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app.py

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+import gradio as gr
+import os
+import requests
+from dotenv import load_dotenv
+
+# Load GROQ API key from environment
+load_dotenv()
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+MODEL_NAME = "llama3-8b-8192"
+
+SYSTEM_PROMPT = """
+You are an intelligent and helpful machine learning assistant. 
+Your task is to help users choose appropriate machine learning models based on their problem type 
+(classification, regression, clustering, etc.), dataset characteristics, accuracy needs, and resource constraints. 
+Explain recommendations clearly and concisely, and offer guidance on why a model is suitable.
+"""
+
+def query_groq(message, chat_history):
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    for user, bot in chat_history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": bot})
+    messages.append({"role": "user", "content": message})
+
+    response = requests.post(GROQ_API_URL, headers=headers, json={
+        "model": MODEL_NAME,
+        "messages": messages,
+        "temperature": 0.7
+    })
+
+    if response.status_code == 200:
+        reply = response.json()["choices"][0]["message"]["content"]
+        return reply
+    else:
+        return f"Error {response.status_code}: {response.text}"
+
+def generate_question(problem_type, notes):
+    return f"I am working on a {problem_type} problem. {notes} What machine learning model would be suitable?"
+
+def respond(message, chat_history):
+    bot_reply = query_groq(message, chat_history)
+    chat_history.append((message, bot_reply))
+    return "", chat_history
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 ML Model Selector Chatbot (Powered by GROQ LLM)")
+    gr.Markdown("Use the dropdown to specify your ML problem and get recommendations!")
+
+    with gr.Row():
+        problem_type = gr.Dropdown(
+            choices=["Classification", "Regression", "Clustering", "Anomaly Detection", "Dimensionality Reduction"],
+            label="Select your ML Problem Type",
+            value="Classification"
+        )
+        notes = gr.Textbox(label="Add extra details (optional)")
+
+    generate_btn = gr.Button("Generate Question")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Ask your question or use generated one above")
+    clear = gr.Button("Clear Chat")
+    state = gr.State([])
+
+    def fill_generated_question(ptype, detail):
+        return generate_question(ptype, detail)
+
+    generate_btn.click(fill_generated_question, [problem_type, notes], msg)
+    msg.submit(respond, [msg, state], [msg, chatbot])
+    clear.click(lambda: ([], []), None, [chatbot, state])
+
+demo.launch()

requirements.txt

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+gradio
+requests