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import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
# Load chatbot model
chatbot_model = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
model = AutoModelForCausalLM.from_pretrained(chatbot_model)
# Load emotion detection model
emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
def generate_response(user_input):
# Generate chatbot response
input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
# Detect emotion
emotion_result = emotion_pipeline(user_input)
emotion = emotion_result[0]["label"]
score = emotion_result[0]["score"]
return response, emotion, score
# Gradio Interface
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(label="Enter your message"),
outputs=[
gr.Textbox(label="Chatbot Response"),
gr.Textbox(label="Emotion Detected"),
gr.Textbox(label="Emotion Score")
],
live=False
)
iface.launch()
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