Create app.py

app.py

@@ -0,0 +1,70 @@
+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}")
+try:
+    if selected_model_key == "English to French":
+        st.write("Loading English to French model...")
+        tokenizer = MarianTokenizer.from_pretrained(model_name) 
+        model = MarianMTModel.from_pretrained(model_name)  
+        st.write("English to French model loaded successfully.")
+    elif selected_model_key == "Sentiment Analysis":
+        st.write("Loading Sentiment Analysis model...")
+        sentiment_analyzer = pipeline("sentiment-analysis", model=model_name)
+        st.write("Sentiment Analysis model loaded successfully.")
+    elif selected_model_key == "Story Generator":
+        st.write("Loading Story Generator model...")
+        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") 
+        model = GPT2LMHeadModel.from_pretrained("distilgpt2")
+        tokenizer.pad_token = tokenizer.eos_token
+        st.write("Story Generator model loaded successfully.")
+except Exception as e:
+    st.error(f"Failed to load the model: {e}")
+user_input = st.text_input("Enter your query:")
+if user_input:
+    if selected_model_key == "English to French":
+        try:
+            inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
+            outputs = model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
+            bot_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            st.write(f"Translated Text: {bot_response}")
+        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 = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
+            outputs = model.generate(inputs["input_ids"], max_length=500, num_return_sequences=1, no_repeat_ngram_size=2, temperature=0.7)
+            bot_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            st.write(f"Generated Story: {bot_response}")
+        except Exception as e:
+            st.error(f"Error during story generation: {e}")
+