Initial commit for Hugging Face Space

app.py

@@ -1,49 +1,78 @@
 import streamlit as st
-from time import sleep
 from retriever import load_vector_store
 from langgraph_graph import generate_answer
+from time import sleep
+
+# Load vector DB
+db = load_vector_store()
 
 st.set_page_config("MedMCQA Chatbot", page_icon="🩺")
 
-# 🌗 Sidebar
+# 🌗 Theme toggle sidebar
 with st.sidebar:
     st.title("🩺 MedMCQA Chatbot")
     theme_mode = st.radio("🌓 Theme", ["Light", "Dark"], horizontal=True)
 
-# 🌓 Theme
+# 🌓 Apply selected theme
 if theme_mode == "Dark":
     st.markdown("""
         <style>
-        body, .stApp { background-color: #1e1e1e !important; color: #eee !important; }
-        .stTextInput input { background-color: #333 !important; color: #eee !important; }
-        input::placeholder { color: #bbb !important; }
-        .stButton>button { background-color: #444 !important; color: #eee !important; }
+        :root { --text-color: #eee; }
+        body, .stApp {
+            background-color: #1e1e1e !important;
+            color: var(--text-color) !important;
+        }
+        .stTextInput input {
+            background-color: #333 !important;
+            color: var(--text-color) !important;
+        }
+        .stTextInput label {
+            color: var(--text-color) !important;
+        }
+        input::placeholder {
+            color: #bbb !important;
+        }
+        .stButton>button {
+            background-color: #444 !important;
+            color: var(--text-color) !important;
+        }
         </style>
     """, unsafe_allow_html=True)
 else:
     st.markdown("""
         <style>
-        body, .stApp { background-color: #ffffff !important; color: #111 !important; }
-        .stTextInput input { background-color: #f0f0f0 !important; color: #111 !important; }
-        input::placeholder { color: #444 !important; }
-        .stButton>button { background-color: #e0e0e0 !important; color: #111 !important; }
+        :root { --text-color: #111; }
+        body, .stApp {
+            background-color: #ffffff !important;
+            color: var(--text-color) !important;
+        }
+        .stTextInput input {
+            background-color: #f0f0f0 !important;
+            color: var(--text-color) !important;
+        }
+        .stTextInput label {
+            color: var(--text-color) !important;
+        }
+        input::placeholder {
+            color: #444 !important;
+        }
+        .stButton>button {
+            background-color: #e0e0e0 !important;
+            color: var(--text-color) !important;
+        }
         </style>
     """, unsafe_allow_html=True)
 
+# 🧠 App title
 st.header("🩺 MedMCQA Chatbot")
 st.caption("Ask a medical question and get answers from the MedMCQA dataset only. If not found, it will respond gracefully.")
 
-# ✅ Cache FAISS loading to avoid blocking
-@st.cache_resource(show_spinner="Loading FAISS Index... Please wait ⏳")
-def get_vector_store():
-    return load_vector_store()
-
-# Load vector store in the background
-db = get_vector_store()
-
-# ✏️ Query
-query = st.text_input("🔍 Enter your medical question:",
-                      placeholder="e.g., What is the mechanism of Aspirin?")
+# ✏️ Query box
+query = st.text_input(
+    "🔍 Enter your medical question:",
+    placeholder="e.g., What is the mechanism of Aspirin?",
+    label_visibility="visible"
+)
 
 # 🚀 Answer generation
 if query:
@@ -53,18 +82,32 @@ if query:
     with st.spinner("🧠 Generating answer..."):
         response = generate_answer(query, context)
 
-    # Animated answer
-    st.markdown("<h4>🧠 Answer:</h4>", unsafe_allow_html=True)
+    st.markdown("""
+    <style>
+    .fade-in {
+        animation: fadeIn 0.7s ease-in;
+    }
+    @keyframes fadeIn {
+        0% { opacity: 0; transform: translateY(20px); }
+        100% { opacity: 1; transform: translateY(0); }
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    st.markdown("<div class='fade-in'><h4>🧠 Answer:</h4></div>", unsafe_allow_html=True)
     answer_placeholder = st.empty()
     final_text = ""
     for char in response:
         final_text += char
-        answer_placeholder.markdown(final_text)
+        answer_placeholder.markdown(f"<div class='fade-in'>{final_text}</div>", unsafe_allow_html=True)
         sleep(0.01)
 
     with st.expander("🔎 Top Matches"):
         for i, doc in enumerate(results, 1):
-            st.markdown(f"**Result {i}:**\n\n{doc.page_content}")
+            content = doc.page_content
+            if query.lower() in content.lower():
+                content = content.replace(query, f"**{query}**")
+            st.markdown(f"**Result {i}:**\n\n{content}")
 
 # 📬 Sidebar Contact
 with st.sidebar:
@@ -72,4 +115,65 @@ with st.sidebar:
     st.markdown("### 📬 Contact")
     st.markdown("[📧 Email](mailto:[email protected])")
     st.markdown("[🔗 LinkedIn](https://linkedin.com/in/sankethhonavar)")
-    st.markdown("[💻 GitHub](https://github.com/SankethHonavar)")
+    st.markdown("[💻 GitHub](https://github.com/sankethhonavar)")
+
+# ✨ Floating Icons (Right side - Top aligned)
+st.markdown("""
+<style>
+.floating-button {
+    position: fixed;
+    top: 80px;
+    right: 20px;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+    z-index: 9999;
+}
+.floating-button a {
+    background-color: #0077b5;
+    color: white;
+    padding: 10px 14px;
+    border-radius: 50%;
+    text-align: center;
+    font-size: 20px;
+    text-decoration: none;
+    box-shadow: 2px 2px 8px rgba(0, 0, 0, 0.3);
+    transition: background-color 0.3s;
+}
+.floating-button a:hover {
+    background-color: #005983;
+}
+.floating-button a.email {
+    background-color: #444444;
+}
+.floating-button a.email:hover {
+    background-color: #222222;
+}
+.floating-button a.github {
+    background-color: #171515;
+}
+.floating-button a.github:hover {
+    background-color: #000000;
+}
+</style>
+
+<div class="floating-button">
+    <a href="mailto:[email protected]" class="email" title="Email Me">
+        <img src="https://img.icons8.com/ios-filled/25/ffffff/new-post.png" alt="Email"/>
+    </a>
+    <a href="https://linkedin.com/in/sankethhonavar" target="_blank" title="LinkedIn">
+        <img src="https://img.icons8.com/ios-filled/25/ffffff/linkedin.png" alt="LinkedIn"/>
+    </a>
+    <a href="https://github.com/SankethHonavar" target="_blank" class="github" title="GitHub">
+        <img src="https://img.icons8.com/ios-filled/25/ffffff/github.png" alt="GitHub"/>
+    </a>
+</div>
+""", unsafe_allow_html=True)
+
+# 📄 Footer
+st.markdown("""
+---
+<p style='text-align: center; font-size: 0.9rem; color: grey'>
+Made with ❤️ by <a href='https://linkedin.com/in/sankethhonavar' target='_blank'>Sanketh Honavar</a>
+</p>
+""", unsafe_allow_html=True)

build_faiss_index.py

@@ -1,39 +0,0 @@
-# build_faiss_index.py
-import faiss
-import pickle
-import numpy as np
-from datasets import load_dataset
-from langchain.embeddings import HuggingFaceEmbeddings  # Or your embedding class
-
-def format_entry(entry):
-    return {
-        "question": entry["question"],
-        "formatted": (
-            f"Q: {entry['question']}\n"
-            f"A. {entry['opa']}  B. {entry['opb']}  C. {entry['opc']}  D. {entry['opd']}\n"
-            f"Correct Answer: {entry['cop']}\n"
-            f"Explanation: {entry['exp']}"
-        )
-    }
-
-print("Loading MedMCQA dataset (5000 rows)...")
-dataset = load_dataset("medmcqa", split="train[:5000]")
-formatted_data = [format_entry(entry) for entry in dataset]
-
-# Extract questions for embeddings
-questions = [entry["formatted"] for entry in formatted_data]
-
-print("Generating embeddings...")
-embeddings_model = HuggingFaceEmbeddings()  # Or your specific embeddings
-vectors = np.array([embeddings_model.embed_query(q) for q in questions], dtype="float32")
-
-print("Building FAISS index...")
-index = faiss.IndexFlatL2(vectors.shape[1])
-index.add(vectors)
-
-# Save FAISS index and data
-faiss.write_index(index, "data/medmcqa_index/index.faiss")
-with open("data/medmcqa_index/index.pkl", "wb") as f:
-    pickle.dump(formatted_data, f)
-
-print("FAISS index saved successfully!")

dataset_loader.py

@@ -1,11 +1,18 @@
 # dataset_loader.py
-import pickle
-import faiss
+from datasets import load_dataset
 
-def load_faiss_index():
-    print("Loading FAISS index...")
-    index = faiss.read_index("data/medmcqa_index/index.faiss")
-    with open("data/medmcqa_index/index.pkl", "rb") as f:
-        formatted_data = pickle.load(f)
-    print("FAISS index loaded successfully!")
-    return index, formatted_data
+def load_medmcqa_subset(limit=5000):
+    dataset = load_dataset("medmcqa", split=f"train[:{limit}]")
+
+    def format_entry(entry):
+        return {
+            "question": entry["question"],
+            "formatted": (
+                f"Q: {entry['question']}\n"
+                f"A. {entry['opa']}  B. {entry['opb']}  C. {entry['opc']}  D. {entry['opd']}\n"
+                f"Correct Answer: {entry['cop']}\n"
+                f"Explanation: {entry['exp']}"
+            )
+        }
+
+    return [format_entry(entry) for entry in dataset]