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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import csv
from datetime import datetime
import pandas as pd
# Page config
st.set_page_config(page_title="Buddy - Indic Student Chatbot", layout="centered")
# Load model
@st.cache_resource
def load_model():
model_name = "ai4bharat/IndicBART"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
return tokenizer, model
tokenizer, model = load_model()
# System prompt
system_prompt = "You are Buddy, a friendly assistant who helps students in Telugu, Hindi, or English. You give simple and polite answers."
# Response function
def indic_answer(query):
input_text = f"{system_prompt}\nQuestion: {query}\nAnswer:"
inputs = tokenizer([input_text], return_tensors="pt", padding=True)
is_short = len(query.strip()) < 25
output = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=100,
do_sample=not is_short,
temperature=0.8 if not is_short else 1.0,
top_k=50 if not is_short else 0,
top_p=0.95 if not is_short else 1.0,
num_beams=4 if is_short else 1
)
raw_output = tokenizer.decode(output[0], skip_special_tokens=True)
# Clean up output
cleaned = raw_output.replace(system_prompt, "")
cleaned = cleaned.replace("Question:", "").replace("Answer:", "").strip()
return cleaned
# UI: Title
st.title("🤖 Buddy - Indic Student Chatbot")
st.markdown("Ask me anything in **Telugu**, **Hindi**, or **English**!")
# Chat state
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# User input
user_input = st.text_input("You:", key="user_input")
# Handle input
if st.button("Send"):
if user_input:
st.session_state.chat_history.append(("You", user_input))
response = indic_answer(user_input)
st.session_state.chat_history.append(("Buddy", response))
# Display chat
st.markdown("### 💬 Conversation")
for sender, message in st.session_state.chat_history:
st.markdown(f"**{sender}:** {message}")
# Feedback
if st.session_state.chat_history:
st.markdown("---")
st.markdown("### 🙋 Rate Buddy's Response")
rating = st.slider("How helpful was Buddy?", 1, 5, 3)
comment = st.text_input("Your suggestion or feedback")
if st.button("Submit Feedback"):
with open("feedback.csv", mode="a", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow([
datetime.now(),
user_input,
st.session_state.chat_history[-1][1],
rating,
comment
])
st.success("✅ Feedback submitted!")
# Clear chat
if st.button("Clear Chat"):
st.session_state.chat_history = []
st.experimental_rerun()
# Download chat
if st.session_state.chat_history:
df = pd.DataFrame(st.session_state.chat_history, columns=["Sender", "Message"])
st.download_button("📥 Download Chat", df.to_csv(index=False), "chat_history.csv", "text/csv")
# Footer
st.markdown("""
<hr style='margin-top: 50px;'>
<p style='text-align: center; font-size: 14px; color: gray;'>
© 2025 Buddy AI • Open-Source for Educational Use 🇮🇳
</p>
""", unsafe_allow_html=True)
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