changes add trusted mode

app.py

@@ -8,13 +8,50 @@ examples = [
     ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
 ]
 
-demo = gr.load(
-    "huggingface/pradhaph/medical-falcon-7b",
-    inputs=gr.Textbox(lines=5, max_lines=6, label="Input Text"),
-    title=title,
-    examples=examples,
+import gradio as gr
+from auto_gptq import AutoGPTQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+
+# Load the trained model
+model_path = "huggingface/pradhaph/medical-falcon-7b"
+model = AutoGPTQForCausalLM.from_quantized(
+    model_path,
+    revision="main",
+    # revision="gptq-8bit-128g-actorder_True",
+    model_basename="model",
+    use_safetensors=True,
     trust_remote_code=True,
+    inject_fused_attention=False,
+    device_map="cuda",
+    quantize_config=None,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+
+# Define the input and output interfaces
+def answer_question(context):
+    # Generate an answer based on the context
+    inputs = tokenizer(context, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs, max_length=200, num_return_sequences=1)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Run the interface
+iface = gr.Interface(
+    fn=answer_question,
+    inputs="text",
+    outputs="text",
+    title="Question Answering with GPT",
+    description="Enter a context to get an answer."
 )
 
+# demo = gr.load(
+#     "huggingface/pradhaph/medical-falcon-7b",
+#     inputs=gr.Textbox(lines=5, max_lines=6, label="Input Text"),
+#     title=title,
+#     examples=examples,
+#     trust_remote_code=True,
+# )
+
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()
+    # demo.launch()