First commit

README.md

@@ -1,12 +1,13 @@
 ---
-title: FLAN T5 GPT
+title:  FLAN-T5 vs GTP
 emoji: 💻
 colorFrom: blue
 colorTo: gray
 sdk: gradio
-sdk_version: 3.42.0
+sdk_version: 3.20.1
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,49 @@
+import os
+os.system('pip install -q git+https://github.com/huggingface/transformers.git')
+os.system('pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu')
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+import torch
+import gradio as gr
+import re
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+class GUI:
+    def query(self,query,modelo="flan-t5-small",tokens=100):
+        options=""
+        tok_len=tokens
+        t5query = f"""Question: "{query}" Context: {options}"""        
+        if (modelo=="flan-t5-small" or modelo=="flan-t5-large"):
+           tokenizer = AutoTokenizer.from_pretrained("google/{}".format(modelo))
+           model = AutoModelForSeq2SeqLM.from_pretrained("google/{}".format(modelo)).to(device)
+           inputs = tokenizer(t5query, return_tensors="pt").to(device)
+           outputs = model.generate(**inputs, max_new_tokens=tok_len)
+        else:
+            model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M").to(device)
+            tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
+            input_ids = tokenizer(t5query, return_tensors="pt").to(device) 
+            outputs = model.generate(**input_ids, do_sample=True, max_length=tok_len) 
+        generation=tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        
+        return '\n'.join(generation)
+    def begin(self,question,modelo,tokens):
+        results = app.query(question,tokens)
+        return results
+
+app = GUI()
+title = "Get answers with questions with Flan-T5"
+description = "Results will show up in a few seconds."
+article="More info  <a href='https://ruslanmv.com/'>ruslanmv.com</a><br>" 
+css = """.output_image, .input_image {height: 600px !important}"""
+
+iface = gr.Interface(fn=app.begin, 
+                     inputs=[ gr.Textbox(label="Question"),
+                     gr.Radio(["flan-t5-small", "flan-t5-large","gpt-neo-125M"],label="Model",value="flan-t5-small"),
+                     gr.Slider(30, 200, value=100, step = 1,label="Max Tokens"),],
+                     outputs = gr.Text(label="Answer Summary"),
+                     title=title,
+                     description=description,
+                     article=article,
+                     css=css,
+                     analytics_enabled = True
+                    ,enable_queue=True)
+iface.launch(inline=False, share=False, debug=False)