Added images and huggingface files

HuggingFace/HuggingFaceSpacesApp.py

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+import streamlit as st
+from transformers import pipeline
+
+
+# Function to load the model
+@st.cache_data
+def load_model():
+    summarizer = pipeline("text2text-generation", model="pk248/pegasus-samsum")
+    return summarizer
+
+
+# Load the model
+model = load_model()
+
+# Streamlit UI
+st.title("Dialogue Summarization App")
+st.write("This app uses the pk248/pegasus-samsum model to summarize coversations.")
+
+# Text input
+user_input = st.text_area("Enter text to summarize:", height=200)
+
+# Summarize button
+if st.button("Summarize"):
+    if user_input:
+        # Model inference
+        summary = model(user_input, max_length=1000, min_length=5, length_penalty=2.0)
+        st.write(summary[0]["generated_text"])
+    else:
+        st.write("Please enter some text to summarize.")

HuggingFace/requirements.txt

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+huggingface_hub
+python-dotenv
+streamlit
+transformers
+torch

README.md

@@ -85,3 +85,18 @@ These are the steps that should be followed while implementing each step in the
     AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com
 
     ECR_REPOSITORY_NAME = simple-app
+
+# 8. HuggingFace:
+
+    The HuggingFace Folder contains the files required to push to HuggingFace Hub and deploy the model card Spaces.
+
+    Dialogue_Summarizer_notebook : Contains the complete codebase to train, finetune and push to hub.
+
+    HuggingFaceSpacesApp: Contains the complete Application code to host on HuggingFace Spaces. (Just rename the file to app.py while hosting)
+
+    requirements.txt : Contains the requirements for the hugging face application. (Upload the file to spaces folder).
+
+# 9. Corresponding Images:
+
+    ![Github Actions Deployement](/images/GithubActions_2.png)
+    *CI-CD pipeline through Github Actions*