Update c1.py

c1.py

@@ -1,72 +1,72 @@
-import streamlit as st
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-import torch
-import spacy
-import nltk
-
-#nltk.download('punkt')
-from nltk.tokenize import sent_tokenize
-
-# Load spaCy model
-nlp = spacy.load("en_core_web_sm")
-
-# Load T5 model and tokenizer
-model_name = "DevBM/t5-large-squad"
-model = T5ForConditionalGeneration.from_pretrained(model_name)
-tokenizer = T5Tokenizer.from_pretrained(model_name)
-
-# Function to extract keywords using spaCy
-def extract_keywords(text):
-    doc = nlp(text)
-    keywords = set()
-    # Extract named entities
-    for entity in doc.ents:
-        keywords.add(entity.text)
-    # Extract nouns and proper nouns
-    for token in doc:
-        if token.pos_ in ["NOUN", "PROPN"]:
-            keywords.add(token.text)
-    return list(keywords)
-
-# Function to map keywords to sentences
-def map_keywords_to_sentences(text, keywords):
-    sentences = sent_tokenize(text)
-    keyword_sentence_mapping = {}
-    for keyword in keywords:
-        for i, sentence in enumerate(sentences):
-            if keyword in sentence:
-                # Combine current sentence with surrounding sentences for context
-                start = max(0, i-1)
-                end = min(len(sentences), i+2)
-                context = ' '.join(sentences[start:end])
-                if keyword not in keyword_sentence_mapping:
-                    keyword_sentence_mapping[keyword] = context
-                else:
-                    keyword_sentence_mapping[keyword] += ' ' + context
-    return keyword_sentence_mapping
-
-# Function to generate questions
-def generate_question(context, answer):
-    input_text = f"<context> {context} <answer> {answer}"
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    outputs = model.generate(input_ids)
-    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return question
-
-# Streamlit interface
-st.title("Question Generator from Text")
-text = st.text_area("Enter text here:")
-if st.button("Generate Questions"):
-    if text:
-        keywords = extract_keywords(text)
-        keyword_sentence_mapping = map_keywords_to_sentences(text, keywords)
-        
-        st.subheader("Generated Questions:")
-        for keyword, context in keyword_sentence_mapping.items():
-            question = generate_question(context, keyword)
-            st.write(f"**Context:** {context}")
-            st.write(f"**Answer:** {keyword}")
-            st.write(f"**Question:** {question}")
-            st.write("---")
-    else:
-        st.write("Please enter some text to generate questions.")
+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+import spacy
+import nltk
+from b import b
+#nltk.download('punkt')
+from nltk.tokenize import sent_tokenize
+
+# Load spaCy model
+nlp = spacy.load("en_core_web_sm")
+
+# Load T5 model and tokenizer
+model_name = "DevBM/t5-large-squad"
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+# Function to extract keywords using spaCy
+def extract_keywords(text):
+    doc = nlp(text)
+    keywords = set()
+    # Extract named entities
+    for entity in doc.ents:
+        keywords.add(entity.text)
+    # Extract nouns and proper nouns
+    for token in doc:
+        if token.pos_ in ["NOUN", "PROPN"]:
+            keywords.add(token.text)
+    return list(keywords)
+
+# Function to map keywords to sentences
+def map_keywords_to_sentences(text, keywords):
+    sentences = sent_tokenize(text)
+    keyword_sentence_mapping = {}
+    for keyword in keywords:
+        for i, sentence in enumerate(sentences):
+            if keyword in sentence:
+                # Combine current sentence with surrounding sentences for context
+                start = max(0, i-1)
+                end = min(len(sentences), i+2)
+                context = ' '.join(sentences[start:end])
+                if keyword not in keyword_sentence_mapping:
+                    keyword_sentence_mapping[keyword] = context
+                else:
+                    keyword_sentence_mapping[keyword] += ' ' + context
+    return keyword_sentence_mapping
+
+# Function to generate questions
+def generate_question(context, answer):
+    input_text = f"<context> {context} <answer> {answer}"
+    input_ids = tokenizer.encode(input_text, return_tensors='pt')
+    outputs = model.generate(input_ids)
+    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return question
+
+# Streamlit interface
+st.title("Question Generator from Text")
+text = st.text_area("Enter text here:")
+if st.button("Generate Questions"):
+    if text:
+        keywords = extract_keywords(text)
+        keyword_sentence_mapping = map_keywords_to_sentences(text, keywords)
+        
+        st.subheader("Generated Questions:")
+        for keyword, context in keyword_sentence_mapping.items():
+            question = generate_question(context, keyword)
+            st.write(f"**Context:** {context}")
+            st.write(f"**Answer:** {keyword}")
+            st.write(f"**Question:** {question}")
+            st.write("---")
+    else:
+        st.write("Please enter some text to generate questions.")