Update app.py

app.py

@@ -1,58 +1,66 @@
-# import torch
-import streamlit as st
-# import numpy as np
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-# from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-def load_model(model_name):
-    if model_name == "T5":
-        model = T5ForConditionalGeneration.from_pretrained('google/flan-t5-base')
-        tokenizer = T5Tokenizer.from_pretrained('google/flan-t5-base')
-        return model, tokenizer
-    if model_name == "Llama3":
-        model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
-        return model, tokenizer
-    if model_name == "Llama3-Instruct":
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-        model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-        return model, tokenizer
-    else:
-        st.error(f"Model {model_name} not available.")
-        return None, None
-
-def generate_question(model,tokenizer,context):
-    input_text = 'Generate a question from this: ' + context
-    input_ids = tokenizer(input_text, return_tensors='pt').input_ids
-    outputs = model.generate(input_ids,max_length=512)
-    output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
-    return output_text
-
-def main():
-    st.title("Question Generation From Given Text")
-    context = st.text_area("Enter text","Laughter is the best medicine.")
-    st.write("Select a model and provide the text to generate questions.")
-    model_choice = st.selectbox("Select a model", ["T5", "Llama3", "Llama3-Instruct"])
-
-    if st.button("Generate Questions"):
-        model, tokenizer = load_model(model_choice)
-        if model and tokenizer:
-            questions = generate_question(model, tokenizer, context)
-            st.write("Generated Question:")
-            st.write(questions)
-        else:
-            st.error("Model loading failed.")
-    # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
-    # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
-    # tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5_squad_v1")
-    # model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_squad_v1")
-    # input_text = 'Generate a question from this: ' + context
-    # input_ids = tokenizer(input_text, return_tensors='pt').input_ids
-    # outputs = model.generate(input_ids)
-    # output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
-    # st.write("Generated question:")
-    # st.write(output_text)
-
-if __name__ == '__main__':
+# import torch
+import streamlit as st
+# import numpy as np
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+# from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+def load_model(model_name):
+    if model_name == "T5":
+        model = T5ForConditionalGeneration.from_pretrained('google/flan-t5-base')
+        tokenizer = T5Tokenizer.from_pretrained('google/flan-t5-base')
+        return model, tokenizer
+    if model_name == "Llama3":
+        model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
+        return model, tokenizer
+    if model_name == "Llama3-Instruct":
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+        model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+        return model, tokenizer
+    if model_name == "Phi3":
+        tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+        return model, tokenizer
+    if model_name == "Gemma":
+        tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+        model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
+        return model, tokenizer
+    else:
+        st.error(f"Model {model_name} not available.")
+        return None, None
+
+def generate_question(model,tokenizer,context):
+    input_text = 'Generate a question from this: ' + context
+    input_ids = tokenizer(input_text, return_tensors='pt').input_ids
+    outputs = model.generate(input_ids,max_length=512)
+    output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
+    return output_text
+
+def main():
+    st.title("Question Generation From Given Text")
+    context = st.text_area("Enter text","Laughter is the best medicine.")
+    st.write("Select a model and provide the text to generate questions.")
+    model_choice = st.selectbox("Select a model", ["T5", "Llama3", "Llama3-Instruct","Phi3","Gemma"])
+
+    if st.button("Generate Questions"):
+        model, tokenizer = load_model(model_choice)
+        if model and tokenizer:
+            questions = generate_question(model, tokenizer, context)
+            st.write("Generated Question:")
+            st.write(questions)
+        else:
+            st.error("Model loading failed.")
+    # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
+    # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
+    # tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5_squad_v1")
+    # model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_squad_v1")
+    # input_text = 'Generate a question from this: ' + context
+    # input_ids = tokenizer(input_text, return_tensors='pt').input_ids
+    # outputs = model.generate(input_ids)
+    # output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
+    # st.write("Generated question:")
+    # st.write(output_text)
+
+if __name__ == '__main__':
     main()