Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import fitz  # PyMuPDF
+import re
+import json
+
+def extract_pdf_text(pdf_path):
+    """Extract text from a PDF file."""
+    with fitz.open(pdf_path) as pdf_document:
+        content_text = ""
+        for page_num in range(len(pdf_document)):
+            page = pdf_document.load_page(page_num)
+            content_text += page.get_text() + "\n"
+    return content_text
+
+# Streamlit Application
+st.title("PDF Data Extractor")
+
+uploaded_file = st.file_uploader("Upload a PDF File", type="pdf")
+
+if uploaded_file is not None:
+    with open("temp.pdf", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    pdf_text = extract_pdf_text("temp.pdf")
+
+    # Step 2: Extract relevant information from the text using regex
+    pattern = r"2\s*3\s*4\s*5\s*\n-1,5\s*0([\s\S]*?)\n\nTrainer & Berater-Feedback"
+    matches = re.findall(pattern, pdf_text)
+
+    json_chunks = []
+    for match in matches:
+        match = match.replace(",", ".")
+        values = [value.strip() for value in match.split("\n") if value.strip()]
+        if len(values) == 22:
+            json_chunks.append({"current": values})
+        else:
+            current = values[1::2]
+            json_chunks.append({"current": current})
+
+    json_output = json.dumps(json_chunks, indent=2)
+    json_data = json.loads(json_output)
+
+    # Define the original data structure
+    original_data = {
+        'Title': [
+            "Personal Competence", "Personal Competence", "Personal Competence", "Personal Competence", "Personal Competence", "Personal Competence",
+            "Personal Competence", "Personal Competence", "Personal Competence", "Personal Competence", "Personal Competence",
+            "Personal Competence", "Personal Competence", "Business Competence", "Business Competence", "Business Competence", "Business Competence",
+            "Business Competence", "Management Competence", "Management Competence", "Management Competence", "Management Competence",
+        ],
+        'Code': ["P1", "P2", "P3", "P4", "P5", "P6", "P7", "P8", "P9", "P10", "P11", "P12",
+                 "P13", "B1", "B2", "B3", "B4", "B5", "M1", "M2", "M3", "M4"],
+        'Dimensions': [
+            "Integrity/ Reliability", "Appearance", "Enthusiasm/Passion", "Learning Motivation/ Self-Development", "Ability to Adapt/Flexibility",
+            "Communication/Information", "Cooperation/ Team spirit", "Handling of Complex Situations", "Coolness/Handling of Unclear Situations",
+            "Self-reliance/Initiative", "Conflict Management", "Ability to Assert Oneself/ Negotiation Skills", "Tact and Sensitivity",
+            "Quality Orientation", "Client Orientation", "Specialized Knowledge", "Methodology/ Didactics/ Language", "Creativity/ Conceptional Skills",
+            "Project Management", "Result Orientation", "Leadership Skills", "Coach and Advisor"
+        ]
+    }
+
+    df = pd.DataFrame(original_data)
+
+    # Add extracted scores to the DataFrame
+    score_columns = ['Boss_score', 'Colleague_score', 'Colleague_other_score', 'Report_score', 'Customer_score']
+    for idx, col in enumerate(score_columns):
+        df[col] = json_data[idx]['current'] + [None] * (len(df) - len(json_data[idx]['current']))
+
+    score_pattern = r"\d{1,2},\d{2}"
+    code_pattern = r"[A-Z]\.[0-9]{1,2}"
+
+    all_scores = re.findall(score_pattern, pdf_text)
+    all_codes = re.findall(code_pattern, pdf_text)
+
+    scores = [float(score.replace(",", ".")) for score in all_scores]
+    codes = [code.strip() for code in all_codes]
+
+    if len(codes) >= 44:
+        codes = codes[22:44]
+    if len(scores) >= 22:
+        scores = scores[0:22]
+
+    df1 = pd.DataFrame({'Code': [code.replace('.', '') for code in codes], 'All_raters_Score': scores})
+    df_combined = pd.merge(df, df1, on="Code", how="inner")
+
+    feature_cols = ['Boss_score', 'Colleague_score', 'Report_score', 'Customer_score', 'Colleague_other_score']
+    df_combined[feature_cols] = df_combined[feature_cols].astype(float)
+
+    def calculate_self_score(row):
+        valid_features = [val for val in row[feature_cols] if pd.notna(val)]
+        num_features = len(valid_features)
+        if num_features > 1:
+            sum_features = sum(valid_features) - row['All_raters_Score']
+            return (row['All_raters_Score'] * num_features) - sum_features
+        return np.nan
+
+    df_combined['Self_score'] = df_combined.apply(calculate_self_score, axis=1)
+
+    # Display the resultant DataFrame
+    st.write("### Extracted Dataset")
+    st.dataframe(df_combined)