Upload app.py

app.py

@@ -0,0 +1,71 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import gradio as gr
+import re
+import fitz
+
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large").to(device)
+
+class GUI:
+
+    def preprocess(self,text):
+        text = text.replace('\n', ' ')
+        text = re.sub('\s+', ' ', text)
+        return text
+
+    def query_from_list(self,query, options, tok_len):
+
+        
+        t5query = f"""Question: "{query}" Context: {options}"""
+        inputs = tokenizer(t5query, return_tensors="pt").to(device)
+        outputs = model.generate(**inputs, max_new_tokens=tok_len)
+        return tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+
+
+
+    def begin(self,pdf,question,start_page=1, end_page=None):
+
+        doc = fitz.open(pdf)
+        total_pages = doc.page_count
+
+        if end_page is None:
+            end_page = total_pages
+
+        pdf_text = ""
+
+        for i in range(start_page-1, end_page):
+            text = doc.load_page(i).get_text("text")
+            text =  app.preprocess(text)
+            pdf_text+=text
+            # Call the LLM with input data and instruction
+        input_data=pdf_text
+        
+        results = app.query_from_list(question,  input_data, 30)
+
+        return results
+
+app = GUI()
+title = "Get answers from your document with questions with Flan-T5"
+description = "Results will show up in a few seconds."
+
+article="<b>References</b><br>[1] FLAN-T5” 2022. <a href='https://huggingface.co/docs/transformers/model_doc/flan-t5'>Transformers Link</a><br>" 
+
+
+css = """.output_image, .input_image {height: 600px !important}"""
+
+iface = gr.Interface(fn=app.begin, 
+                     inputs=[gr.File(label="PDF File",file_types=['.pdf']), gr.Textbox(label="Question") ],
+                     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)