Update app.py

app.py

@@ -1,45 +1,26 @@
-import os
 import streamlit as st
+from transformers import pipeline
 
+# Load the text generation model
+generator = pipeline("text-generation", model="distilgpt2")
 
-from dotenv import load_dotenv
-from langchain.llms import HuggingFaceEndpoint
+# Streamlit app title
+st.title("Text Generation with DistilGPT-2")
 
-load_dotenv()
+# Input text from the user
+input_text = st.text_area("Enter your prompt:", "In this course, we will teach you how to")
 
-os.environ["HUGGINGFACEHUB_API_TOKEN"]=os.getenv("hf_token")
+# Number of sequences to generate
+num_sequences = st.slider("Number of sequences to generate:", 1, 5, 2)
 
-huggingface_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
+# Generate button
+if st.button("Generate Text"):
+    with st.spinner("Generating..."):
+        # Generate text using the model
+        generated_texts = generator(input_text, max_length=30, num_return_sequences=num_sequences)
+    
+    # Display generated texts
+    for i, generated in enumerate(generated_texts):
+        st.subheader(f"Generated Text {i + 1}")
+        st.write(generated['generated_text'])
 
-#Function to return the response
-def load_answer(question):
-    # "text-davinci-003" model is depreciated, so using the latest one https://platform.openai.com/docs/deprecations
-    if question:
-        llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
-
-        #Last week langchain has recommended to use invoke function for the below please :)
-        answer=llm.invoke(question)
-        return answer
-
-
-#App UI starts here
-st.set_page_config(page_title="LangChain Demo - Mistral", page_icon=":robot:")
-st.header("LangChain Demo - Mistral")
-
-#Gets the user input
-def get_text():
-    input_text = st.text_input("You: ", key="input")
-    return input_text
-
-
-user_input=get_text()
-response = load_answer(user_input)
-
-submit = st.button('Generate')  
-
-#If generate button is clicked
-if submit:
-
-    st.subheader("Answer:")
-
-    st.write(response)