Update app.py

app.py

@@ -1,175 +1,72 @@
 import gradio as gr
-import requests
 import pandas as pd
-import tempfile
+import requests
+import os
+from datetime import datetime
 from docx import Document
 
-API_BASE_URL = "https://pubmed-api-jwfq.onrender.com/search_pubmed"
-global_df = None  # Global variable to store search results
-
-
-def fetch_pubmed_articles(query, max_results=10, page=1, sort_by="Year",
-                          filter_journal="All", min_year=None, max_year=None):
-    """
-    Fetches PubMed articles and applies sorting and filtering.
-    """
-    try:
-        url = f"{API_BASE_URL}?query={query}&max_results={max_results}&page={page}"
-        response = requests.get(url)
-
-        if response.status_code != 200:
-            return f"⚠️ API Error: {response.status_code} - {response.text}", None
-
-        articles = response.json()
-        if not articles:
-            return "No articles found for this query.", None
-
-        for article in articles:
-            try:
-                article["Year"] = int(article["Year"])
-            except:
-                article["Year"] = 0
-
-        # Filter by journal
-        if filter_journal and filter_journal != "All":
-            articles = [a for a in articles if filter_journal.lower() in a['Journal'].lower()]
-
-        # Filter by year
-        if min_year:
-            articles = [a for a in articles if a["Year"] >= int(min_year)]
-        if max_year:
-            articles = [a for a in articles if a["Year"] <= int(max_year)]
-
-        # Sorting
-        if sort_by == "Year":
-            articles.sort(key=lambda x: x["Year"], reverse=True)
-        elif sort_by == "Title":
-            articles.sort(key=lambda x: x["Title"])
-        elif sort_by == "Journal":
-            articles.sort(key=lambda x: x["Journal"])
-
-        # Format markdown results
-        formatted_results = []
-        for article in articles:
-            formatted_results.append(
-                f"## 📰 {article['Title']}\n"
-                f"📖 **<span style='color:blue'>{article['Journal']}</span>** ({article['Year']})\n"
-                f"👨‍🔬 **<span style='color:gray'>{article['Authors']}</span>**\n"
-                f"🔗 [Read on PubMed]({article['PubMed_URL']})\n\n"
-                f"<details><summary>📄 **Show Abstract**</summary>\n{article['Abstract']}\n</details>"
-                f"\n---\n"
-            )
-
-        df = pd.DataFrame(articles)
-        return "\n\n".join(formatted_results), df
-
-    except Exception as e:
-        return f"⚠️ Error fetching data: {str(e)}", None
-
-
-def export_results(df, format_type):
-    """
-    Safely exports the given DataFrame to a temporary file (CSV or DOCX).
-    """
-    import traceback
-
-    if df is None or df.empty:
-        print("⚠️ Warning: DataFrame is empty or None. Nothing to export.")
-        return None
-
-    try:
-        suffix = f".{format_type.lower()}"
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
-        temp_file_path = temp_file.name
-
-        if format_type == "CSV":
-            df.to_csv(temp_file_path, index=False)
-
-        elif format_type == "DOCX":
-            doc = Document()
-            doc.add_heading("PubMed Search Results", level=1)
-            for _, row in df.iterrows():
-                doc.add_heading(row.get("Title", "Untitled"), level=2)
-                doc.add_paragraph(f"📖 Journal: {row.get('Journal', 'Unknown')} ({row.get('Year', '')})")
-                doc.add_paragraph(f"👨‍🔬 Authors: {row.get('Authors', 'N/A')}")
-                doc.add_paragraph(f"🔗 Link: {row.get('PubMed_URL', 'N/A')}")
-                doc.add_paragraph(f"📄 Abstract: {row.get('Abstract', '')}")
-                doc.add_paragraph("---")
-            doc.save(temp_file_path)
-
-        temp_file.close()
-        return temp_file_path
-
-    except Exception as e:
-        print("❌ Export failed:", str(e))
-        traceback.print_exc()
-        return None
-
-
-def export_csv():
-    if global_df is not None:
-        file_path = export_results(global_df, "CSV")
-        if isinstance(file_path, str):
-            return file_path
-    return None
-
-
-def export_docx():
-    if global_df is not None:
-        file_path = export_results(global_df, "DOCX")
-        if isinstance(file_path, str):
-            return file_path
-    return None
-
-
-def search_and_display(query, max_results, page, sort_by, journal_filter, min_year, max_year):
-    global global_df
-    result_text, df = fetch_pubmed_articles(query, max_results, page, sort_by, journal_filter, min_year, max_year)
-    global_df = df
-    print("🔍 Search completed. DataFrame loaded with", len(df) if df is not None else 0, "articles.")
-    return result_text
-
-
-with gr.Blocks() as app:
-    gr.Markdown("""
-    # 🔍 **PubMed Search Tool with Export Options**
-    ### Search biomedical literature and export results as CSV or Word DOCX.
-    """)
-
-    with gr.Row():
-        query_input = gr.Textbox(label="🔎 Search Query", placeholder="e.g., 'Deep Learning in Psychiatry'")
-    
-    with gr.Row():
-        max_results_input = gr.Slider(1, 50, value=10, step=1, label="📄 Results per Page")
-        page_input = gr.Slider(1, 100, value=1, step=1, label="📄 Page Number")
-
-    with gr.Row():
-        sort_input = gr.Dropdown(["Year", "Title", "Journal"], value="Year", label="🔄 Sort By")
-        journal_filter_input = gr.Textbox(label="🎯 Filter by Journal (optional)", placeholder="e.g., Nature")
+def search_api(query):
+    url = f"https://api.duckduckgo.com/?q={query}&format=json&no_redirect=1"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        related_topics = data.get("RelatedTopics", [])
+        results = []
+        for topic in related_topics:
+            if "Text" in topic and "FirstURL" in topic:
+                results.append({
+                    "Title": topic["Text"],
+                    "URL": topic["FirstURL"]
+                })
+        return pd.DataFrame(results)
+    else:
+        return pd.DataFrame([{"Title": "Error", "URL": "Failed to fetch results"}])
+
+# Save results
+def export_csv(df):
+    file_path = "search_results.csv"
+    df.to_csv(file_path, index=False)
+    return file_path
+
+def export_docx(df):
+    doc = Document()
+    doc.add_heading("Search Results", 0)
+    for _, row in df.iterrows():
+        doc.add_paragraph(f"{row['Title']}\n{row['URL']}")
+    file_path = "search_results.docx"
+    doc.save(file_path)
+    return file_path
+
+# Set up Gradio Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔎 Simple Search with Export Options")
+
+    query_input = gr.Textbox(label="Search Query", placeholder="Enter a topic...")
+    search_button = gr.Button("Search")
+    results_df = gr.Dataframe(label="Search Results", interactive=False)
 
     with gr.Row():
-        min_year_input = gr.Number(label="📅 Min Year", value=None)
-        max_year_input = gr.Number(label="📅 Max Year", value=None)
+        export_csv_button = gr.Button("Export as CSV")
+        export_docx_button = gr.Button("Export as Word DOCX")
 
-    with gr.Row():
-        search_button = gr.Button("🔍 Search")
-        export_csv_button = gr.Button("📂 Export as CSV")
-        export_docx_button = gr.Button("📄 Export as Word DOCX")
+    csv_output = gr.File(label="Download CSV")
+    docx_output = gr.File(label="Download DOCX")
 
-    results_output = gr.HTML()
-    export_csv_output = gr.File(label="Download CSV")
-    export_docx_output = gr.File(label="Download DOCX")
+    # Logic
+    def handle_search(q):
+        df = search_api(q)
+        return df
 
-    search_button.click(search_and_display,
-                        inputs=[query_input, max_results_input, page_input,
-                                sort_input, journal_filter_input, min_year_input, max_year_input],
-                        outputs=results_output)
+    def handle_csv_export(df):
+        return export_csv(df)
 
-    export_csv_button.click(export_csv, outputs=export_csv_output)
-    export_docx_button.click(export_docx, outputs=export_docx_output)
+    def handle_docx_export(df):
+        return export_docx(df)
 
+    search_button.click(handle_search, inputs=query_input, outputs=results_df)
+    export_csv_button.click(handle_csv_export, inputs=results_df, outputs=csv_output)
+    export_docx_button.click(handle_docx_export, inputs=results_df, outputs=docx_output)
 
-if __name__ == "__main__":
-    app.launch()
+demo.launch()