delete app.py

app.py

@@ -1,532 +0,0 @@
-<<<<<<< HEAD
-# ✅ Optimized mtDNA MVP UI with Faster Pipeline & Required Feedback
-
-import gradio as gr
-from collections import Counter
-import csv
-import os
-from functools import lru_cache
-from mtdna_classifier import classify_sample_location
-import subprocess
-import json
-
-@lru_cache(maxsize=128)
-def classify_sample_location_cached(accession):
-    return classify_sample_location(accession)
-
-# Count and suggest final location
-def compute_final_suggested_location(rows):
-    candidates = [
-        row.get("Predicted Location", "").strip()
-        for row in rows
-        if row.get("Predicted Location", "").strip().lower() not in ["", "sample id not found", "unknown"]
-    ] + [
-        row.get("Inferred Region", "").strip()
-        for row in rows
-        if row.get("Inferred Region", "").strip().lower() not in  ["", "sample id not found", "unknown"]
-    ]
-
-    if not candidates:
-        return Counter(), ("Unknown", 0)
-
-    counts = Counter(candidates)
-    top_location, count = counts.most_common(1)[0]
-    return counts, (top_location, count)
-
-# Store feedback (with required fields)
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-
-'''creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
-
-scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
-
-def store_feedback_to_google_sheets(accession, answer1, answer2, contact=""):
-    if not answer1.strip() or not answer2.strip():
-        return "⚠️ Please answer both questions before submitting."
-
-    try:
-        # Define the scope and authenticate
-        scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-        creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
-        client = gspread.authorize(creds)
-
-        # Open the spreadsheet and worksheet
-        sheet = client.open("feedback_mtdna").sheet1  # You can change the name
-        sheet.append_row([accession, answer1, answer2, contact])
-        return "✅ Feedback submitted. Thank you!"
-    except Exception as e:
-        return f"❌ Error submitting feedback: {str(e)}"'''
-
-import os
-import json
-from oauth2client.service_account import ServiceAccountCredentials
-import gspread
-
-def store_feedback_to_google_sheets(accession, answer1, answer2, contact=""):
-    if not answer1.strip() or not answer2.strip():
-        return "⚠️ Please answer both questions before submitting."
-
-    try:
-        # ✅ Step: Load credentials from Hugging Face secret
-        creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
-        scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-        creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
-
-        # Connect to Google Sheet
-        client = gspread.authorize(creds)
-        sheet = client.open("feedback_mtdna").sheet1  # make sure sheet name matches
-
-        # Append feedback
-        sheet.append_row([accession, answer1, answer2, contact])
-        return "✅ Feedback submitted. Thank you!"
-
-    except Exception as e:
-        return f"❌ Error submitting feedback: {e}"
-
-
-def summarize_results(accession):
-    try:
-        output = classify_sample_location_cached(accession)
-        print(output)
-    except Exception as e:
-        return [], f"❌ Error: {e}"
-
-    if accession not in output:
-        return [], "❌ Accession not found in results."
-
-    isolate = next((k for k in output if k != accession), None)
-    row_score = []
-    rows = []
-
-    for key in [accession, isolate]:
-        if key not in output:
-            continue
-        sample_id_label = f"{key} ({'accession number' if key == accession else 'isolate of accession'})"
-        for section, techniques in output[key].items():
-            for technique, content in techniques.items():
-                source = content.get("source", "")
-                predicted = content.get("predicted_location", "")
-                haplogroup = content.get("haplogroup", "")
-                inferred = content.get("inferred_location", "")
-                context = content.get("context_snippet", "")[:300] if "context_snippet" in content else ""
-
-                row = {
-                    "Sample ID": sample_id_label,
-                    "Technique": technique,
-                    "Source": f"The region of haplogroup is inferred\nby using this source: {source}" if technique == "haplogroup" else source,
-                    "Predicted Location": "" if technique == "haplogroup" else predicted,
-                    "Haplogroup": haplogroup if technique == "haplogroup" else "",
-                    "Inferred Region": inferred if technique == "haplogroup" else "",
-                    "Context Snippet": context
-                }
-
-                row_score.append(row)
-                rows.append(list(row.values()))
-
-    location_counts, (final_location, count) = compute_final_suggested_location(row_score)
-    summary_lines = [f"### 🧭 Location Frequency Summary", "After counting all predicted and inferred locations:\n"]
-    summary_lines += [f"- **{loc}**: {cnt} times" for loc, cnt in location_counts.items()]
-    summary_lines.append(f"\n**Final Suggested Location:** 🗺️ **{final_location}** (mentioned {count} times)")
-    summary = "\n".join(summary_lines)
-
-    return rows, summary
-# Gradio UI
-with gr.Blocks() as interface:
-    gr.Markdown("# 🧬 mtDNA Location Classifier (MVP)")
-    gr.Markdown("Enter an accession number to infer geographic origin. You'll see predictions, confidence scores, and can submit feedback.")
-
-    with gr.Row():
-        accession = gr.Textbox(label="Enter Accession Number (e.g., KU131308)")
-        run_button = gr.Button("🔍 Submit and Classify")
-        reset_button = gr.Button("🔄 Reset")
-
-    status = gr.Markdown(visible=False)
-    headers = ["Sample ID", "Technique", "Source", "Predicted Location", "Haplogroup", "Inferred Region", "Context Snippet"]
-    output_table = gr.Dataframe(headers=headers, interactive=False)
-    output_summary = gr.Markdown()
-
-    gr.Markdown("---")
-    gr.Markdown("### 💬 Feedback (required)")
-    q1 = gr.Textbox(label="1️⃣ Was the inferred location accurate or helpful? Please explain.")
-    q2 = gr.Textbox(label="2️⃣ What would improve your experience with this tool?")
-    contact = gr.Textbox(label="📧 Your email or institution (optional)")
-    submit_feedback = gr.Button("✅ Submit Feedback")
-    feedback_status = gr.Markdown()
-
-    def classify_with_loading(accession):
-        return gr.update(value="⏳ Please wait... processing...", visible=True)
-
-    def classify_main(accession):
-        table, summary = summarize_results(accession)
-        return table, summary, gr.update(visible=False)
-
-    def reset_fields():
-        return "", "", "", "", "", [], "", gr.update(visible=False)
-
-    run_button.click(fn=classify_with_loading, inputs=accession, outputs=status)
-    run_button.click(fn=classify_main, inputs=accession, outputs=[output_table, output_summary, status])
-    submit_feedback.click(fn=store_feedback_to_google_sheets, inputs=[accession, q1, q2, contact], outputs=feedback_status)
-    reset_button.click(fn=reset_fields, inputs=[], outputs=[accession, q1, q2, contact, feedback_status, output_table, output_summary, status])
-
-interface.launch(share=True)
-=======
-# ✅ Optimized mtDNA MVP UI with Faster Pipeline & Required Feedback
-
-import gradio as gr
-from collections import Counter
-import csv
-import os
-from functools import lru_cache
-from mtdna_classifier import classify_sample_location 
-import subprocess
-import json
-import pandas as pd
-import io
-import re
-import tempfile
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-
-@lru_cache(maxsize=128)
-def classify_sample_location_cached(accession):
-    return classify_sample_location(accession)
-
-# Count and suggest final location
-def compute_final_suggested_location(rows):
-    candidates = [
-        row.get("Predicted Location", "").strip()
-        for row in rows
-        if row.get("Predicted Location", "").strip().lower() not in ["", "sample id not found", "unknown"]
-    ] + [
-        row.get("Inferred Region", "").strip()
-        for row in rows
-        if row.get("Inferred Region", "").strip().lower() not in  ["", "sample id not found", "unknown"]
-    ]
-
-    if not candidates:
-        return Counter(), ("Unknown", 0)
-
-    counts = Counter(candidates)
-    top_location, count = counts.most_common(1)[0]
-    return counts, (top_location, count)
-
-# Store feedback (with required fields)
-
-'''creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
-
-scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
-
-def store_feedback_to_google_sheets(accession, answer1, answer2, contact=""):
-    if not answer1.strip() or not answer2.strip():
-        return "⚠️ Please answer both questions before submitting."
-
-    try:
-        # Define the scope and authenticate
-        scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-        creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
-        client = gspread.authorize(creds)
-
-        # Open the spreadsheet and worksheet
-        sheet = client.open("feedback_mtdna").sheet1  # You can change the name
-        sheet.append_row([accession, answer1, answer2, contact])
-        return "✅ Feedback submitted. Thank you!"
-    except Exception as e:
-        return f"❌ Error submitting feedback: {str(e)}"'''
-
-def store_feedback_to_google_sheets(accession, answer1, answer2, contact=""):
-    if not answer1.strip() or not answer2.strip():
-        return "⚠️ Please answer both questions before submitting."
-
-    try:
-        # ✅ Step: Load credentials from Hugging Face secret
-        creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
-        scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-        creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
-
-        # Connect to Google Sheet
-        client = gspread.authorize(creds)
-        sheet = client.open("feedback_mtdna").sheet1  # make sure sheet name matches
-
-        # Append feedback
-        sheet.append_row([accession, answer1, answer2, contact])
-        return "✅ Feedback submitted. Thank you!"
-
-    except Exception as e:
-        return f"❌ Error submitting feedback: {e}"
-
-# helper function to extract accessions
-def extract_accessions_from_input(file=None, raw_text=""):
-    print(f"RAW TEXT RECEIVED: {raw_text}")
-    accessions = []
-    seen = set()
-    if file:
-        try:
-            if file.name.endswith(".csv"):
-                df = pd.read_csv(file)
-            elif file.name.endswith(".xlsx"):
-                df = pd.read_excel(file)
-            else:
-                return [], "Unsupported file format. Please upload CSV or Excel."
-            for acc in df.iloc[:, 0].dropna().astype(str).str.strip():
-                if acc not in seen:
-                    accessions.append(acc)
-                    seen.add(acc)
-        except Exception as e:
-            return [], f"Failed to read file: {e}"
-
-    if raw_text:
-        text_ids = [s.strip() for s in re.split(r"[\n,;\t]", raw_text) if s.strip()]
-        for acc in text_ids:
-            if acc not in seen:
-                accessions.append(acc)
-                seen.add(acc)
-
-    return list(accessions), None
-
-def summarize_results(accession):
-    try:
-        output, labelAncient_Modern, explain_label = classify_sample_location_cached(accession)
-        print(output)
-    except Exception as e:
-        return [], f"Error: {e}"
-
-    if accession not in output:
-        return [], "Accession not found in results."
-
-    isolate = next((k for k in output if k != accession), None)
-    row_score = []
-    rows = []
-
-    for key in [accession, isolate]:
-        if key not in output:
-            continue
-        sample_id_label = f"{key} ({'accession number' if key == accession else 'isolate of accession'})"
-        for section, techniques in output[key].items():
-            for technique, content in techniques.items():
-                source = content.get("source", "")
-                predicted = content.get("predicted_location", "")
-                haplogroup = content.get("haplogroup", "")
-                inferred = content.get("inferred_location", "")
-                context = content.get("context_snippet", "")[:300] if "context_snippet" in content else ""
-    
-                row = {
-                    "Sample ID": sample_id_label,
-                    "Technique": technique,
-                    "Source": f"The region of haplogroup is inferred\nby using this source: {source}" if technique == "haplogroup" else source,
-                    "Predicted Location": "" if technique == "haplogroup" else predicted,
-                    "Haplogroup": haplogroup if technique == "haplogroup" else "",
-                    "Inferred Region": inferred if technique == "haplogroup" else "",
-                    "Context Snippet": context
-                }
-
-                row_score.append(row)
-                rows.append(list(row.values()))
-
-    location_counts, (final_location, count) = compute_final_suggested_location(row_score)
-    summary_lines = [f"### 🧭 Location Frequency Summary", "After counting all predicted and inferred locations:\n"]
-    summary_lines += [f"- **{loc}**: {cnt} times" for loc, cnt in location_counts.items()]
-    summary_lines.append(f"\n**Final Suggested Location:** 🗺️ **{final_location}** (mentioned {count} times)")
-    summary = "\n".join(summary_lines)
-
-    return rows, summary, labelAncient_Modern, explain_label
-
-# save the batch input in excel file
-def save_to_excel(all_rows, summary_text, flag_text, filename):
-    with pd.ExcelWriter(filename) as writer:
-        # Save table
-        df = pd.DataFrame(all_rows, columns=["Sample ID", "Technique", "Source", "Predicted Location", "Haplogroup", "Inferred Region", "Context Snippet"])
-        df.to_excel(writer, sheet_name="Detailed Results", index=False)
-        
-        # Save summary
-        summary_df = pd.DataFrame({"Summary": [summary_text]})
-        summary_df.to_excel(writer, sheet_name="Summary", index=False)
-        
-        # Save flag
-        flag_df = pd.DataFrame({"Flag": [flag_text]})
-        flag_df.to_excel(writer, sheet_name="Ancient_Modern_Flag", index=False)
-
-# save the batch input in JSON file
-def save_to_json(all_rows, summary_text, flag_text, filename):
-    output_dict = {
-        "Detailed_Results": all_rows,
-        "Summary_Text": summary_text,
-        "Ancient_Modern_Flag": flag_text
-    }
-    with open(filename, "w") as f:
-        json.dump(output_dict, f, indent=2)
-
-# save the batch input in Text file
-def save_to_txt(all_rows, summary_text, flag_text, filename):
-    with open(filename, "w") as f:
-        f.write("=== Detailed Results ===\n")
-        for row in all_rows:
-            f.write(", ".join(str(x) for x in row) + "\n")
-        
-        f.write("\n=== Summary ===\n")
-        f.write(summary_text + "\n")
-        
-        f.write("\n=== Ancient/Modern Flag ===\n")
-        f.write(flag_text + "\n")
-
-def save_batch_output(all_rows, summary_text, flag_text, output_type):
-    tmp_dir = tempfile.mkdtemp()
-
-    if output_type == "Excel":
-        file_path = f"{tmp_dir}/batch_output.xlsx"
-        save_to_excel(all_rows, summary_text, flag_text, file_path)
-    elif output_type == "JSON":
-        file_path = f"{tmp_dir}/batch_output.json"
-        save_to_json(all_rows, summary_text, flag_text, file_path)
-    elif output_type == "TXT":
-        file_path = f"{tmp_dir}/batch_output.txt"
-        save_to_txt(all_rows, summary_text, flag_text, file_path)
-    else:
-        return None  # invalid option
-    
-    return file_path
-
-# run the batch
-def summarize_batch(file=None, raw_text=""):
-    accessions, error = extract_accessions_from_input(file, raw_text)
-    if error:
-        return [], "", "", f"Error: {error}"
-
-    all_rows = []
-    all_summaries = []
-    all_flags = []
-
-    for acc in accessions:
-        try:
-            rows, summary, label, explain = summarize_results(acc)
-            all_rows.extend(rows)
-            all_summaries.append(f"**{acc}**\n{summary}")
-            all_flags.append(f"**{acc}**: {label}\n_Explanation:_ {explain}")
-        except Exception as e:
-            all_summaries.append(f"**{acc}**: Failed - {e}")
-
-    summary_text = "\n\n---\n\n".join(all_summaries)
-    flag_text = "\n\n".join(all_flags)
-
-    return all_rows, summary_text, flag_text, gr.update(visible=False)
-
-# Gradio UI
-with gr.Blocks() as interface:
-    gr.Markdown("# 🧬 mtDNA Location Classifier (MVP)")
-
-    inputMode = gr.Radio(choices=["Single Accession", "Batch Input"], value="Single Accession", label="Choose Input Mode")
-
-    with gr.Group() as single_input_group:
-        single_accession = gr.Textbox(label="Enter Single Accession (e.g., KU131308)")
-
-    with gr.Group(visible=False) as batch_input_group:
-        raw_text = gr.Textbox(label="🧬 Paste Accession Numbers")
-        file_upload = gr.File(label="📁 Or Upload CSV/Excel File", file_types=[".csv", ".xlsx"], interactive=True, elem_id="file-upload-box")
-        print(raw_text)
-        # Make the file box smaller
-        gr.HTML('<style>#file-upload-box { width: 200px; }</style>')
-
-    with gr.Row():
-        run_button = gr.Button("🔍 Submit and Classify")
-        reset_button = gr.Button("🔄 Reset")
-
-    status = gr.Markdown(visible=False)
-
-    with gr.Group(visible=False) as results_group:
-        with gr.Row():
-            with gr.Column():
-                output_summary = gr.Markdown()
-            with gr.Column():
-                output_flag = gr.Markdown()
-        
-        gr.Markdown("---")
-        output_table = gr.Dataframe(
-            headers=["Sample ID", "Technique", "Source", "Predicted Location", "Haplogroup", "Inferred Region", "Context Snippet"],
-            interactive=False,
-            row_count=(5, "dynamic")
-        )
-
-        with gr.Row():
-            output_type = gr.Dropdown(choices=["Excel", "JSON", "TXT"], label="Select Output Format", value="Excel")
-            download_button = gr.Button("⬇️ Download Output")
-            download_file = gr.File(label="Download File Here")
-
-        gr.Markdown("---")
-
-        gr.Markdown("### 💬 Feedback (required)")
-        q1 = gr.Textbox(label="1️⃣ Was the inferred location accurate or helpful? Please explain.")
-        q2 = gr.Textbox(label="2️⃣ What would improve your experience with this tool?")
-        contact = gr.Textbox(label="📧 Your email or institution (optional)")
-        submit_feedback = gr.Button("✅ Submit Feedback")
-        feedback_status = gr.Markdown()
-
-    # Functions
-
-    def toggle_input_mode(mode):
-        if mode == "Single Accession":
-            return gr.update(visible=True), gr.update(visible=False)
-        else:
-            return gr.update(visible=False), gr.update(visible=True)
-
-    def classify_with_loading():
-        return gr.update(value="⏳ Please wait... processing...", visible=True)
-
-    def classify_dynamic(single_accession, file, text, mode):
-        print(f"MODE: {mode} | RAW TEXT: {text}")
-        if mode == "Single Accession":
-            return classify_main(single_accession)
-        else:
-            return summarize_batch(file, text)
-
-    def classify_main(accession):
-        table, summary, labelAncient_Modern, explain_label = summarize_results(accession)
-        flag_output = f"### 🏺 Ancient/Modern Flag\n**{labelAncient_Modern}**\n\n_Explanation:_ {explain_label}"
-        return (
-            table,
-            summary,
-            flag_output,
-            gr.update(visible=True),
-            gr.update(visible=False)
-        )
-
-    def reset_fields():
-        return (
-            gr.update(value=""),  # single_accession
-            gr.update(value=""),  # raw_text
-            gr.update(value=None), # file_upload
-            gr.update(value="Single Accession"), # inputMode
-            gr.update(value=[], visible=True), # output_table
-            gr.update(value="", visible=True), # output_summary
-            gr.update(value="", visible=True), # output_flag
-            gr.update(visible=False), # status
-            gr.update(visible=False)  # results_group
-        )
-
-    inputMode.change(fn=toggle_input_mode, inputs=inputMode, outputs=[single_input_group, batch_input_group])
-    run_button.click(fn=classify_with_loading, inputs=[], outputs=status)
-    run_button.click(
-        fn=classify_dynamic,
-        inputs=[single_accession, file_upload, raw_text, inputMode],
-        outputs=[output_table, output_summary, output_flag, results_group, status]
-    )
-    reset_button.click(
-        fn=reset_fields,
-        inputs=[],
-        outputs=[
-            single_accession, raw_text, file_upload, inputMode,
-            output_table, output_summary, output_flag,
-            status, results_group
-        ]
-    )
-
-    download_button.click(
-        save_batch_output, [output_table, output_summary, output_flag, output_type], download_file
-    )
-    submit_feedback.click(
-        fn=store_feedback_to_google_sheets, inputs=[single_accession, q1, q2, contact], outputs=feedback_status
-    )
-
-interface.launch(share=True)
->>>>>>> 597aa7c (WIP: Save local changes which mainly updated appUI  before moving to UpdateAppUI)