add app.py

app.py

@@ -0,0 +1,81 @@
+import gradio as gr
+import requests
+
+import requests
+import json
+import os
+
+APIKEY = os.environ.get("APIKEY")
+APISECRET = os.environ.get("APISECRET")
+
+def predict(text, seed, out_seq_length, min_gen_length, sampling_strategy, 
+    num_beams, length_penalty, no_repeat_ngram_size, 
+    temperature, topk, topp):
+    global APIKEY
+    global APISECRET
+    url = 'https://wudao.aminer.cn/os/api/api/v2/completions_130B'
+
+    payload = json.dumps({
+        "apikey": APIKEY,
+        "apisecret": APISECRET,
+        "language": "zh-CN",
+        "prompt": text,
+        "length_penalty": length_penalty,
+        "temperature": temperature,
+        "top_k": topk,
+        "top_p": topp,
+        "min_gen_length": min_gen_length,
+        "sampling_strategy": sampling_strategy,
+        "num_beams": num_beams,
+        "max_tokens": out_seq_length
+    })
+
+    headers = {
+    'Content-Type': 'application/json'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+
+    print(response.text)
+    return ret.text
+
+
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # GLM-130B
+            An Open Bilingual Pre-Trained Model
+            """)
+
+        with gr.Row():
+            with gr.Column():
+                model_input = gr.Textbox(lines=7, placeholder='Input something in English or Chinese', label='Input')
+                with gr.Row():
+                    gen = gr.Button("Generate")
+                    clr = gr.Button("Clear")
+            outputs = gr.Textbox(lines=7, label='Output')
+            
+
+        seed = gr.Slider(maximum=100000, value=1234, label='Seed')
+        out_seq_length = gr.Slider(maximum=256, value=128, minimum=8, label='Output Sequence Length')
+        min_gen_length = gr.Slider(maximum=64, value=0, label='Min Generate Length')
+        sampling_strategy = gr.Radio(choices=['BeamSearchStrategy', 'BaseStrategy'], value='BeamSearchStrategy', label='Search Strategy')
+
+        with gr.Tabs():
+            with gr.TabItem("Beam Search Parameter"):
+                # beam search
+                num_beams = gr.Slider(maximum=4, value=1, minimum=1, step=1, label='Number of Beams')
+                length_penalty = gr.Slider(maximum=1, value=0.8, minimum=0, label='Length Penalty')
+                no_repeat_ngram_size = gr.Slider(maximum=5, value=3, minimum=1, step=1, label='No Repeat Ngram Size')
+            with gr.TabItem("Base Search Parameter"):
+                # base search
+                temperature = gr.Slider(maximum=1, value=1, minimum=0, label='Temperature')
+                topk = gr.Slider(maximum=8, value=1, minimum=1, step=1, label='Top K')
+                topp = gr.Slider(maximum=8, value=0, minimum=0, step=1, label='Top P')
+            
+        inputs = [model_input, seed, out_seq_length, min_gen_length, sampling_strategy, num_beams, length_penalty, no_repeat_ngram_size, temperature, topk, topp]
+        gen.click(fn=predict, inputs=inputs, outputs=outputs)
+        clr.click(fn=lambda value: gr.update(value=""), inputs=clr, outputs=model_input)
+
+    demo.launch()