Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# -------------------------------
+# Load TinyLlama Model (LLaMA-based)
+# -------------------------------
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float32,
+    device_map="auto"
+)
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    temperature=0.7,
+    do_sample=True
+)
+
+# -------------------------------
+# College Assistant Function
+# -------------------------------
+def college_ai(question):
+    if not question:
+        return "Please ask a question."
+
+    prompt = f"""
+You are a helpful college assistant.
+Answer clearly for students.
+
+Student Question: {question}
+
+Answer:
+"""
+
+    result = pipe(prompt)[0]["generated_text"]
+
+    # Clean output
+    answer = result.split("Answer:")[-1].strip()
+    return answer
+
+
+# -------------------------------
+# Gradio UI
+# -------------------------------
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎓 College AI Assistant (LLaMA Powered)")
+    gr.Markdown("Ask any academic question!")
+
+    user_input = gr.Textbox(
+        label="Enter Your Question",
+        placeholder="Example: Explain Machine Learning"
+    )
+
+    output = gr.Textbox(label="AI Answer")
+
+    ask_btn = gr.Button("Ask AI")
+
+    ask_btn.click(college_ai, inputs=user_input, outputs=output)
+
+demo.launch()