Update app.py

app.py

@@ -1,23 +1,20 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments, DataCollatorForSeq2Seq
-from datasets import Dataset
+from datasets import Dataset, load_dataset
 import gradio as gr
 import torch
 import os
+import pandas as pd
 
 # ------------------------------
-# 1. Internal Dataset
+# 1. Load dataset from CSV
 # ------------------------------
-data = [
-    {"input": "What should I do for a cold?", "output": "I'm sorry you're feeling unwell. Drink warm water, get rest, and consider vitamin C."},
-    {"input": "What to do if I have a headache?", "output": "I understand headaches are frustrating. Try meditation, rest, and stay hydrated."},
-    {"input": "My child has fever, what do I do?", "output": "Give paracetamol, keep them hydrated, and if fever persists, consult a doctor."},
-    {"input": "Who can I contact for fever treatment?", "output": "You can reach Dr. Ankit Verma at +91-9876543210 or Dr. Priya Singh at +91-9123456780."},
-    {"input": "I feel dizzy, what should I do?", "output": "Sit down, drink water, and rest. If it continues, see a doctor."},
-    {"input": "I am anxious and need help.", "output": "Feeling anxious is okay. Try deep breathing. You can also speak with Dr. Richa Nair at +91-9874455667."},
-    {"input": "I have mild back pain.", "output": "Gentle stretching and rest can help. For consultation, Dr. Amit Khanna +91-9988774455 is available."},
-    {"input": "My child has a cough and cold.", "output": "Dr. Sneha Kapoor at +91-9871122334 and Dr. Arjun Mehta at +91-9112233445 can assist. Keep your child warm."}
-]
+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)
 
 # ------------------------------
@@ -64,7 +61,7 @@ trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_dataset,
-    tokenizer=tokenizer,  # FIXED
+    tokenizer=tokenizer,
     data_collator=data_collator
 )
 
@@ -81,7 +78,7 @@ model = AutoModelForSeq2SeqLM.from_pretrained(OUTPUT_DIR)
 tokenizer = AutoTokenizer.from_pretrained(OUTPUT_DIR)
 
 # ------------------------------
-# 7. Gradio UI (chat-style)
+# 7. Gradio UI (blue & white)
 # ------------------------------
 def respond(user_input, chat_history):
     inputs = tokenizer(user_input, return_tensors="pt")
@@ -92,8 +89,13 @@ def respond(user_input, chat_history):
     chat_history.append(("Bot", reply))
     return chat_history, chat_history
 
-with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align:center; color:#4CAF50;'>💊 Health Remedies Chatbot</h1>")
+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():
@@ -103,4 +105,3 @@ with gr.Blocks() as demo:
     msg.submit(respond, [msg, state], [chatbot, state])
 
 demo.launch()
-