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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() |