t

app.py

@@ -7,29 +7,41 @@ import gradio as gr
 # #          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
 # #          "researchers was the fact that the unicorns spoke perfect English."
 
-ex=None
-try:
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+# ex=None
+# try:
+#     from transformers import AutoModelForCausalLM, AutoTokenizer
+#     tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
     
-    # "EluttherAI" on this line and for the next occurence only
-    # tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
-    # model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
-except Exception as e:
-    ex = e
+#     # "EluttherAI" on this line and for the next occurence only
+#     # tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+#     # model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
+# except Exception as e:
+#     ex = e
 
 def f(text):
-    try:
-        if ex:
-            raise Exception("err from transformers import: \n"+str(ex))
-        model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
-        default_do_sample = False #True (default is True)
-        input_ids = tokenizer(text, return_tensors="pt").input_ids
-        gen_tokens = model.generate(input_ids, do_sample=default_do_sample, temperature=0.9, max_length=30)
-        gen_text = tokenizer.batch_decode(gen_tokens)[0]
-        return gen_text
-    except Exception as e:
-        return "err: \n" + str(e)
+    import requests
+
+    API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
+    headers = {"Authorization": "Bearer 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"}
+
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+
+    output = query(text)
+    return output
+
+    # try:
+    #     if ex:
+    #         raise Exception("err from transformers import: \n"+str(ex))
+    #     model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
+    #     default_do_sample = False #True (default is True)
+    #     input_ids = tokenizer(text, return_tensors="pt").input_ids
+    #     gen_tokens = model.generate(input_ids, do_sample=default_do_sample, temperature=0.9, max_length=30)
+    #     gen_text = tokenizer.batch_decode(gen_tokens)[0]
+    #     return gen_text
+    # except Exception as e:
+    #     return "err: \n" + str(e)
     # from transformers import AutoModelForCausalLM, AutoTokenizer
     # return text
 # def predict(text):