Create app.py

app.py

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+import gradio as gr
+from fastapi import FastAPI, Query
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import uvicorn
+import torch
+
+# Inisialisasi FastAPI
+app = FastAPI()
+
+# Inisialisasi model dan tokenizer
+def load_model(model_name):
+    if model_name == "mixtral":
+        model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)
+        return pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
+    elif model_name == "gpt2":
+        return pipeline("text-generation", model="gpt2")
+    else:
+        raise ValueError("Model tidak didukung. Pilih 'mixtral' atau 'gpt2'.")
+
+# Fungsi untuk menghasilkan teks
+def generate_text(prompt, model_name, max_length=100):
+    try:
+        generator = load_model(model_name)
+        # Menghasilkan teks
+        output = generator(prompt, max_length=max_length, num_return_sequences=1, do_sample=True)
+        return output[0]["generated_text"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Endpoint API
+@app.get("/generate")
+async def generate(prompt: str = Query(..., description="Teks input untuk model"),
+                  model: str = Query("gpt2", description="Model AI: 'mixtral' atau 'gpt2'")):
+    result = generate_text(prompt, model)
+    return {"prompt": prompt, "model": model, "generated_text": result}
+
+# Antarmuka Gradio
+def gradio_generate(prompt, model_choice):
+    return generate_text(prompt, model_choice)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Text Generation API")
+    gr.Markdown("Masukkan teks dan pilih model untuk menghasilkan teks. Gunakan API di `/generate` untuk akses programatik.")
+    
+    # Komponen input
+    prompt_input = gr.Textbox(label="Prompt", placeholder="Masukkan teks di sini...")
+    model_choice = gr.Dropdown(choices=["gpt2", "mixtral"], label="Pilih Model", value="gpt2")
+    submit_button = gr.Button("Generate")
+    
+    # Komponen output
+    output_text = gr.Textbox(label="Hasil Generasi")
+    
+    # Menghubungkan tombol dengan fungsi
+    submit_button.click(
+        fn=gradio_generate,
+        inputs=[prompt_input, model_choice],
+        outputs=output_text
+    )
+
+# Menjalankan aplikasi (untuk lokal, bukan di Hugging Face)
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+else:
+    # Untuk Hugging Face Spaces, luncurkan Gradio
+    demo.launch()