# import streamlit as st
# from transformers import pipeline

# # ---- Load both models ----
# @st.cache_resource
# def load_summarizers():
#     model_name_ft = "AIsumit123/flan-t5-base_samsum_best_ckpt"  # your fine-tuned
#     summarizer_ft = pipeline("summarization", model=model_name_ft, tokenizer=model_name_ft)

#     model_name_ft2 = "philschmid/flan-t5-base-samsum"  # comparison fine-tuned
#     summarizer_ft2 = pipeline("summarization", model=model_name_ft2, tokenizer=model_name_ft2)

#     model_name = "google/flan-t5-base"  # pretrained
#     summarizer = pipeline("summarization", model=model_name, tokenizer=model_name)

#     return summarizer_ft, summarizer_ft2, summarizer


# summarizer_ft, summarizer_ft2, summarizer = load_summarizers()

# # ---- Streamlit Page Config ----
# st.set_page_config(page_title="Conversation Summarizer", page_icon="🤖", layout="wide")

# # ---- Custom CSS for Styling ----
# st.markdown("""
# <style>
# /* Background gradient */
# .stApp {
#     background: linear-gradient(to bottom right, #0f2027, #203a43, #2c5364);
#     color: #f5f6f7;
#     font-family: 'Segoe UI', sans-serif;
# }

# /* Title */
# h1, h2, h3 {
#     text-align: center;
#     color: #fdfdfd;
# }

# /* Subheader accent */
# h2, h3 {
#     color: #e0e0e0;
# }

# /* Input box styling */
# textarea {
#     border-radius: 12px !important;
# }

# /* Summary cards */
# .summary-card {
#     background: rgba(255, 255, 255, 0.08);
#     border-radius: 15px;
#     padding: 20px;
#     box-shadow: 0 0 10px rgba(255,255,255,0.05);
#     transition: transform 0.2s ease-in-out;
# }
# .summary-card:hover {
#     transform: scale(1.02);
# }

# /* Section divider */
# hr {
#     border: none;
#     height: 2px;
#     background: linear-gradient(to right, #00c6ff, #0072ff);
#     margin: 30px 0;
# }

# /* Sidebar */
# [data-testid="stSidebar"] {
#     background-color: rgba(15, 25, 35, 0.95);
   
# }
# [data-testid="stSidebar"] * {
#     color: white !important;
# }

# .stButton>button {
#     background: linear-gradient(90deg, #00c6ff, #0072ff);
#     color: white;
#     border-radius: 8px;
#     font-weight: bold;
#     border: none;
#     transition: 0.3s;
# }
# .stButton>button:hover {
#     background: linear-gradient(90deg, #0072ff, #00c6ff);
#     transform: translateY(-1px);
# }

# </style>
# """, unsafe_allow_html=True)


# # ---- Header Section ----
# st.markdown("<h1>🤖 Conversation Summarizer</h1>", unsafe_allow_html=True)
# st.markdown("<h3>✨ Compare Pretrained vs Fine-tuned FLAN-T5 Models ✨</h3>", unsafe_allow_html=True)
# st.write("Paste your **conversation** below and instantly compare how fine-tuning changes summary quality.")

# # ---- Text Input ----
# input_text = st.text_area(
#     "💬 Conversation Input:",
#     height=250,
#     placeholder="Person A: Hi, how are you?\nPerson B: I'm good, just finished work...",
# )

# # ---- Sidebar ----
# st.sidebar.header("⚙️ Summary Settings")
# max_length = st.sidebar.slider("Max summary length", 30, 200, 100, step=10)
# min_length = st.sidebar.slider("Min summary length", 10, 100, 30, step=5)
# num_beams = st.sidebar.slider("Number of beams", 1, 8, 4)

# # ---- Generate Button ----
# if st.button("✨ Generate Summaries"):
#     if input_text.strip():
#         with st.spinner("🧠 Models are thinking..."):
#             base_summary = summarizer(
#                 input_text,
#                 max_length=max_length,
#                 min_length=min_length,
#                 num_beams=num_beams,
#                 early_stopping=True,
#             )[0]["summary_text"]

#             ft_summary = summarizer_ft(
#                 input_text,
#                 max_length=max_length,
#                 min_length=min_length,
#                 num_beams=num_beams,
#                 early_stopping=True,
#             )[0]["summary_text"]

#             ft_summary2 = summarizer_ft2(
#                 input_text,
#                 max_length=max_length,
#                 min_length=min_length,
#                 num_beams=num_beams,
#                 early_stopping=True,
#             )[0]["summary_text"]

#         st.success("✅ Summaries Generated!")

#         # ---- Display Results ----
#         st.markdown("<hr>", unsafe_allow_html=True)
#         col1, col2, col3 = st.columns(3)

#         with col1:
#             st.markdown('<div class="summary-card"><h3>🧠 Base Model</h3><p>{}</p></div>'.format(base_summary), unsafe_allow_html=True)
#         with col2:
#             st.markdown('<div class="summary-card"><h3>🚀 Fine-tuned (Yours)</h3><p>{}</p></div>'.format(ft_summary), unsafe_allow_html=True)
#         with col3:
#             st.markdown('<div class="summary-card"><h3>🔬 Fine-tuned (Reference)</h3><p>{}</p></div>'.format(ft_summary2), unsafe_allow_html=True)

#         st.markdown("<hr>", unsafe_allow_html=True)
#     else:
#         st.warning("⚠️ Please enter a conversation to summarize.")

import streamlit as st
from transformers import pipeline

# ---- Load both models ----
@st.cache_resource
def load_summarizers():
    model_name_ft = "AIsumit123/flan-t5-base_samsum_best_ckpt"  # your fine-tuned
    summarizer_ft = pipeline("summarization", model=model_name_ft, tokenizer=model_name_ft)

    model_name_ft2 = "philschmid/flan-t5-base-samsum"  # comparison fine-tuned
    summarizer_ft2 = pipeline("summarization", model=model_name_ft2, tokenizer=model_name_ft2)

    model_name = "google/flan-t5-base"  # pretrained
    summarizer = pipeline("summarization", model=model_name, tokenizer=model_name)

    return summarizer_ft, summarizer_ft2, summarizer


summarizer_ft, summarizer_ft2, summarizer = load_summarizers()

# ---- Streamlit Page Config ----
st.set_page_config(page_title="Conversation Summarizer", page_icon="🤖", layout="wide")

# ---- Custom CSS for Styling ----
st.markdown("""
<style>
/* Background gradient */
.stApp {
    background: linear-gradient(to bottom right, #0f2027, #203a43, #2c5364);
    color: #f5f6f7;
    font-family: 'Segoe UI', sans-serif;
}

/* Title */
h1, h2, h3 {
    text-align: center;
    color: #fdfdfd;
}

/* Subheader accent */
h2, h3 {
    color: #e0e0e0;
}

/* Input box styling */
textarea {
    border-radius: 12px !important;
}

/* Summary cards */
.summary-card {
    background: rgba(255, 255, 255, 0.08);
    border-radius: 15px;
    padding: 20px;
    box-shadow: 0 0 10px rgba(255,255,255,0.05);
    transition: transform 0.2s ease-in-out;
    height: 100%;
}
.summary-card:hover {
    transform: scale(1.02);
}

/* Section divider */
hr {
    border: none;
    height: 2px;
    background: linear-gradient(to right, #00c6ff, #0072ff);
    margin: 30px 0;
}

/* Sidebar Styling - FIXED TEXT COLOR */
[data-testid="stSidebar"] {
    background-color: rgba(15, 25, 35, 0.95);
}

[data-testid="stSidebar"] * {
    color: white !important;
}

[data-testid="stSidebar"] .stSlider label,
[data-testid="stSidebar"] .stSlider div,
[data-testid="stSidebar"] .stSlider span {
    color: white !important;
}

/* Button styling */
.stButton>button {
    background: linear-gradient(90deg, #00c6ff, #0072ff);
    color: white;
    border-radius: 8px;
    font-weight: bold;
    border: none;
    transition: 0.3s;
    width: 100%;
    padding: 12px;
}
.stButton>button:hover {
    background: linear-gradient(90deg, #0072ff, #00c6ff);
    transform: translateY(-1px);
    box-shadow: 0 4px 12px rgba(0, 114, 255, 0.3);
}

/* Stats cards */
.stats-card {
    background: rgba(255, 255, 255, 0.05);
    border-radius: 10px;
    padding: 15px;
    text-align: center;
    margin: 5px;
}
</style>
""", unsafe_allow_html=True)

# ---- Header Section ----
st.markdown("<h1>🤖 Conversation Summarizer</h1>", unsafe_allow_html=True)
st.markdown("<h3>✨ Compare Pretrained vs Fine-tuned FLAN-T5 Models ✨</h3>", unsafe_allow_html=True)
st.write("Paste your **conversation** below and instantly compare how fine-tuning changes summary quality.")

# ---- Example Conversations ----
example_conversations = {
    "Select an example...": "",
    "Business Meeting": """Alex: Are we ready for the client presentation tomorrow?
Sarah: Almost. I just need to finalize the quarterly figures.
Mike: The slides are done, but we should rehearse the demo.
Alex: Let's meet at 3 PM today for a dry run.
Sarah: I'll bring the updated reports.
Mike: Perfect, I'll set up the conference room.""",
    
    "Casual Chat": """Tom: Hey, are you watching the game tonight?
Lisa: Which one? The championship?
Tom: Yeah, it starts at 8. Want to come over?
Lisa: Sure! Should I bring anything?
Tom: Just yourself! Maybe some snacks.
Lisa: Awesome, see you at 7:30!""",
    
    "Customer Support": """Agent: Thank you for calling support. How can I help?
Customer: I can't login to my account.
Agent: Are you getting an error message?
Customer: It says 'invalid password' but I'm sure it's correct.
Agent: Let me reset your password. Check your email for a link.
Customer: Got it! Thanks for your help."""
}

# ---- Text Input ----
selected_example = st.selectbox("Choose an example conversation:", list(example_conversations.keys()))
input_text = st.text_area(
    "💬 Conversation Input:",
    height=250,
    value=example_conversations[selected_example],
    placeholder="Person A: Hi, how are you?\nPerson B: I'm good, just finished work...",
)

# ---- Sidebar ----
st.sidebar.header("⚙️ Summary Settings")
max_length = st.sidebar.slider("Max summary length", 30, 200, 100, step=10)
min_length = st.sidebar.slider("Min summary length", 10, 100, 30, step=5)
num_beams = st.sidebar.slider("Number of beams", 1, 8, 4, help="Higher values = better quality but slower")

with st.sidebar.expander("Advanced Settings"):
    repetition_penalty = st.slider("Repetition penalty", 1.0, 2.0, 1.2, 0.1)
    length_penalty = st.slider("Length penalty", 0.5, 2.0, 1.0, 0.1)

# ---- Generate Button ----
if st.button("✨ Generate Summaries", use_container_width=True):
    if input_text.strip():
        with st.spinner("🧠 Models are thinking..."):
            try:
                base_summary = summarizer(
                    input_text,
                    max_length=max_length,
                    min_length=min_length,
                    num_beams=num_beams,
                    early_stopping=True,
                    repetition_penalty=repetition_penalty,
                    length_penalty=length_penalty,
                )[0]["summary_text"]

                ft_summary = summarizer_ft(
                    input_text,
                    max_length=max_length,
                    min_length=min_length,
                    num_beams=num_beams,
                    early_stopping=True,
                    repetition_penalty=repetition_penalty,
                    length_penalty=length_penalty,
                )[0]["summary_text"]

                ft_summary2 = summarizer_ft2(
                    input_text,
                    max_length=max_length,
                    min_length=min_length,
                    num_beams=num_beams,
                    early_stopping=True,
                    repetition_penalty=repetition_penalty,
                    length_penalty=length_penalty,
                )[0]["summary_text"]

                st.success("✅ Summaries Generated!")

                # ---- Display Results ----
                st.markdown("<hr>", unsafe_allow_html=True)
                
                # Stats row
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.markdown(f'<div class="stats-card"><b>Original Length</b><br>{len(input_text.split())} words</div>', unsafe_allow_html=True)
                with col2:
                    st.markdown(f'<div class="stats-card"><b>Base Summary</b><br>{len(base_summary.split())} words</div>', unsafe_allow_html=True)
                with col3:
                    st.markdown(f'<div class="stats-card"><b>Your Model</b><br>{len(ft_summary.split())} words</div>', unsafe_allow_html=True)
                with col4:
                    st.markdown(f'<div class="stats-card"><b>Reference Model</b><br>{len(ft_summary2.split())} words</div>', unsafe_allow_html=True)

                # Summary cards
                st.markdown("<br>", unsafe_allow_html=True)
                col1, col2, col3 = st.columns(3)

                with col1:
                    st.markdown(f'''
                    <div class="summary-card">
                        <h3>🧠 Base Model</h3>
                        <p style="color: #a8d8ea">{base_summary}</p>
                    </div>
                    ''', unsafe_allow_html=True)
                    
                with col2:
                    st.markdown(f'''
                    <div class="summary-card">
                        <h3>🚀 Your Fine-tuned</h3>
                        <p style="color: #a8e6cf">{ft_summary}</p>
                    </div>
                    ''', unsafe_allow_html=True)
                    
                with col3:
                    st.markdown(f'''
                    <div class="summary-card">
                        <h3>🔬 Reference Model</h3>
                        <p style="color: #ffd3b6">{ft_summary2}</p>
                    </div>
                    ''', unsafe_allow_html=True)

                st.markdown("<hr>", unsafe_allow_html=True)
                
            except Exception as e:
                st.error(f"❌ Error generating summaries: {str(e)}")
    else:
        st.warning("⚠️ Please enter a conversation to summarize.")

# ---- Footer ----
st.markdown("---")
st.markdown(
    "<div style='text-align: center; color: #888;'>"
    "Built with ❤️ using Streamlit & Hugging Face Transformers"
    "</div>", 
    unsafe_allow_html=True
)