app.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments, DataCollatorForSeq2Seq
from datasets import Dataset, load_dataset
import gradio as gr
import torch
import os
import pandas as pd

# ------------------------------
# 1. Load dataset from CSV
# ------------------------------
CSV_FILE = "remedies.csv"  # path to your CSV
df = pd.read_csv(CSV_FILE)

# Prepare dataset in HuggingFace format
data = []
for _, row in df.iterrows():
    data.append({"input": row['symptoms'], "output": row['response']})
dataset = Dataset.from_list(data)

# ------------------------------
# 2. Model & Tokenizer
# ------------------------------
MODEL_NAME = "google/flan-t5-small"
OUTPUT_DIR = "./finetuned-bot"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

# ------------------------------
# 3. Preprocess
# ------------------------------
def preprocess(examples):
    model_inputs = tokenizer(examples['input'], truncation=True, padding='max_length', max_length=64)
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(examples['output'], truncation=True, padding='max_length', max_length=64)
    model_inputs['labels'] = labels['input_ids']
    return model_inputs

tokenized_dataset = dataset.map(preprocess, batched=True)

# ------------------------------
# 4. Data collator
# ------------------------------
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

# ------------------------------
# 5. Training
# ------------------------------
training_args = TrainingArguments(
    output_dir=OUTPUT_DIR,
    per_device_train_batch_size=4,
    num_train_epochs=3,
    logging_steps=5,
    save_steps=50,
    save_total_limit=2,
    fp16=torch.cuda.is_available(),
    report_to="none"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
    data_collator=data_collator
)

# Fine-tune only if directory is missing or empty
if not os.path.exists(OUTPUT_DIR) or not os.listdir(OUTPUT_DIR):
    trainer.train()
    model.save_pretrained(OUTPUT_DIR)
    tokenizer.save_pretrained(OUTPUT_DIR)

# ------------------------------
# 6. Load fine-tuned model
# ------------------------------
model = AutoModelForSeq2SeqLM.from_pretrained(OUTPUT_DIR)
tokenizer = AutoTokenizer.from_pretrained(OUTPUT_DIR)

# ------------------------------
# 7. Gradio UI (blue & white)
# ------------------------------
def respond(user_input, chat_history):
    inputs = tokenizer(user_input, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=64)
    reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
    chat_history = chat_history or []
    chat_history.append(("You", user_input))
    chat_history.append(("Bot", reply))
    return chat_history, chat_history

with gr.Blocks(css="""
    body {background-color: #f0f8ff;}
    .gradio-container {border-radius: 15px; padding: 20px; background-color: #ffffff;}
    .chatbot-message.user {background-color: #cce5ff; color: #000;}
    .chatbot-message.bot {background-color: #e6f0ff; color: #000;}
""") as demo:
    gr.Markdown("<h1 style='text-align:center; color:#007BFF;'>💊 Health Remedies Chatbot</h1>")
    chatbot = gr.Chatbot()
    state = gr.State([])
    with gr.Row():
        msg = gr.Textbox(placeholder="Type your message here...", scale=8)
        send = gr.Button("Send", scale=2)
    send.click(respond, [msg, state], [chatbot, state])
    msg.submit(respond, [msg, state], [chatbot, state])

demo.launch()