Update app.py
Browse files
app.py
CHANGED
@@ -2,24 +2,37 @@ import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from PyPDF2 import PdfReader
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import pandas as pd
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# Title and emojis
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st.title("π WizNerd Insp π")
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# Sidebar for file uploads
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st.sidebar.header("Upload Files")
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uploaded_pdf = st.sidebar.file_uploader("Upload PDF File", type=["pdf"])
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# Load the HuggingFace model and tokenizer
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@st.cache_resource
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def load_model():
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model_name = "amiguel/optimizedModelLinsting6.1"
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return tokenizer, model
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# Prompt style
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prompt_style = """
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@@ -37,14 +50,10 @@ Please answer the following inspection scope question.
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# Function to process user input and generate response
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def generate_response(input_text):
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# Format the input using the prompt style
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formatted_input = prompt_style.format(input_text, "", "")
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# Tokenize and generate response
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inputs = tokenizer(formatted_input, return_tensors="pt", truncation=True, max_length=512)
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outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Main chat interface
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st.write(response)
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# Process uploaded files
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if
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st.write("Processing
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st.write(text)
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from PyPDF2 import PdfReader
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import pandas as pd
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Title and emojis
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st.title("π WizNerd Insp π")
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# Sidebar for file uploads
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st.sidebar.header("Upload Files")
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uploaded_file = st.sidebar.file_uploader("Upload XLSX or PDF File", type=["xlsx", "pdf"])
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# Load the HuggingFace model and tokenizer
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@st.cache_resource
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def load_model():
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model_name = "amiguel/optimizedModelLinsting6.1"
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hf_token = os.getenv("HUGGINGFACE_TOKEN") # Load token from .env
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if hf_token:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
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model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=hf_token)
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else:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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return tokenizer, model
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try:
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tokenizer, model = load_model()
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except Exception as e:
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st.error(f"Error loading model: {e}")
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st.info("Ensure the model name is correct or provide a valid Hugging Face token.")
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# Prompt style
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prompt_style = """
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# Function to process user input and generate response
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def generate_response(input_text):
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formatted_input = prompt_style.format(input_text, "", "")
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inputs = tokenizer(formatted_input, return_tensors="pt", truncation=True, max_length=512)
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outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Main chat interface
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st.write(response)
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# Process uploaded files
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if uploaded_file:
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st.write(f"Processing {uploaded_file.type} file...")
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if uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
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df = pd.read_excel(uploaded_file)
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st.write(df)
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elif uploaded_file.type == "application/pdf":
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pdf_reader = PdfReader(uploaded_file)
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text()
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st.write(text)
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