Update app.py

app.py

@@ -1,60 +1,25 @@
 
 
+import streamlit as st
 import os
 HF_TOKEN = os.getenv('HF_TOKEN')
-
 from huggingface_hub import HfFolder
-
 # Set the token using HfFolder (this persists the token)
 HfFolder.save_token(HF_TOKEN)
 
 import transformers
-from transformers import VisionEncoderDecoderModel, AutoTokenizer, pipeline, AutoModel
+from transformers import pipeline
+
+generator = pipeline("text-generation")
+text = st.text_area("your input")
+
+if text:
+    out = generator(text)
+    st.json(out)
+
+
+
+
+
 
-import streamlit as st
 
-# Set Hugging Face API Token if required
-"""
-os.environ["HF_HOME"] = "path_to_your_huggingface_cache_directory"
-os.environ["TRANSFORMERS_CACHE"] = "path_to_your_transformers_cache_directory"
-os.environ["HF_DATASETS_CACHE"] = "path_to_your_datasets_cache_directory"
-os.environ["HF_METRICS_CACHE"] = "path_to_your_metrics_cache_directory"
-os.environ["HF_MODULES_CACHE"] = "path_to_your_modules_cache_directory"
-os.environ["HF_TOKEN"] = "your_hugging_face_access_token"
-"""
-
-# Setup Streamlit interface for input
-st.title("Image to Text Model")
-
-# Using Pipeline
-st.header("Using Pipeline for Image Captioning")
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Assuming the pipeline handles image files directly
-    pipe = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-    try:
-        result = pipe(uploaded_file.getvalue())
-        st.write("Generated Caption:", result[0]['generated_text'])
-    except Exception as e:
-        st.error(f"Failed to generate caption: {str(e)}")
-
-# Load model directly for further analysis or different processing steps
-st.header("Load Model Directly")
-
-# Assuming 'nlpconnect/vit-gpt2-image-captioning' is your model identifier
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-# Example of how you might use model and tokenizer directly
-# This section can be customized based on what you need to do with the model
-if st.button("Load Model Information"):
-    try:
-        st.text("Model and Tokenizer loaded successfully")
-        # Display some model details, for example:
-        st.text(f"Model Architecture: {model.__class__.__name__}")
-        st.text(f"Tokenizer Type: {tokenizer.__class__.__name__}")
-    except Exception as e:
-        st.error(f"Error loading model: {str(e)}")
-
-