Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import BartForConditionalGeneration, BartTokenizer
+
+# Load the model and tokenizer
+model_repo_path = 'ABC/hotpotQA_BART_Finetuned_E5'
+model = BartForConditionalGeneration.from_pretrained(model_repo_path)
+tokenizer = BartTokenizer.from_pretrained(model_repo_path)
+
+# Ensure the model is in evaluation mode
+model.eval()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+# Streamlit app layout
+st.title("Question Answering App")
+
+# User input for context and question
+context_input = st.text_area("Enter context", height=200)
+question_input = st.text_area("Enter question")
+
+# Generate the answer
+if st.button("Get Answer"):
+    if context_input and question_input:
+        with st.spinner("Generating answer..."):
+            try:
+                # Prepare the input for the model
+                input_text = f"context: {context_input} question: {question_input}"
+                inputs = tokenizer(input_text, return_tensors='pt')
+                inputs = {key: value.to(device) for key, value in inputs.items()}
+
+                # Perform inference
+                with torch.no_grad():
+                    outputs = model.generate(inputs['input_ids'], max_length=50)
+
+                # Decode the output
+                answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+                st.subheader("Answer")
+                st.write(answer)
+            except Exception as e:
+                st.error(f"Error during question answering: {e}")
+    else:
+        st.warning("Please enter both context and question.")
+
+
+
+# import streamlit as st
+# import requests
+# import torch
+# from transformers import pipeline
+# from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# # Replace with your Hugging Face model repository path
+# model_repo_path = 'AbdurRehman313/hotpotQA_BART_Finetuned_E5'
+
+# # Load the model and tokenizer
+# model = T5ForConditionalGeneration.from_pretrained(model_repo_path)
+# tokenizer = T5Tokenizer.from_pretrained(model_repo_path)
+
+# #Take model in evaluation mode
+# model.eval()
+
+# # Example input question and context (replace with your actual inputs)
+# # question = "What is the capital of France?"
+# # context = "France is a country in Europe. Its capital is Paris, which is known for its art, culture, and history."
+
+# # print(f'Predicted answer: {answer}')
+# ###
+
+# # Prepare the input for the model
+# # input_text = f"context: {context} question: {question} "
+# # inputs = tokenizer(input_text, return_tensors='pt')
+
+# # # Move inputs to the appropriate device
+# # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+# # model.to(device)
+# # inputs = {key: value.to(device) for key, value in inputs.items()}
+
+# # # Perform inference
+# # with torch.no_grad():
+# #     outputs = model.generate(inputs['input_ids'], max_length=50)
+
+# # # Decode the output
+# # answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# # Streamlit app layout
+# st.title("Text Summarization App")
+
+# # User input
+# text_input = st.text_area("Enter text to summarize", height=300)
+
+# # Summarize the text
+# if st.button("Summarize"):
+#     if text_input:
+#         with st.spinner("Generating summary..."):
+#             try:
+#                 summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
+#                 st.subheader("Summary")
+#                 st.write(summary[0]['summary_text'])
+#             except Exception as e:
+#                 st.error(f"Error during summarization: {e}")
+#     else:
+#         st.warning("Please enter some text to summarize.")