(figsize=(10, 5))
+sns.lineplot(data=filtered, x="Test Date", y=metric, hue="ID", marker="o", ax=ax)
+st.pyplot(fig)
+
+# Cluster Scatterplot
+st.subheader("🧬 Body Type Clusters")
+fig2, ax2 = plt.subplots()
+sns.scatterplot(data=filtered, x="18. BFM (Body Fat Mass)", y="24. SMM (Skeletal Muscle Mass)", 
+                hue="Body Type Cluster", palette="Set1", ax=ax2)
+st.pyplot(fig2)
+
+# Summary Table
+st.subheader("📋 Latest Measurements (Per Patient)")
+latest = filtered.sort_values("Test Date").groupby("ID").tail(1)
+st.dataframe(latest[["ID", "Test Date", "6. Weight", "27. BMI (Body Mass Index)", 
+                     "18. BFM (Body Fat Mass)", "24. SMM (Skeletal Muscle Mass)",
+                     "58. BMR (Basal Metabolic Rate)", "Body Type Cluster"]])
+
+# Download option
+st.download_button("📥 Download Filtered Data", latest.to_csv(index=False), file_name="filtered_inbody.csv")