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import streamlit as st
from transformers import (
MarianMTModel, MarianTokenizer,
GPT2LMHeadModel, GPT2Tokenizer,
pipeline
)
st.title("Multi Chatbot")
models = {
"English to French": {
"name": "Helsinki-NLP/opus-mt-en-fr",
"description": "Translate English text to French."
},
"Sentiment Analysis": {
"name": "distilbert-base-uncased-finetuned-sst-2-english",
"description": "Analyze the sentiment of input text."
},
"Story Generator": {
"name": "distilgpt2",
"description": "Generate creative stories based on input."
}
}
st.sidebar.header("Choose a Model")
selected_model_key = st.sidebar.radio("Select a Model:", list(models.keys()))
model_name = models[selected_model_key]["name"]
model_description = models[selected_model_key]["description"]
st.sidebar.markdown(f"### Model Description\n{model_description}")
@st.cache_resource
def load_english_to_french():
tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
return tokenizer, model
@st.cache_resource
def load_sentiment_analysis():
return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
@st.cache_resource
def load_story_generator():
tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
model = GPT2LMHeadModel.from_pretrained("distilgpt2")
tokenizer.pad_token = tokenizer.eos_token # Set pad token to EOS token
return tokenizer, model
if selected_model_key == "English to French":
st.write("Loading English to French model...")
en_fr_tokenizer, en_fr_model = load_english_to_french()
st.write("English to French model loaded successfully.")
elif selected_model_key == "Sentiment Analysis":
st.write("Loading Sentiment Analysis model...")
sentiment_analyzer = load_sentiment_analysis()
st.write("Sentiment Analysis model loaded successfully.")
elif selected_model_key == "Story Generator":
st.write("Loading Story Generator model...")
story_gen_tokenizer, story_gen_model = load_story_generator()
st.write("Story Generator model loaded successfully.")
user_input = st.text_input("Enter your query:")
if user_input:
if selected_model_key == "English to French":
try:
inputs = en_fr_tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
outputs = en_fr_model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1)
translated_text = en_fr_tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(f"Translated Text: {translated_text}")
except Exception as e:
st.error(f"Error during translation: {e}")
elif selected_model_key == "Sentiment Analysis":
try:
result = sentiment_analyzer(user_input)[0]
st.write(f"Sentiment: {result['label']}")
st.write(f"Confidence: {result['score']:.2f}")
except Exception as e:
st.error(f"Error during sentiment analysis: {e}")
elif selected_model_key == "Story Generator":
try:
inputs = story_gen_tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
outputs = story_gen_model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"], # Pass the attention mask
max_length=200,
num_return_sequences=1,
temperature=0.7,
no_repeat_ngram_size=2
)
story = story_gen_tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(f"Generated Story: {story}")
except Exception as e:
st.error(f"Error during story generation: {e}")
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