initail commit

app.py

@@ -0,0 +1,204 @@
+import requests
+import json
+import gradio as gr
+# from concurrent.futures import ThreadPoolExecutor
+import pdfplumber
+import pandas as pd
+import langchain
+import time
+from cnocr import CnOcr
+
+# from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import UnstructuredWordDocumentLoader
+from sentence_transformers import SentenceTransformer, models, util
+word_embedding_model = models.Transformer('sentence-transformers/all-MiniLM-L6-v2', do_lower_case=True)
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='cls')
+embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+ocr = CnOcr()
+# chat_url = 'https://souljoy-my-api.hf.space/sale'
+chat_url = 'https://souljoy-my-api.hf.space/chatpdf'
+headers = {
+    'Content-Type': 'application/json',
+}
+# thread_pool_executor = ThreadPoolExecutor(max_workers=4)
+history_max_len = 500
+all_max_len = 3000
+
+
+def get_emb(text):
+    emb_url = 'https://souljoy-my-api.hf.space/embeddings'
+    data = {"content": text}
+    try:
+        result = requests.post(url=emb_url,
+                               data=json.dumps(data),
+                               headers=headers
+                               )
+        return result.json()['data'][0]['embedding']
+    except Exception as e:
+        print('data', data, 'result json', result.json())
+
+
+def doc_emb(doc: str):
+    texts = doc.split('\n')
+    # futures = []
+    emb_list = embedder.encode(texts)
+    # for text in texts:
+    #     futures.append(thread_pool_executor.submit(get_emb, text))
+    # for f in futures:
+    #     emb_list.append(f.result())
+    print('\n'.join(texts))
+    gr.Textbox.update(value="")
+    return texts, emb_list, gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Markdown.update(
+        value="""success ! Let's talk"""), gr.Chatbot.update(visible=True)
+
+
+def get_response(msg, bot, doc_text_list, doc_embeddings):
+    # future = thread_pool_executor.submit(get_emb, msg)
+    gr.Textbox.update(value="")
+    now_len = len(msg)
+    req_json = {'question': msg}
+    his_bg = -1
+    for i in range(len(bot) - 1, -1, -1):
+        if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:
+            break
+        now_len += len(bot[i][0]) + len(bot[i][1])
+        his_bg = i
+    req_json['history'] = [] if his_bg == -1 else bot[his_bg:]
+    # query_embedding = future.result()
+    query_embedding = embedder.encode([msg])
+    cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
+    score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]
+    score_index.sort(key=lambda x: x[0], reverse=True)
+    print('score_index:\n', score_index)
+    index_set, sub_doc_list = set(), []
+    for s_i in score_index:
+        doc = doc_text_list[s_i[1]]
+        if now_len + len(doc) > all_max_len:
+            break
+        index_set.add(s_i[1])
+        now_len += len(doc)
+       # Maybe the paragraph is truncated wrong, so add the upper and lower paragraphs
+        if s_i[1] > 0 and s_i[1] -1 not in index_set:
+            doc = doc_text_list[s_i[1]-1]
+            if now_len + len(doc) > all_max_len:
+                break
+            index_set.add(s_i[1]-1)
+            now_len += len(doc)
+        if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:
+            doc = doc_text_list[s_i[1]+1]
+            if now_len + len(doc) > all_max_len:
+                break
+            index_set.add(s_i[1]+1)
+            now_len += len(doc)
+
+    index_list = list(index_set)
+    index_list.sort()
+    for i in index_list:
+        sub_doc_list.append(doc_text_list[i])
+    req_json['doc'] = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list)
+    data = {"content": json.dumps(req_json)}
+    print('data:\n', req_json)
+    result = requests.post(url=chat_url,
+                           data=json.dumps(data),
+                           headers=headers
+                           )
+    res = result.json()['content']
+    bot.append([msg, res])
+    return bot[max(0, len(bot) - 3):]
+
+
+def up_file(fls):
+    doc_text_list = []
+    names = []
+    
+    for i in fls:
+        names.append(str(i.name))
+    
+    files = []
+    docs = []
+    for i in names:
+        
+        if(i[-3:] == "pdf"):
+            files.append(i)
+        else:
+            docs.append(i)
+
+    for i in docs:
+        loader = UnstructuredWordDocumentLoader(i, mode="elements")
+        data = loader.load()
+        extracted = data[1]
+        doc_text_list.append(extracted)
+
+
+    
+    for idx, file in enumerate(files):
+        print("11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111")
+        #print(file.name)
+        with pdfplumber.open(file) as pdf:
+            for i in range(len(pdf.pages)):
+                # Read page i+1 of a PDF document
+                page = pdf.pages[i]
+                res_list = page.extract_text().split('\n')[:-1]
+
+                for j in range(len(page.images)):
+                   # Get the binary stream of the image
+                    img = page.images[j]
+                    file_name = '{}-{}-{}.png'.format(str(time.time()), str(i), str(j))
+                    with open(file_name, mode='wb') as f:
+                        f.write(img['stream'].get_data())
+                    try:
+                        res = ocr.ocr(file_name)
+                        # res = PyPDFLoader(file_name)
+                    except Exception as e:
+                        res = []
+                    if len(res) > 0:
+                        res_list.append(' '.join([re['text'] for re in res]))
+
+                tables = page.extract_tables()
+                for table in tables:
+                    # The first column is used as the header
+                    df = pd.DataFrame(table[1:], columns=table[0])
+                    try:
+                        records = json.loads(df.to_json(orient="records", force_ascii=False))
+                        for rec in records:
+                            res_list.append(json.dumps(rec, ensure_ascii=False))
+                    except Exception as e:
+                        res_list.append(str(df))
+
+                doc_text_list += res_list
+    doc_text_list = [str(text).strip() for text in doc_text_list if len(str(text).strip()) > 0]
+    # print(doc_text_list)
+    return gr.Textbox.update(value='\n'.join(doc_text_list), visible=True), gr.Button.update(
+        visible=True), gr.Markdown.update(
+        value="Processing")
+
+
+
+
+
+with gr.Blocks(css=".gradio-container {background-color: #f7f377}, footer {visibility: hidden}") as demo:
+    with gr.Row():
+        with gr.Column():
+            file = gr.File(file_types=['.docx','.pdf'], label='Click to upload Document', file_count='multiple')
+            doc_bu = gr.Button(value='Submit', visible=False)
+
+            
+            txt = gr.Textbox(label='result', visible=False)
+            
+            
+            doc_text_state = gr.State([])
+            doc_emb_state = gr.State([])
+        with gr.Column():
+            md = gr.Markdown("Please Upload the PDF")
+            chat_bot = gr.Chatbot(visible=False)
+            msg_txt = gr.Textbox(visible = False)
+            chat_bu = gr.Button(value='Clear', visible=False)
+
+    file.change(up_file, [file], [txt, doc_bu, md]) #hiding the text
+    doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])
+    msg_txt.submit(get_response, [msg_txt, chat_bot,doc_text_state, doc_emb_state], [chat_bot],queue=False)
+    chat_bu.click(lambda: None, None, chat_bot, queue=False)
+
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False)
+    # demo.queue().launch(share=False, server_name='172.22.2.54', server_port=9191)

requirements.txt

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+pdfplumber
+sentence_transformers
+cnocr
+langchain
+unstructured