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Update app.py
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pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
# Use a pipeline as a high-level helper
pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3.1-70B")
# Load model directly
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-70B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-70B")
# Load sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
# Initialize conversation context
context = []
def predict(context, input_text):
"""Generate response based on context and input."""
context.append(input_text)
inputs = tokenizer(" ".join(context), return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
context.append(response)
return response
def predict_with_emotion(context, input_text):
"""Generate response with emotion detection."""
sentiment = sentiment_analyzer(input_text)[0]['label']
response = predict(context, input_text)
if sentiment == 'NEGATIVE':
response = "I'm sorry to hear that. " + response
elif sentiment == 'POSITIVE':
response = "That's great! " + response
return response
def chatbot(input_text):
"""Gradio chatbot function."""
global context
response = predict_with_emotion(context, input_text)
return response
# Create Gradio interface
iface = gr.Interface(fn=chatbot, inputs="text", outputs="text", title="Contextual Emotion-Aware LLaMA-70B Chatbot")
iface.launch()