Update app.py

app.py

@@ -1,38 +1,249 @@
 import gradio as gr
-from main2 import search_trials  # Importing from main2.py
+import pandas as pd
+import re
+from sklearn.feature_extraction.text import TfidfVectorizer
+import numpy as np
+from main2 import search_trials  # Import your updated search_trials
+
+PAGE_SIZE = 5
+PREVIEW_WORDS = 100  # Number of words in collapsed preview
+
+US_STATES = [
+    "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware",
+    "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky",
+    "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi",
+    "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico",
+    "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania",
+    "Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont",
+    "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming", "District of Columbia"
+]
+
+def split_sentences(text):
+    return [s.strip() for s in re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s', text) if s.strip()]
+
+def build_input_text(row):
+    text_parts = [
+        f"Intervention Name: {row.get('InterventionName', '')}",
+        f"Intervention Description: {row.get('InterventionDescription', '')}",
+        f"Brief Summary: {row.get('BriefSummary', '')}",
+        f"Eligibility Criteria: {row.get('EligibilityCriteria', '')}",
+        f"Primary Outcome Measure: {row.get('PrimaryOutcomeMeasure', '')}",
+        f"Primary Outcome Description: {row.get('PrimaryOutcomeDescription', '')}",
+        f"Start Date: {row.get('StartDate', '')}",
+        f"Primary Completion Date: {row.get('PrimaryCompletionDate', '')}"
+    ]
+    return " ".join([part for part in text_parts if part.strip()])
+
+def generate_summary(row, max_sentences=7, min_sentence_length=5):
+    text = build_input_text(row)
+    if not text.strip():
+        return ""
+    sentences = split_sentences(text)
+    sentences = [s for s in sentences if len(s.split()) >= min_sentence_length]
+    if not sentences:
+        return ""
+    if len(sentences) <= max_sentences:
+        return " ".join(sentences)
+    vectorizer = TfidfVectorizer(stop_words="english")
+    tfidf_matrix = vectorizer.fit_transform(sentences)
+    scores = np.array(tfidf_matrix.sum(axis=1)).flatten()
+    position_weights = np.linspace(1.5, 1.0, num=len(sentences))
+    combined_scores = scores * position_weights
+    top_indices = combined_scores.argsort()[-max_sentences:][::-1]
+    top_indices = sorted(top_indices)
+    summary_sentences = []
+    for i in top_indices:
+        s = sentences[i]
+        if re.match(r"^(Start Date|Primary Completion Date|Intervention Name|Primary Outcome Measure|Primary Outcome Description):", s):
+            continue
+        summary_sentences.append(s)
+    if len(summary_sentences) < max_sentences:
+        for i in top_indices:
+            if len(summary_sentences) >= max_sentences:
+                break
+            if sentences[i] not in summary_sentences:
+                summary_sentences.append(sentences[i])
+    return " ".join(summary_sentences[:max_sentences])
 
 def run_search(age, sex, state, keywords):
-    results = search_trials(
+    df = search_trials(
         user_age=age,
         user_sex=sex,
         user_state=state,
-        user_keywords=keywords
+        user_keywords=keywords,
+        generate_summaries=False
     )
-    return results
+    if df.empty:
+        return pd.DataFrame(), 0, None
+    total_pages = (len(df) + PAGE_SIZE - 1) // PAGE_SIZE
+    page_df = df.iloc[:PAGE_SIZE].copy()
+    page_df['LaymanSummary'] = ""
+    return page_df, total_pages, df
 
-with gr.Blocks() as demo:
-    gr.Markdown("#  Clinical Trials Search Tool")
-    gr.Markdown(
-        "Find **recruiting US clinical trials** that match your **age**, **sex**, "
-        "**state**, and optional **keywords**."
-    )
+def load_page(page_num, full_df):
+    if full_df is None or full_df.empty:
+        return pd.DataFrame()
+    start = page_num * PAGE_SIZE
+    end = start + PAGE_SIZE
+    page_df = full_df.iloc[start:end].copy()
+    page_df['LaymanSummary'] = page_df.apply(generate_summary, axis=1)
+    return page_df
 
-    with gr.Row():
-        age_input = gr.Number(label="Your Age", value=30)
-        sex_input = gr.Dropdown(["Male", "Female"], label="Sex", value="Male")
+def update_page_controls(page_num, total_pages):
+    prev_visible = gr.update(visible=page_num > 0)
+    next_visible = gr.update(visible=page_num < total_pages - 1)
+    page_text = f"Page {page_num + 1} of {total_pages}" if total_pages > 0 else ""
+    return prev_visible, next_visible, page_text
 
-    with gr.Row():
-        state_input = gr.Dropdown(["Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming"], label="State (full name or abbreviation)", value="California")
-        keywords_input = gr.Textbox(label="Keywords (comma separated)", placeholder="e.g., cancer, diabetes")
+def hide_empty_columns(df):
+    cols_to_keep = []
+    for col in df.columns:
+        col_values = df[col].dropna().astype(str).str.strip()
+        if not col_values.empty and any(val != "" for val in col_values):
+            cols_to_keep.append(col)
+    return df[cols_to_keep]
 
-    search_btn = gr.Button("Search Trials")
+def df_to_html_with_readmore(df: pd.DataFrame) -> str:
+    if df.empty:
+        return "<p>No matching trials found.</p>"
+    from html import escape
+    if "LaymanSummary" in df.columns:
+        cols = list(df.columns)
+        cols.insert(0, cols.pop(cols.index("LaymanSummary")))
+        df = df[cols]
+    df = hide_empty_columns(df)
+    html = ['''
+    <style>
+        table {
+            width: 100%;
+            border-collapse: collapse;
+            font-family: Arial, sans-serif;
+        }
+        th {
+            background-color: #007bff;
+            color: white;
+            padding: 12px;
+            text-align: left;
+            border: 1px solid #ddd;
+        }
+        td {
+            border: 1px solid #ddd;
+            padding: 12px;
+            vertical-align: top;
+            white-space: normal;
+            max-width: 1000px; /* 2.5x original 400px */
+            min-width: 1000px; /* force width */
+            word-wrap: break-word;
+        }
+        details summary {
+            cursor: pointer;
+            color: #007bff;
+            font-weight: bold;
+        }
+        details summary:after {
+            content: " (Read More)";
+            color: #0056b3;
+            font-weight: normal;
+        }
+        details[open] summary {
+            display: none; /* hide preview when expanded */
+        }
+        details div.full-text {
+            display: none;
+        }
+        details[open] div.full-text {
+            display: block;
+            margin-top: 8px;
+        }
+    </style>
+    ''']
+    html.append('<table><thead><tr>')
+    for col in df.columns:
+        html.append(f'<th>{escape(col)}</th>')
+    html.append('</tr></thead><tbody>')
+    for _, row in df.iterrows():
+        html.append('<tr>')
+        for col in df.columns:
+            val = str(row[col])
+            words = val.split()
+            if len(words) > PREVIEW_WORDS:
+                short_text = escape(" ".join(words[:PREVIEW_WORDS]) + "...")
+                full_text = escape(val)
+                cell_html = f'''
+                <div>
+                    <details>
+                        <summary>{short_text}</summary>
+                        <div class="full-text">{full_text}</div>
+                    </details>
+                </div>
+                '''
+            else:
+                cell_html = f'<div>{escape(val)}</div>'
+            html.append(f'<td>{cell_html}</td>')
+        html.append('</tr>')
+    html.append('</tbody></table>')
+    return "".join(html)
 
-    output_table = gr.Dataframe(label="Matching Trials", interactive=False)
+def on_search(age, sex, state, keywords):
+    df_page, total_pages, full_df = run_search(age, sex, state, keywords)
+    page_num = 0
+    if not df_page.empty:
+        df_page = load_page(page_num, full_df)
+    prev_vis, next_vis, page_text = update_page_controls(page_num, total_pages)
+    html_output = df_to_html_with_readmore(df_page)
+    return html_output, page_text, prev_vis, next_vis, page_num, total_pages, full_df, gr.update(visible=False), gr.update(visible=True)
 
+def on_page_change(increment, page_num, total_pages, full_df):
+    if full_df is None or full_df.empty:
+        return "<p>No matching trials found.</p>", "", gr.update(visible=False), gr.update(visible=False), 0
+    new_page = max(0, min(page_num + increment, total_pages - 1))
+    page_df = load_page(new_page, full_df)
+    prev_vis, next_vis, page_text = update_page_controls(new_page, total_pages)
+    html_output = df_to_html_with_readmore(page_df)
+    return html_output, page_text, prev_vis, next_vis, new_page
+
+def show_input_page():
+    return gr.update(visible=True), gr.update(visible=False)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Clinical Trials Search Tool with Pagination and Inline Read More")
+    with gr.Column(visible=True) as input_page:
+        gr.Markdown("Find **recruiting US clinical trials** that match your **age**, **sex**, **state**, and optional **keywords**.")
+        with gr.Row():
+            age_input = gr.Number(label="Your Age", value=30)
+            sex_input = gr.Dropdown(["Male", "Female", "All"], label="Sex", value="All")
+        with gr.Row():
+            state_input = gr.Dropdown(US_STATES, label="State", value="California")
+            keywords_input = gr.Textbox(label="Keywords", placeholder="e.g., Cancer, Diabetes")
+        search_btn = gr.Button("Search Trials")
+    with gr.Column(visible=False) as results_page:
+        output_html = gr.HTML()
+        total_pages_text = gr.Textbox(value="", interactive=False)
+        with gr.Row():
+            prev_btn = gr.Button("Previous Page")
+            next_btn = gr.Button("Next Page")
+            back_btn = gr.Button("Back")
+    page_num_state = gr.State(0)
+    total_pages_state = gr.State(0)
+    full_results_state = gr.State(None)
     search_btn.click(
-        fn=run_search,
+        fn=on_search,
         inputs=[age_input, sex_input, state_input, keywords_input],
-        outputs=output_table
+        outputs=[output_html, total_pages_text, prev_btn, next_btn, page_num_state, total_pages_state, full_results_state, input_page, results_page]
+    )
+    next_btn.click(
+        fn=on_page_change,
+        inputs=[gr.State(1), page_num_state, total_pages_state, full_results_state],
+        outputs=[output_html, total_pages_text, prev_btn, next_btn, page_num_state]
+    )
+    prev_btn.click(
+        fn=on_page_change,
+        inputs=[gr.State(-1), page_num_state, total_pages_state, full_results_state],
+        outputs=[output_html, total_pages_text, prev_btn, next_btn, page_num_state]
+    )
+    back_btn.click(
+        fn=show_input_page,
+        outputs=[input_page, results_page]
     )
 
 if __name__ == "__main__":