Update app.py

app.py

@@ -1,3 +1,5 @@
+# 🔄 COMBINED STREAMLIT PROTEIN ANALYSIS TOOL WITH COLORED COMPARISON
+
 import os
 os.system("pip install streamlit pandas xlsxwriter openpyxl pymongo")
 
@@ -8,7 +10,7 @@ from io import BytesIO
 from collections import defaultdict
 import hashlib
 
-# Optional for Repeats Functionality
+# MongoDB Setup
 try:
     from pymongo import MongoClient
     client = MongoClient("mongodb+srv://dhruvmangroliya:Eussmh5MbCBIkLJ6@cluster0.rrnbxfw.mongodb.net/BTP_DB?retryWrites=true&w=majority")
@@ -17,169 +19,167 @@ try:
 except:
     results_collection = None
 
-st.set_page_config(page_title="Protein Tool", layout="wide")
-st.title("🧬 Protein Analysis Toolkit")
-
-app_choice = st.radio("Choose an option", ["🔁 Protein Repeat Finder", "📊 Protein Comparator"])
-
-# ------------------- REPEATS FUNCTIONALITY -------------------
-if app_choice == "🔁 Protein Repeat Finder":
-    def is_homo_repeat(s):
-        return all(c == s[0] for c in s)
-
-    def hash_sequence(sequence):
-        return hashlib.md5(sequence.encode()).hexdigest()
-
-    @st.cache_data(show_spinner=False)
-    def fragment_protein_sequence(sequence, max_length=1000):
-        return [sequence[i:i+max_length] for i in range(0, len(sequence), max_length)]
-
-    def find_homorepeats(protein):
-        n = len(protein)
-        freq = defaultdict(int)
-        i = 0
-        while i < n:
-            curr = protein[i]
-            repeat = ""
-            while i < n and curr == protein[i]:
-                repeat += protein[i]
-                i += 1
-            if len(repeat) > 1:
-                freq[repeat] += 1
-        return freq
-
-    def find_hetero_amino_acid_repeats(sequence):
-        repeat_counts = defaultdict(int)
-        for length in range(2, len(sequence) + 1):
-            for i in range(len(sequence) - length + 1):
-                substring = sequence[i:i+length]
-                repeat_counts[substring] += 1
-        return {k: v for k, v in repeat_counts.items() if v > 1}
-
-    def check_boundary_repeats(fragments, final_repeats, overlap=50):
-        for i in range(len(fragments) - 1):
-            left_overlap = fragments[i][-overlap:]
-            right_overlap = fragments[i + 1][:overlap]
-            overlap_region = left_overlap + right_overlap
-            boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
-            for substring, count in boundary_repeats.items():
-                if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
-                    final_repeats[substring] += count
-        return final_repeats
-
-    def find_new_boundary_repeats(fragments, final_repeats, overlap=50):
-        new_repeats = defaultdict(int)
-        for i in range(len(fragments) - 1):
-            left_overlap = fragments[i][-overlap:]
-            right_overlap = fragments[i + 1][:overlap]
-            overlap_region = left_overlap + right_overlap
-            boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
-            for substring, count in boundary_repeats.items():
-                if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
-                    if substring not in final_repeats:
-                        new_repeats[substring] += count
-        return new_repeats
-
-    def get_or_process_sequence(sequence, analysis_type, overlap=50):
-        if results_collection is None:
-            return {}
-
-        hash_input = f"{sequence}_{analysis_type}"
-        sequence_hash = hash_sequence(hash_input)
-        cached = results_collection.find_one({"_id": sequence_hash})
-        if cached:
-            return cached["repeats"]
-
-        fragments = fragment_protein_sequence(sequence)
-        final_repeats = defaultdict(int)
-
-        if analysis_type == "Hetero":
-            for fragment in fragments:
-                fragment_repeats = find_hetero_amino_acid_repeats(fragment)
-                for k, v in fragment_repeats.items():
-                    final_repeats[k] += v
-            final_repeats = check_boundary_repeats(fragments, final_repeats, overlap)
-            new_repeats = find_new_boundary_repeats(fragments, final_repeats, overlap)
-            for k, v in new_repeats.items():
+# Utility Functions
+def is_homo_repeat(s):
+    return all(c == s[0] for c in s)
+
+def hash_sequence(sequence):
+    return hashlib.md5(sequence.encode()).hexdigest()
+
+@st.cache_data(show_spinner=False)
+def fragment_protein_sequence(sequence, max_length=1000):
+    return [sequence[i:i+max_length] for i in range(0, len(sequence), max_length)]
+
+def find_homorepeats(protein):
+    n = len(protein)
+    freq = defaultdict(int)
+    i = 0
+    while i < n:
+        curr = protein[i]
+        repeat = ""
+        while i < n and curr == protein[i]:
+            repeat += protein[i]
+            i += 1
+        if len(repeat) > 1:
+            freq[repeat] += 1
+    return freq
+
+def find_hetero_amino_acid_repeats(sequence):
+    repeat_counts = defaultdict(int)
+    for length in range(2, len(sequence) + 1):
+        for i in range(len(sequence) - length + 1):
+            substring = sequence[i:i+length]
+            repeat_counts[substring] += 1
+    return {k: v for k, v in repeat_counts.items() if v > 1}
+
+def check_boundary_repeats(fragments, final_repeats, overlap=50):
+    for i in range(len(fragments) - 1):
+        left_overlap = fragments[i][-overlap:]
+        right_overlap = fragments[i + 1][:overlap]
+        overlap_region = left_overlap + right_overlap
+        boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
+        for substring, count in boundary_repeats.items():
+            if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
+                final_repeats[substring] += count
+    return final_repeats
+
+def find_new_boundary_repeats(fragments, final_repeats, overlap=50):
+    new_repeats = defaultdict(int)
+    for i in range(len(fragments) - 1):
+        left_overlap = fragments[i][-overlap:]
+        right_overlap = fragments[i + 1][:overlap]
+        overlap_region = left_overlap + right_overlap
+        boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
+        for substring, count in boundary_repeats.items():
+            if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
+                if substring not in final_repeats:
+                    new_repeats[substring] += count
+    return new_repeats
+
+def get_or_process_sequence(sequence, analysis_type, overlap=50):
+    if results_collection is None:
+        return {}
+    hash_input = f"{sequence}_{analysis_type}"
+    sequence_hash = hash_sequence(hash_input)
+    cached = results_collection.find_one({"_id": sequence_hash})
+    if cached:
+        return cached["repeats"]
+
+    fragments = fragment_protein_sequence(sequence)
+    final_repeats = defaultdict(int)
+
+    if analysis_type == "Hetero":
+        for fragment in fragments:
+            fragment_repeats = find_hetero_amino_acid_repeats(fragment)
+            for k, v in fragment_repeats.items():
                 final_repeats[k] += v
-            final_repeats = {k: v for k, v in final_repeats.items() if not is_homo_repeat(k)}
-
-        elif analysis_type == "Homo":
-            final_repeats = find_homorepeats(sequence)
-
-        elif analysis_type == "Both":
-            hetero_repeats = defaultdict(int)
-            for fragment in fragments:
-                fragment_repeats = find_hetero_amino_acid_repeats(fragment)
-                for k, v in fragment_repeats.items():
-                    hetero_repeats[k] += v
-            hetero_repeats = check_boundary_repeats(fragments, hetero_repeats, overlap)
-            new_repeats = find_new_boundary_repeats(fragments, hetero_repeats, overlap)
-            for k, v in new_repeats.items():
+        final_repeats = check_boundary_repeats(fragments, final_repeats, overlap)
+        new_repeats = find_new_boundary_repeats(fragments, final_repeats, overlap)
+        for k, v in new_repeats.items():
+            final_repeats[k] += v
+        final_repeats = {k: v for k, v in final_repeats.items() if not is_homo_repeat(k)}
+
+    elif analysis_type == "Homo":
+        final_repeats = find_homorepeats(sequence)
+
+    elif analysis_type == "Both":
+        hetero_repeats = defaultdict(int)
+        for fragment in fragments:
+            fragment_repeats = find_hetero_amino_acid_repeats(fragment)
+            for k, v in fragment_repeats.items():
                 hetero_repeats[k] += v
-            hetero_repeats = {k: v for k, v in hetero_repeats.items() if not is_homo_repeat(k)}
-
-            homo_repeats = find_homorepeats(sequence)
-            final_repeats = homo_repeats.copy()
-            for k, v in hetero_repeats.items():
-                final_repeats[k] += v
-
-        results_collection.insert_one({
-            "_id": sequence_hash,
-            "sequence": sequence,
-            "analysis_type": analysis_type,
-            "repeats": dict(final_repeats)
-        })
-
-        return final_repeats
-
-    def process_excel(excel_data, analysis_type):
-        repeats = set()
-        sequence_data = []
-        count = 0
-        for sheet_name in excel_data.sheet_names:
-            df = excel_data.parse(sheet_name)
-            if len(df.columns) < 3:
-                st.error(f"Error: Sheet '{sheet_name}' must have at least 3 columns: ID, Name, Sequence.")
-                return None, None
-            for _, row in df.iterrows():
-                entry_id = str(row[0])
-                protein_name = str(row[1])
-                sequence = str(row[2]).replace('"', '').replace(' ', '').strip()
-                if not sequence:
-                    continue
-                count += 1
-                freq = get_or_process_sequence(sequence, analysis_type)
-                sequence_data.append((entry_id, protein_name, freq))
-                repeats.update(freq.keys())
-        st.toast(f"{count} sequences processed.")
-        return repeats, sequence_data
-
-    def create_excel(sequences_data, repeats, filenames):
-        output = BytesIO()
-        workbook = xlsxwriter.Workbook(output, {'in_memory': True})
-        for file_index, file_data in enumerate(sequences_data):
-            filename = filenames[file_index]
-            worksheet = workbook.add_worksheet(filename[:31])
-            worksheet.write(0, 0, "Entry")
-            worksheet.write(0, 1, "Protein Name")
+        hetero_repeats = check_boundary_repeats(fragments, hetero_repeats, overlap)
+        new_repeats = find_new_boundary_repeats(fragments, hetero_repeats, overlap)
+        for k, v in new_repeats.items():
+            hetero_repeats[k] += v
+        hetero_repeats = {k: v for k, v in hetero_repeats.items() if not is_homo_repeat(k)}
+        homo_repeats = find_homorepeats(sequence)
+        final_repeats = homo_repeats.copy()
+        for k, v in hetero_repeats.items():
+            final_repeats[k] += v
+
+    results_collection.insert_one({
+        "_id": sequence_hash,
+        "sequence": sequence,
+        "analysis_type": analysis_type,
+        "repeats": dict(final_repeats)
+    })
+    return final_repeats
+
+def process_excel(excel_data, analysis_type):
+    repeats = set()
+    sequence_data = []
+    count = 0
+    for sheet_name in excel_data.sheet_names:
+        df = excel_data.parse(sheet_name)
+        if len(df.columns) < 3:
+            st.error(f"Error: The sheet '{sheet_name}' must have at least three columns: ID, Protein Name, Sequence")
+            return None, None
+        for _, row in df.iterrows():
+            entry_id = str(row[0])
+            protein_name = str(row[1])
+            sequence = str(row[2]).replace('"', '').replace(' ', '').strip()
+            if not sequence:
+                continue
+            count += 1
+            freq = get_or_process_sequence(sequence, analysis_type)
+            sequence_data.append((entry_id, protein_name, freq))
+            repeats.update(freq.keys())
+    st.toast(f"{count} sequences processed.")
+    return repeats, sequence_data
+
+def create_excel(sequences_data, repeats, filenames):
+    output = BytesIO()
+    workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+    for file_index, file_data in enumerate(sequences_data):
+        filename = filenames[file_index]
+        worksheet = workbook.add_worksheet(filename[:31])
+        worksheet.write(0, 0, "Entry")
+        worksheet.write(0, 1, "Protein Name")
+        col = 2
+        for repeat in sorted(repeats):
+            worksheet.write(0, col, repeat)
+            col += 1
+        row = 1
+        for entry_id, protein_name, freq in file_data:
+            worksheet.write(row, 0, entry_id)
+            worksheet.write(row, 1, protein_name)
             col = 2
             for repeat in sorted(repeats):
-                worksheet.write(0, col, repeat)
+                worksheet.write(row, col, freq.get(repeat, 0))
                 col += 1
-            row = 1
-            for entry_id, protein_name, freq in file_data:
-                worksheet.write(row, 0, entry_id)
-                worksheet.write(row, 1, protein_name)
-                col = 2
-                for repeat in sorted(repeats):
-                    worksheet.write(row, col, freq.get(repeat, 0))
-                    col += 1
-                row += 1
-        workbook.close()
-        output.seek(0)
-        return output
+            row += 1
+    workbook.close()
+    output.seek(0)
+    return output
 
+# Streamlit UI
+st.set_page_config(page_title="Protein Tool", layout="wide")
+st.title("🧬 Protein Analysis Toolkit")
+
+app_choice = st.radio("Choose an option", ["🔁 Protein Repeat Finder", "📊 Protein Comparator"])
+
+if app_choice == "🔁 Protein Repeat Finder":
     analysis_type = st.radio("Select analysis type:", ["Homo", "Hetero", "Both"], index=2)
     uploaded_files = st.file_uploader("Upload Excel files", accept_multiple_files=True, type=["xlsx"])
 
@@ -227,101 +227,67 @@ if app_choice == "🔁 Protein Repeat Finder":
         result_df = pd.DataFrame(rows)
         st.dataframe(result_df)
 
-# ------------------- COMPARATOR FUNCTIONALITY -------------------
-# ------------------- COMPARATOR FUNCTIONALITY -------------------
 elif app_choice == "📊 Protein Comparator":
-    # st.set_page_config(page_title="Protein Repeat Comparator", layout="centered")
-    st.title("🧬 Protein Repeat Comparator")
-    st.write("Upload two Excel files with protein data. Frequency values should start from the first row (header).")
-
-    uploaded_file1 = st.file_uploader("Upload First Excel File", type=["xlsx"], key="comp1")
-    uploaded_file2 = st.file_uploader("Upload Second Excel File", type=["xlsx"], key="comp2")
-
-    if uploaded_file1 and uploaded_file2:
-        try:
-            df1 = pd.read_excel(uploaded_file1, header=0)
-            df2 = pd.read_excel(uploaded_file2, header=0)
-
-            df1.columns = df1.columns.astype(str)
-            df2.columns = df2.columns.astype(str)
-
-            id_col = df1.columns[0]
-            name_col = df1.columns[1]
-            repeat_columns = df1.columns[2:]
-
-            differences = []
-
-            for _, row1 in df1.iterrows():
-                entry_id = row1[id_col]
-                protein_name = row1[name_col]
-
-                row2_match = df2[(df2[id_col] == entry_id) & (df2[name_col] == protein_name)]
-                if row2_match.empty:
-                    continue
-
-                row2 = row2_match.iloc[0]
-
-                for repeat_col in repeat_columns:
-                    freq1 = row1[repeat_col]
-                    freq2 = row2[repeat_col]
-
-                    if pd.isna(freq1) or pd.isna(freq2):
-                        continue
-
-                    if freq1 != freq2:
-                        if freq1 == 0:
-                            pct_change = "Infinity"
-                        else:
-                            pct_change = ((freq2 - freq1) / freq1) * 100
-                            pct_change = round(pct_change, 2)
-
-                        diff = abs(freq1 - freq2)
-                        differences.append({
-                            id_col: entry_id,
-                            name_col: protein_name,
-                            "Repeat": repeat_col,
-                            "Frequency File 1": freq1,
-                            "Frequency File 2": freq2,
-                            "Difference": diff,
-                            "%age Change": pct_change
-                        })
-
-            if differences:
-                result_df = pd.DataFrame(differences)
-                result_df = result_df.sort_values(by="Difference", ascending=False)
-
-                # Show DataFrame in Streamlit app
-                st.subheader("🔍 View Changed Repeats")
-                st.dataframe(result_df, use_container_width=True)
-
-                # Apply styling
-                def color_pct(val):
-                    if isinstance(val, str) and val == "Infinity":
-                        return 'color: green'
-                    elif isinstance(val, (int, float)):
-                        if val > 0:
-                            return 'color: green'
-                        elif val < 0:
-                            return 'color: red'
-                    return ''
-
-                styled_df = result_df.style.applymap(color_pct, subset=["%age Change"])
-
-                # Save styled output
-                output = BytesIO()
-                with pd.ExcelWriter(output, engine='openpyxl') as writer:
-                    styled_df.to_excel(writer, index=False, sheet_name="Changed Repeats")
-                output.seek(0)
-
-                st.download_button(
-                    label="📥 Download Excel File",
-                    data=output,
-                    file_name="changed_repeats_with_percentage.xlsx",
-                    mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-                )
-            else:
-                st.info("No changes in repeat frequencies were found.")
-
-        except Exception as e:
-            st.error(f"⚠ Error: {e}")
+    st.write("Upload two Excel files with protein data to compare repeat frequencies.")
+
+    file1 = st.file_uploader("Upload First Excel File", type=["xlsx"], key="comp1")
+    file2 = st.file_uploader("Upload Second Excel File", type=["xlsx"], key="comp2")
+
+    if file1 and file2:
+        df1 = pd.read_excel(file1)
+        df2 = pd.read_excel(file2)
+
+        df1.columns = df1.columns.astype(str)
+        df2.columns = df2.columns.astype(str)
+
+        id_col = df1.columns[0]
+        name_col = df1.columns[1]
+        repeat_columns = df1.columns[2:]
+
+        diff_data = []
+        for i in range(min(len(df1), len(df2))):
+            row1 = df1.iloc[i]
+            row2 = df2.iloc[i]
+            diff_row = {"Entry": row1[id_col], "Protein Name": row1[name_col]}
+            for repeat in repeat_columns:
+                val1 = row1.get(repeat, 0)
+                val2 = row2.get(repeat, 0)
+                change = ((val2 - val1) / val1 * 100) if val1 != 0 else (100 if val2 > 0 else 0)
+                diff_row[repeat] = change
+            diff_data.append(diff_row)
+
+        result_df = pd.DataFrame(diff_data)
+        st.dataframe(result_df.style.format("{:.2f}%"))
+
+        def to_excel_with_colors(df):
+            output = BytesIO()
+            workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+            worksheet = workbook.add_worksheet('Comparison')
+
+            green_format = workbook.add_format({'font_color': 'green'})
+            red_format = workbook.add_format({'font_color': 'red'})
+            header_format = workbook.add_format({'bold': True, 'bg_color': '#D7E4BC'})
+
+            for col_num, col_name in enumerate(df.columns):
+                worksheet.write(0, col_num, col_name, header_format)
+
+            for row_num, row in enumerate(df.itertuples(index=False), start=1):
+                for col_num, value in enumerate(row):
+                    if col_num < 2:
+                        worksheet.write(row_num, col_num, value)
+                    else:
+                        fmt = green_format if value > 0 else red_format if value < 0 else None
+                        worksheet.write(row_num, col_num, f"{value:.2f}%", fmt)
+
+            workbook.close()
+            output.seek(0)
+            return output
+
+        excel_file = to_excel_with_colors(result_df)
 
+        st.download_button(
+            label="Download Colored Comparison Excel",
+            data=excel_file,
+            file_name="comparison_result_colored.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )