Update app.py

app.py

@@ -1,166 +1,169 @@
-import gradio as gr
-import requests
-import pandas as pd
-import io
-from docx import Document
-import tempfile
-
-API_BASE_URL = "https://pubmed-api-jwfq.onrender.com/search_pubmed"
-
-global_df = None  # Global variable to store search results for export
-
-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
-
-        # Apply journal filtering
-        if filter_journal and filter_journal != "All":
-            articles = [a for a in articles if filter_journal.lower() in a['Journal'].lower()]
-
-        # Apply year filtering
-        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)]
-
-        # Apply 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 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):
-    """
-    Exports search results as a CSV or DOCX file.
-    - Returns the file path instead of BytesIO to avoid TypeError in Gradio.
-    """
-    if df is None or df.empty:
-        return None
-
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=f".{format_type.lower()}")
-    temp_file_path = temp_file.name  # Store the temporary file path
-
-    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["Title"], level=2)
-            doc.add_paragraph(f"📖 Journal: {row['Journal']} ({row['Year']})")
-            doc.add_paragraph(f"👨‍🔬 Authors: {row['Authors']}")
-            doc.add_paragraph(f"🔗 Link: {row['PubMed_URL']}")
-            doc.add_paragraph(f"📄 Abstract: {row['Abstract']}")
-            doc.add_paragraph("---")
-        doc.save(temp_file_path)
-
-    temp_file.close()  # Close the file before returning the path
-    return temp_file_path  # Return file path instead of BytesIO
-
-
-with gr.Blocks() as app:
-    gr.Markdown("""
-    # 🔍 **PubMed Search Tool with Advanced Features**
-
-    ## 📖 **How to Use This App**
-    1️⃣ **Enter a Search Query** *(e.g., "Deep Learning in Psychiatry")*
-    2️⃣ **Set the Number of Results & Page Number** *(Default: 10 results per page)*
-    3️⃣ **Choose Sorting Option** *(Year, Title, or Journal - Default: Year)*
-    4️⃣ **(Optional) Filter by Journal Name** *(e.g., "Nature", "JAMA")*
-    5️⃣ **(Optional) Filter by Year Range** *(Set min & max year, e.g., 2015 - 2023)*
-    6️⃣ **Click "🔍 Search" to fetch results**
-    7️⃣ **Click "📂 Export as CSV" or "📄 Export as Word DOCX" to save articles**
-    8️⃣ **Click "📄 Show Abstract" under each result to expand full abstract**
-
-    ## ⚠️ **Important Notes**
-    - **Sorting & Filtering can be combined** *(e.g., show only "Nature" articles from 2020-2024, sorted by Title)*
-    
-    """)
-
-    with gr.Row():
-        query_input = gr.Textbox(label="🔎 Search Query", placeholder="Enter topic (e.g., 'Neural Networks in Psychiatry')", lines=1)
-
-    with gr.Row():
-        max_results_input = gr.Slider(1, 50, value=10, step=1, label="📄 Number of Results per Page")
-        page_input = gr.Slider(1, 200, value=1, step=1, label="📄 Page Number")
-
-    with gr.Row():
-        sort_input = gr.Dropdown(choices=["Year", "Title", "Journal"], value="Year", label="🔄 Sort By")
-        journal_filter_input = gr.Textbox(label="🎯 Filter by Journal (Optional)", placeholder="Enter journal name or leave blank")
-
-    with gr.Row():
-        min_year_input = gr.Number(label="📅 Min Year", value=None)
-        max_year_input = gr.Number(label="📅 Max Year", value=None)
-
-    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")
-
-    results_output = gr.HTML()
-    export_csv_output = gr.File(label="Download CSV")
-    export_docx_output = gr.File(label="Download Word DOCX")
-
-    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
-        return result_text
-
-    def export_csv():
-        if global_df is not None:
-            return export_results(global_df, "CSV")
-
-    def export_docx():
-        if global_df is not None:
-            return export_results(global_df, "DOCX")
-
-    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)
-
-    export_csv_button.click(export_csv, outputs=export_csv_output)
-    export_docx_button.click(export_docx, outputs=export_docx_output)
-
-if __name__ == "__main__":
-    app.launch(inbrowser=True)
+import gradio as gr
+import requests
+import pandas as pd
+import io
+from docx import Document
+import tempfile
+import os
+os.system("pip install python-docx")
+
+
+API_BASE_URL = "https://pubmed-api-jwfq.onrender.com/search_pubmed"
+
+global_df = None  # Global variable to store search results for export
+
+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
+
+        # Apply journal filtering
+        if filter_journal and filter_journal != "All":
+            articles = [a for a in articles if filter_journal.lower() in a['Journal'].lower()]
+
+        # Apply year filtering
+        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)]
+
+        # Apply 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 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):
+    """
+    Exports search results as a CSV or DOCX file.
+    - Returns the file path instead of BytesIO to avoid TypeError in Gradio.
+    """
+    if df is None or df.empty:
+        return None
+
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=f".{format_type.lower()}")
+    temp_file_path = temp_file.name  # Store the temporary file path
+
+    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["Title"], level=2)
+            doc.add_paragraph(f"📖 Journal: {row['Journal']} ({row['Year']})")
+            doc.add_paragraph(f"👨‍🔬 Authors: {row['Authors']}")
+            doc.add_paragraph(f"🔗 Link: {row['PubMed_URL']}")
+            doc.add_paragraph(f"📄 Abstract: {row['Abstract']}")
+            doc.add_paragraph("---")
+        doc.save(temp_file_path)
+
+    temp_file.close()  # Close the file before returning the path
+    return temp_file_path  # Return file path instead of BytesIO
+
+
+with gr.Blocks() as app:
+    gr.Markdown("""
+    # 🔍 **PubMed Search Tool with Advanced Features**
+
+    ## 📖 **How to Use This App**
+    1️⃣ **Enter a Search Query** *(e.g., "Deep Learning in Psychiatry")*
+    2️⃣ **Set the Number of Results & Page Number** *(Default: 10 results per page)*
+    3️⃣ **Choose Sorting Option** *(Year, Title, or Journal - Default: Year)*
+    4️⃣ **(Optional) Filter by Journal Name** *(e.g., "Nature", "JAMA")*
+    5️⃣ **(Optional) Filter by Year Range** *(Set min & max year, e.g., 2015 - 2023)*
+    6️⃣ **Click "🔍 Search" to fetch results**
+    7️⃣ **Click "📂 Export as CSV" or "📄 Export as Word DOCX" to save articles**
+    8️⃣ **Click "📄 Show Abstract" under each result to expand full abstract**
+
+    ## ⚠️ **Important Notes**
+    - **Sorting & Filtering can be combined** *(e.g., show only "Nature" articles from 2020-2024, sorted by Title)*
+    
+    """)
+
+    with gr.Row():
+        query_input = gr.Textbox(label="🔎 Search Query", placeholder="Enter topic (e.g., 'Neural Networks in Psychiatry')", lines=1)
+
+    with gr.Row():
+        max_results_input = gr.Slider(1, 50, value=10, step=1, label="📄 Number of Results per Page")
+        page_input = gr.Slider(1, 200, value=1, step=1, label="📄 Page Number")
+
+    with gr.Row():
+        sort_input = gr.Dropdown(choices=["Year", "Title", "Journal"], value="Year", label="🔄 Sort By")
+        journal_filter_input = gr.Textbox(label="🎯 Filter by Journal (Optional)", placeholder="Enter journal name or leave blank")
+
+    with gr.Row():
+        min_year_input = gr.Number(label="📅 Min Year", value=None)
+        max_year_input = gr.Number(label="📅 Max Year", value=None)
+
+    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")
+
+    results_output = gr.HTML()
+    export_csv_output = gr.File(label="Download CSV")
+    export_docx_output = gr.File(label="Download Word DOCX")
+
+    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
+        return result_text
+
+    def export_csv():
+        if global_df is not None:
+            return export_results(global_df, "CSV")
+
+    def export_docx():
+        if global_df is not None:
+            return export_results(global_df, "DOCX")
+
+    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)
+
+    export_csv_button.click(export_csv, outputs=export_csv_output)
+    export_docx_button.click(export_docx, outputs=export_docx_output)
+
+if __name__ == "__main__":
+    app.launch(inbrowser=True)