Update app.py

app.py

@@ -1,44 +1,17 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 
-# Load model and tokenizer
-model_name = "persadian/CropSeek-LLM"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
+def embed_cropseek():
+    return """
+    <iframe src="https://cropseek-llm.netlify.app/" width="100%" height="800px" style="border:none;"></iframe>
+    """
 
-# Native inference function
-def cropseek_response(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=300,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.95,
-        repetition_penalty=1.2,
-    )
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+iface = gr.Interface(
+    fn=embed_cropseek,
+    inputs=[],
+    outputs=gr.HTML(),
+    title="CropSeek-LLM (DARJYO)",
+    live=True,
+)
 
-# External embed
-def embed_external():
-    return """<iframe src="https://cropseek-llm.netlify.app/" width="100%" height="800px" style="border:none;"></iframe>"""
+iface.launch()
 
-# Gradio Tabs layout
-with gr.Blocks(title="CropSeek-LLM") as demo:
-    gr.Markdown("# 🌿 CropSeek-LLM\nYour AI partner for crop planning, pest control, and soil optimization.")
-
-    with gr.Tab("🧠 Ask the Model"):
-        gr.Markdown("### Ask CropSeek-LLM a question:")
-        with gr.Row():
-            prompt = gr.Textbox(label="Your Question", lines=4, placeholder="e.g. Best time to plant tomatoes in Limpopo?")
-        output = gr.Textbox(label="Model Response")
-        ask_button = gr.Button("Submit")
-        ask_button.click(fn=cropseek_response, inputs=prompt, outputs=output)
-
-    with gr.Tab("🌐 External Dashboard"):
-        gr.HTML(embed_external())
-
-# Launch app
-if __name__ == "__main__":
-    demo.launch()