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import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
from groq import Groq
import os
# Set Groq API key
os.environ["GROQ_API_KEY"] = "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"
# Text Classification Setup
classifier = pipeline("zero-shot-classification",
model="facebook/bart-large-mnli")
# Chatbot Setup
client = Groq()
system_prompt = """You are an advanced AI assistant with deep contextual understanding.
Maintain natural conversation while demonstrating:
1. Complex sentence comprehension
2. Contextual awareness across multiple turns
3. Emotional intelligence
4. Domain-specific knowledge adaptation"""
def classify_text(text, labels):
labels = [label.strip() for label in labels.split(",")]
results = classifier(text, labels, multi_label=False)
return {label: score for label, score in zip(results["labels"], results["scores"])}
def groq_chat(user_input, history):
conversation = [{"role": "system", "content": system_prompt}]
for user, assistant in history:
conversation.extend([
{"role": "user", "content": user},
{"role": "assistant", "content": assistant}
])
conversation.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="llama3-70b-8192",
messages=conversation,
temperature=0.7,
max_tokens=512,
top_p=1
)
return response.choices[0].message.content
# Gradio Interface
with gr.Blocks() as app:
gr.Markdown("# Advanced LLM Application")
with gr.Tab("Text Classification"):
with gr.Row():
with gr.Column():
text_input = gr.Textbox(label="Input Text")
labels_input = gr.Textbox(label="Categories (comma-separated)",
value="positive, negative, neutral")
classify_btn = gr.Button("Classify")
results_output = gr.Label(label="Classification Results")
classify_btn.click(
fn=classify_text,
inputs=[text_input, labels_input],
outputs=results_output
)
with gr.Tab("Chatbot"):
chatbot = gr.Chatbot(height=400)
msg = gr.Textbox(label="Your Message")
clear = gr.Button("Clear")
def respond(message, chat_history):
bot_message = groq_chat(message, chat_history)
chat_history.append((message, bot_message))
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
app.launch() |