Update app.py

app.py

@@ -1,5 +1,3 @@
-# Write the Streamlit app script
-# Write the Streamlit app script
 import streamlit as st
 import pdfplumber
 import torch
@@ -13,13 +11,8 @@ import os
 
 print(os.listdir('.'))
 
-
-# Download the 'punkt' package
 nltk.download('punkt')
 
-#openai.api_key = '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'
-
-# Define your model architecture
 class Bert_model(nn.Module):
     def __init__(self, hidden_size, dropout_rate):
         super(Bert_model, self).__init__()
@@ -38,22 +31,19 @@ class Bert_model(nn.Module):
         logits = self.cls_final(pooled_output)
         return logits
 
-# Load the model
-model_path = "model.pt"  # Replace with your actual model path
+model_path = "model.pt" 
 state_dict = torch.load(model_path)
-device = torch.device("cuda:0")  # or "cpu" if GPU is not available
+device = torch.device("cuda:0") 
 
-# Instantiate the model architecture
-model = Bert_model(hidden_size=768, dropout_rate=0.1)  # Adjust the hidden size to match the saved model
+model = Bert_model(hidden_size=768, dropout_rate=0.1) 
 model = nn.DataParallel(model)
 model.load_state_dict(state_dict)
 model = model.to(device)
 model.eval()
 
-# Load the tokenizer
 tokenizer = RobertaTokenizer.from_pretrained('deepset/roberta-base-squad2')
 
-# Function to preprocess PDF text
+
 def preprocess_pdf(pdf_path, tokenizer):
     with pdfplumber.open(pdf_path) as pdf:
         text = " ".join([page.extract_text() for page in pdf.pages[2:]])
@@ -80,7 +70,7 @@ def translate_text(text, target_language):
 
 def explain_term(term):
     response = openai.ChatCompletion.create(
-        model="gpt-4",
+        model="gpt-4.5-turbo",
         messages=[
             {
                 "role": "system",
@@ -95,7 +85,8 @@ def explain_term(term):
     return response['choices'][0]['message']['content']
 
 # Streamlit code to upload file
-st.title('FinQA (Financial Question-Answering)')
+
+st.title('FinChat')
 
 api_key = st.text_input("Enter your OpenAI API key:", type="password")
 
@@ -103,16 +94,14 @@ if api_key:
     try:
         openai.api_key = api_key
 
-        # Test the API key by making a small request
         openai.ChatCompletion.create(
-            model="gpt-4",
+            model="gpt-4.5-turbo",
             messages=[
                 {"role": "system", "content": "You are a helpful assistant."},
                 {"role": "user", "content": "Hello"},
             ],
         )
 
-        # If the above code doesn't raise an exception, the API key is valid
         st.success("API key is valid!")
         
     except Exception as e:
@@ -122,7 +111,6 @@ else:
 
 uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
 
-# Select language
 language = st.selectbox('Select your language', ['English', 'French','Chinese','Korean','Spanish','German','Japanese'])
 
 if uploaded_file is not None:
@@ -131,7 +119,6 @@ if uploaded_file is not None:
     input_ids, attention_mask, text = preprocess_pdf("temp.pdf", tokenizer)
     st.write('File successfully uploaded and processed')
 
-    # Ask a question
     question = st.text_input("Enter your question:")
 
     if question:
@@ -153,18 +140,10 @@ if uploaded_file is not None:
         predictions.sort(key=lambda pair: pair[1], reverse=True)
         top_5_sentences = predictions[:13]
 
-        #st.write("Top 5 Relevant Sentences:")
-        #for sentence, prediction, probabilities in top_5_sentences:
-            #st.write(f"Sentence: {sentence}, Prediction: {prediction}, Probability: {probabilities[prediction]}")
-
-        # Prepare the chat history with the top 3 sentences
         chat_history = "\n".join([sentence[0] for sentence in top_5_sentences])
 
-        # Ask the question using OpenAI API
-        #openai.api_key = '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'  # Replace with your actual OpenAI API key
-
         response = openai.ChatCompletion.create(
-            model="gpt-4",
+            model="gpt-4.5-turbo",
             messages=[
                 {"role": "system", "content": "You are a helpful generator which read the short paragraphs and answer the question."},
                 {"role": "user", "content": chat_history},
@@ -182,7 +161,6 @@ if uploaded_file is not None:
 term = st.text_input("Enter a term you want to define:")
 
 if term:
-    # Define the term using OpenAI API
     definition = explain_term(term)
 
     if language != 'English':