Update app.py

app.py

@@ -1,110 +1,108 @@
-%%writefile app.py
-
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import os
-from huggingface_hub import login
-
-
-model_name_tinyllama = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-tokenizer_tinyllama = AutoTokenizer.from_pretrained(model_name_tinyllama)
-model_tinyllama = AutoModelForCausalLM.from_pretrained(model_name_tinyllama,torch_dtype=torch.float32,device_map={"": "cpu"})
-
-def summarize_tinyllama(article):
-   # For causal models like TinyLlama, summarization isn't a direct task like with encoder-decoder models.
-   # We can prompt it to continue a summary.
-   prompt="Summarize the following article clearly and concisely:"
-   input_text = f"{prompt}\n{article}\nSummary:"
-   inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
-
-   # Generate tokens - the model will try to complete the input prompt.
-   # We need to adjust generation parameters for open-ended generation.
-   # max_new_tokens controls how much new text is generated after the prompt.
-   outputs = model_tinyllama.generate(
-       inputs["input_ids"],
-       attention_mask=inputs["attention_mask"],
-       max_new_tokens=500,  # Generate up to 300 new tokens for the summary
-       do_sample=True,     # Don't sample, use greedy decoding
-       temperature=0.7,
-       min_new_tokens=150,
-       top_p=0.9,
-       pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
-   )
-
-   # Decode the entire output sequence.
-   generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
-
-
-   # The generated text will include the original prompt. We need to extract the summary part.
-   # This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
-   summary_start_index = generated_text.find("Summary:") + len("Summary:")
-   summary = generated_text[summary_start_index:].strip()
-
-   return summary
-
-def answer_question_tinyllama(article, question):
-   # Formulate the prompt to guide the TinyLlama model to answer the question based on the article.
-   # We ask the model to act as an AI answering a question based on the provided text.
-   input_text = f"From this Article: {article}\n\n Answer the below Question: {question}\n\nAnswer:"
-
-   # Tokenize the input text
-   # Truncate if the combined article and question is too long
-   inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
-
-   # Generate the answer using the model.
-   # We use generate with parameters suitable for generating a concise answer.
-   outputs = model_tinyllama.generate(
-       inputs["input_ids"],
-       attention_mask=inputs["attention_mask"],
-       max_new_tokens=500,  # Generate up to 100 new tokens for the answer
-       do_sample=True,      # Use sampling to potentially get more varied answers
-       temperature=0.7,     # Control randomness
-       top_p=0.9,           # Nucleus sampling
-       pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
-   )
-   # Decode the generated sequence
-   generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
-
-   # The generated text will include the original prompt. We need to extract the answer part.
-   # This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
-   answer_start_index = generated_text.find("Answer:") + len("Answer:")
-   answer = generated_text[answer_start_index:].strip()
-
-   # Basic cleanup: remove potential repetition of the question or prompt in the answer
-   if answer.startswith(question):
-       answer = answer[len(question):].strip()
-
-   return answer
-
-
-st.title("Smart Article Insights Generator")
-st.markdown("Summarize an article or ask a question about it.")
-
-mode = st.radio("Select Mode", ["Summarize", "Answer Question"])
-
-article_input = st.text_area("Article Text", height=300, placeholder="Paste the article here...")
-
-question_input = None
-if mode == "Answer Question":
-   question_input = st.text_input("Question", placeholder="Enter your question here...")
-
-if st.button("Process"):
-   if mode == "Summarize":
-       if article_input:
-           with st.spinner("Generating summary..."):
-               output = summarize_tinyllama(article_input)
-               st.subheader("Summary")
-               st.write(output)
-       else:
-           st.warning("Please provide an article to summarize.")
-   elif mode == "Answer Question":
-       if article_input and question_input:
-           with st.spinner("Generating answer..."):
-               output = answer_question_tinyllama(article_input, question_input)
-               st.subheader("Answer")
-               st.write(output)
-       elif not article_input:
-           st.warning("Please provide an article to answer the question from.")
-       elif not question_input:
-           st.warning("Please provide a question to answer.")
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import os
+from huggingface_hub import login
+
+
+model_name_tinyllama = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer_tinyllama = AutoTokenizer.from_pretrained(model_name_tinyllama)
+model_tinyllama = AutoModelForCausalLM.from_pretrained(model_name_tinyllama,torch_dtype=torch.float32,device_map={"": "cpu"})
+
+def summarize_tinyllama(article):
+   # For causal models like TinyLlama, summarization isn't a direct task like with encoder-decoder models.
+   # We can prompt it to continue a summary.
+   prompt="Summarize the following article clearly and concisely:"
+   input_text = f"{prompt}\n{article}\nSummary:"
+   inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
+
+   # Generate tokens - the model will try to complete the input prompt.
+   # We need to adjust generation parameters for open-ended generation.
+   # max_new_tokens controls how much new text is generated after the prompt.
+   outputs = model_tinyllama.generate(
+       inputs["input_ids"],
+       attention_mask=inputs["attention_mask"],
+       max_new_tokens=500,  # Generate up to 300 new tokens for the summary
+       do_sample=True,     # Don't sample, use greedy decoding
+       temperature=0.7,
+       min_new_tokens=150,
+       top_p=0.9,
+       pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
+   )
+
+   # Decode the entire output sequence.
+   generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
+
+
+   # The generated text will include the original prompt. We need to extract the summary part.
+   # This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
+   summary_start_index = generated_text.find("Summary:") + len("Summary:")
+   summary = generated_text[summary_start_index:].strip()
+
+   return summary
+
+def answer_question_tinyllama(article, question):
+   # Formulate the prompt to guide the TinyLlama model to answer the question based on the article.
+   # We ask the model to act as an AI answering a question based on the provided text.
+   input_text = f"From this Article: {article}\n\n Answer the below Question: {question}\n\nAnswer:"
+
+   # Tokenize the input text
+   # Truncate if the combined article and question is too long
+   inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
+
+   # Generate the answer using the model.
+   # We use generate with parameters suitable for generating a concise answer.
+   outputs = model_tinyllama.generate(
+       inputs["input_ids"],
+       attention_mask=inputs["attention_mask"],
+       max_new_tokens=500,  # Generate up to 100 new tokens for the answer
+       do_sample=True,      # Use sampling to potentially get more varied answers
+       temperature=0.7,     # Control randomness
+       top_p=0.9,           # Nucleus sampling
+       pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
+   )
+   # Decode the generated sequence
+   generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
+
+   # The generated text will include the original prompt. We need to extract the answer part.
+   # This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
+   answer_start_index = generated_text.find("Answer:") + len("Answer:")
+   answer = generated_text[answer_start_index:].strip()
+
+   # Basic cleanup: remove potential repetition of the question or prompt in the answer
+   if answer.startswith(question):
+       answer = answer[len(question):].strip()
+
+   return answer
+
+
+st.title("Smart Article Insights Generator")
+st.markdown("Summarize an article or ask a question about it.")
+
+mode = st.radio("Select Mode", ["Summarize", "Answer Question"])
+
+article_input = st.text_area("Article Text", height=300, placeholder="Paste the article here...")
+
+question_input = None
+if mode == "Answer Question":
+   question_input = st.text_input("Question", placeholder="Enter your question here...")
+
+if st.button("Process"):
+   if mode == "Summarize":
+       if article_input:
+           with st.spinner("Generating summary..."):
+               output = summarize_tinyllama(article_input)
+               st.subheader("Summary")
+               st.write(output)
+       else:
+           st.warning("Please provide an article to summarize.")
+   elif mode == "Answer Question":
+       if article_input and question_input:
+           with st.spinner("Generating answer..."):
+               output = answer_question_tinyllama(article_input, question_input)
+               st.subheader("Answer")
+               st.write(output)
+       elif not article_input:
+           st.warning("Please provide an article to answer the question from.")
+       elif not question_input:
+           st.warning("Please provide a question to answer.")