Update app.py

app.py

@@ -1,37 +1,85 @@
 import gradio as gr
+from gpt4all import GPT4All
+from huggingface_hub import hf_hub_download
+import faiss
+#from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+import numpy as np
+from pypdf import PdfReader
 from gradio_pdf import PDF
 from pdf2image import convert_from_path
 from transformers import pipeline
 from pathlib import Path
-from pypdf import PdfReader
-import numpy as np
+from langchain_chroma import Chroma
+
+title = "Mistral-7B-Instruct-GGUF Run On CPU-Basic Free Hardware"
+
+description = """
+🔎 [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) [GGUF format model](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) , 4-bit quantization balanced quality gguf version, running on CPU. English Only (Also support other languages but the quality's not good). Using [GitHub - llama.cpp](https://github.com/ggerganov/llama.cpp) [GitHub - gpt4all](https://github.com/nomic-ai/gpt4all). 
+🔨 Running on CPU-Basic free hardware. Suggest duplicating this space to run without a queue. 
+Mistral does not support system prompt symbol (such as ```<<SYS>>```) now, input your system prompt in the first message if you need. Learn more: [Guardrailing Mistral 7B](https://docs.mistral.ai/usage/guardrailing). 
+"""
+
+"""
+[Model From TheBloke/Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
+[Mistral-instruct-v0.1 System prompt](https://docs.mistral.ai/usage/guardrailing)
+"""
 
-dir_ = Path(__file__).parent
+model_path = "models"
+model_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
 
-p = pipeline(
-    "document-question-answering",
-    model="impira/layoutlm-document-qa",
+hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
+
+print("Start the model init process")
+model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
+
+
+model.config["promptTemplate"] = "[INST] {0} [/INST]"
+model.config["systemPrompt"] = "Tu es un assitant et tu dois répondre en français"
+model._is_chat_session_activated = False
+
+max_new_tokens = 2048
+
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': False}
+embeddings = HuggingFaceEmbeddings(
+    
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
 )
 
-def qa(question: str, doc: str) -> str:
-    reader  = PdfReader(doc)
+chunk_size = 2048
+
+# creating a pdf reader object
+
+
+
+print("Finish the model init process")
+
+def extract_text(file):
+
+    
+    reader = PdfReader(file)
 
     text = []
     for p in np.arange(0, len(reader.pages), 1):
-      page = reader.pages[int(p)]
-    
-      # extracting text from page
-      text.append(page.extract_text())
+        page = reader.pages[int(p)]
+
+        # extracting text from page
+        text.append(page.extract_text())
+
     text = ' '.join(text)
-    #output = p(img, question)
+
     return text
+    
 
 
-demo = gr.Interface(
-    qa,
-    [gr.Textbox(label="Question"), PDF(label="Document")],
-    gr.Textbox(),
+with gr.Blocks() as demo:
+    file_input = gr.File(label="Upload a PDF file")
+    text_output = gr.Textbox(label="Extracted Text")
+    
+    file_input.upload(extract_text, inputs=file_input, outputs=text_output)
+    
 )
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=3).launch()