Update app.py

app.py

@@ -1,70 +1,122 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_tavily import TavilySearch
 
+# =====================================================
+# SYSTEM PROMPT
+# =====================================================
+ai_subhash = """
+You are Subhash AI, a smart and friendly AI Mentor for engineering students.
+Explain concepts clearly and step by step in simple words.
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+Always:
+- Use simple language
+- Give step-by-step explanations
+- Use examples or analogies
+- Be friendly, patient, and motivating
 
-    messages = [{"role": "system", "content": system_message}]
+After every answer, ask one small follow-up question.
+"""
 
-    messages.extend(history)
+# =====================================================
+# LOAD SECRETS (HUGGING FACE WAY)
+# =====================================================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 
-    messages.append({"role": "user", "content": message})
+# =====================================================
+# MODEL + SEARCH
+# =====================================================
+llm = ChatGroq(
+    model_name="openai/gpt-oss-120b",
+    temperature=0,
+    groq_api_key=GROQ_API_KEY
+)
 
-    response = ""
+prompt = ChatPromptTemplate.from_messages([
+    ("system", ai_subhash),
+    ("human",
+     "Chat history:\n{chat_history}\n\n"
+     "Context:\n{context}\n\n"
+     "User: {user_input}\n\n"
+     "AI:")
+])
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+chain = prompt | llm
 
-        response += token
-        yield response
+search = TavilySearch(
+    max_result=5,
+    tavily_api_key=TAVILY_API_KEY
+)
 
+# =====================================================
+# CHAT LOGIC
+# =====================================================
+def predict(message, history):
+    if not message.strip():
+        return ""
 
+    chat_history = ""
+    for h in history:
+        chat_history += f"User: {h[0]}\nAI: {h[1]}\n"
+
+    # Web search
+    results = search.invoke(message)
+    context = "\n".join(
+        r.get("content", "") for r in results.get("result", [])
+    )
+
+    response = chain.invoke({
+        "user_input": message,
+        "context": context,
+        "chat_history": chat_history
+    })
+
+    return response.content
+
+# =====================================================
+# UI STYLING
+# =====================================================
+custom_css = """
+body {
+    background: radial-gradient(circle at top, #020617, #000000);
+}
+.gr-chatbot {
+    background: rgba(2, 6, 23, 0.75);
+    border-radius: 18px;
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# =====================================================
+# GRADIO UI
+# =====================================================
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("# 🤖 Subhash Chatbot")
+    gr.Markdown(
+        "<div style='text-align:center;color:#a5b4fc'>AI mentor with memory + web search</div>"
+    )
+
+    chatbot = gr.Chatbot(height=420)
+    msg = gr.Textbox(
+        placeholder="Ask your question...",
+        show_label=False
+    )
+
+    clear = gr.Button("🧹 Clear Chat")
+
+    def respond(message, chat_history):
+        reply = predict(message, chat_history)
+        chat_history.append((message, reply))
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: [], None, chatbot)
 
+    gr.Markdown(
+        "<footer style='text-align:center;color:#94a3b8'>Built by Subhash • Powered by Groq + Tavily</footer>"
+    )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()