Update app.py
Browse files
app.py
CHANGED
@@ -12,17 +12,15 @@ import os
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model_name = 'google/flan-t5-base'
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model = T5ForConditionalGeneration.from_pretrained(model_name
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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print('flan read')
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ST_name = 'sentence-transformers/sentence-t5-base'
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st_model = SentenceTransformer(ST_name)
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print('sentence read')
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client = chromadb.Client()
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collection = client.create_collection("
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def get_context(query_text):
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@@ -35,7 +33,7 @@ def get_context(query_text):
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def local_query(query, context):
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t5query = """Using the available context, please answer the question.
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If you
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Context: {}
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Question: {}
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""".format(context, query)
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@@ -52,20 +50,9 @@ def local_query(query, context):
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def run_query(btn, history, query):
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context = get_context(query)
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result = local_query(query, context)
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print('printing result after call back')
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print(result)
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history.append((query, str(result[0])))
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print('printing history')
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print(history)
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return history, ""
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@@ -127,11 +114,9 @@ with gr.Blocks() as demo:
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# Event handler for uploading a PDF
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btn.upload(fn=upload_pdf, inputs=[btn], outputs=[output])
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txt.submit(run_query, [btn, chatbot, txt], [chatbot, txt])
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#.then(
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# generate_response, inputs =[chatbot,],outputs = chatbot,)
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gr.close_all()
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demo.queue().launch()
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model_name = 'google/flan-t5-base'
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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ST_name = 'sentence-transformers/sentence-t5-base'
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st_model = SentenceTransformer(ST_name)
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client = chromadb.Client()
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collection = client.create_collection("my_database")
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def get_context(query_text):
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def local_query(query, context):
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t5query = """Using the available context, please answer the question.
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If you are not sure please say I don't know.
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Context: {}
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Question: {}
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""".format(context, query)
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def run_query(btn, history, query):
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context = get_context(query)
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result = local_query(query, context)
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history.append((query, str(result[0])))
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return history, ""
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# Event handler for uploading a PDF
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btn.upload(fn=upload_pdf, inputs=[btn], outputs=[output])
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txt.submit(run_query, [btn, chatbot, txt], [chatbot, txt])
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gr.close_all()
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demo.queue().launch()
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