Streamlit UI commit

app.py

@@ -0,0 +1,80 @@
+import streamlit as st
+import pickle
+#from Summarizer_Helper import Summary_Gen
+
+from gpt4all import GPT4All
+import textwrap
+
+with open('examples.pkl', 'rb') as f:
+    example_list = pickle.load(f)
+
+# Model initialization (assuming the model is already downloaded)
+from huggingface_hub import hf_hub_download
+model_path = "models"
+model_name = "Llama-2-7b-MOM_Summar.Q2_K.gguf"
+# hf_hub_download(repo_id="sasvata/Llama2-7b-MOM-Summary-Finetuned-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
+
+print("Start the model init process")
+model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
+print("Finish the model init process")
+
+## Function to convert plain text to markdown format
+def to_markdown(text):
+  text = text.replace('•', '  *')
+  return textwrap.indent(text, '> ', predicate=lambda _: True)
+
+# Default system prompt for generating conversation summaries
+DEFAULT_SYSTEM_PROMPT = """
+Below is a conversation between a human and an AI agent. Write a summary of the conversation.
+""".strip()
+
+def generate_prompt(
+    Transcript: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT
+) -> str:
+    return f"""### Instruction: {system_prompt}
+
+### Input:
+{Transcript.strip()}
+
+### Response:
+""".strip()
+
+# Function to generate summary with the help of fine-tuned model
+def Summary_Gen(Transcript):
+    prompt = generate_prompt(Transcript)
+    summary = model.generate(prompt=prompt,max_tokens=4096)
+    # summary_output = to_markdown(summary)
+    return summary_output
+
+# Function for text summarization
+def summarize_text(text):
+    # Your text summarization logic here (replaced with Summary_Gen)
+    summarized_text = Summary_Gen(text)
+    return summarized_text
+
+
+st.set_page_config(layout="wide", page_title="MOM-Summary-Generator📑", page_icon="📑")
+st.title("Minutes Of Meeting (MOM) - Summary Generator 📑")
+
+# Text input and output elements
+option = st.selectbox(
+   "Choose Example",
+   example_list,
+   index=None,
+   placeholder="Choose Example",
+)
+
+
+summary_output = ""
+col1, col2 = st.columns(2)
+
+col1.title('Input')   
+col1.container(height=500, border=True).text_area("", option, height=1000)
+if col1.button("Summarize"):
+    with st.spinner('Wait for it...'):
+        summary_output = summarize_text(option)
+    
+
+col2.title('Output')   
+col2.container(height=500, border=True).markdown(summary_output, unsafe_allow_html=True)
+