Upload app.py

app.py

@@ -0,0 +1,97 @@
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from langchain import OpenAI, PromptTemplate, LLMChain
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.mapreduce import MapReduceChain
+from langchain.prompts import PromptTemplate
+from langchain.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
+# from langchain.chains import LLMChain
+from langchain.chains.summarize import load_summarize_chain
+from langchain.docstore.document import Document
+from langchain.llms import HuggingFacePipeline
+from transformers import LlamaTokenizer, LlamaForCausalLM
+import gradio as gr
+
+print("Loading Pipeline Dolly...")
+    # print("Loading Pipeline...", str(File.name))
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left")
+base_model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16)
+instruct_pipeline = pipeline(
+    "text-generation",
+    model=base_model,
+    tokenizer=tokenizer,
+    max_length=2048,
+    temperature=0.6,
+    pad_token_id=tokenizer.eos_token_id,
+    top_p=0.95,
+    repetition_penalty=1.2
+)
+# instruct_pipeline = pipeline(model="databricks/dolly-v2-3b",  torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+# print(instruct_pipeline)
+print("Dolly Pipeline Loaded!")
+llm_dolly = HuggingFacePipeline(pipeline=instruct_pipeline)
+
+
+print("Loading Pipeline Alpaca...")
+tokenizer_alpaca = LlamaTokenizer.from_pretrained('minlik/chinese-alpaca-plus-7b-merged')
+model_alpaca = LlamaForCausalLM.from_pretrained('minlik/chinese-alpaca-plus-7b-merged')
+instruct_pipeline_alpaca = pipeline(
+    "text-generation",
+    model=model_alpaca,
+    tokenizer=tokenizer_alpaca,
+    max_length=1024,
+    temperature=0.6,
+    pad_token_id=tokenizer_alpaca.eos_token_id,
+    top_p=0.95,
+    repetition_penalty=1.2,
+    device_map= "auto"
+)
+print("Pipeline Loaded Alpaca!")
+llm_alpaca = HuggingFacePipeline(pipeline=instruct_pipeline_alpaca)
+
+def summarize(Model, File, Input_text):
+    prompt_template = """Write a concise summary of the following:
+
+
+    {text}
+    
+    Summary in English:
+    """
+
+    PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+
+
+    text_splitter = CharacterTextSplitter()
+    if File:
+        with open(str(File.name)) as f:
+            state_of_the_union = f.read()
+        text = state_of_the_union
+    else:
+        text = Input_text
+    print(text)
+    texts = text_splitter.split_text(text)
+
+    docs = [Document(page_content=t) for t in texts[:3]]
+    print("Printing Docs-------")
+    print(docs)
+    print("-----------------\n\n")
+    if Model=='Dolly':
+        chain = load_summarize_chain(llm_dolly, chain_type="refine", question_prompt=PROMPT)
+    else:
+        chain = load_summarize_chain(llm_alpaca, chain_type="refine", question_prompt=PROMPT)
+    summary_text = chain({"input_documents": docs}, return_only_outputs=True)
+    print(summary_text["output_text"])
+    return summary_text["output_text"]
+
+def greet(name):
+    return "Hello " + name + "!"
+
+# with gr.Blocks() as demo:
+#     a = gr.File()
+#     gr.Interface(fn=summarize, inputs = [gr.inputs.Dropdown(["Dolly", "Alpaca"]), a , "text"], outputs="text", title="Summarization Tool")
+
+demo = gr.Interface(fn=summarize, inputs = [gr.inputs.Dropdown(["Dolly", "Alpaca"]),gr.inputs.File(label="Upload .txt file"), "text"], outputs="text", title="Summarization Tool")
+    
+demo.queue().launch(share = True)