Update app.py

app.py

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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()