Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,160 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+import json
+import re
+import fitz  # PyMuPDF
+from langdetect import detect, DetectorFactory
+from googletrans import Translator
+from transformers import pipeline
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# for model serialization
+import joblib
+
+# for creating a folder
+import os
+
+# for hugging face space authentication to upload files
+from huggingface_hub import login, HfApi
+
+DetectorFactory.seed = 0
+
+# Initialize Translator & Summarizer
+# -------------------------
+# Note: Initializing models here will load them when the app starts.
+# Consider caching or lazy loading for performance in production.
+translator = Translator()
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+# -------------------------
+# Extract text from PDF
+# -------------------------
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    # Save the uploaded file temporarily to process it with fitz
+    temp_file_path = "temp.pdf"
+    with open(temp_file_path, "wb") as f:
+        f.write(pdf_file.getvalue())
+
+    try:
+        with fitz.open(temp_file_path) as doc:
+            for page in doc:
+                text += page.get_text("text")
+    finally:
+        # Ensure the temporary file is removed
+        if os.path.exists(temp_file_path):
+            os.remove(temp_file_path)
+
+    return text.strip()
+
+# Translate text to English using Google Translate
+# -------------------------
+def translate_text_google(text):
+    if not text:
+        return ""
+
+    max_chunk = 5000  # Google Translate handles large text but splitting is safer
+    chunks = [text[i:i+max_chunk] for i in range(0, len(text), max_chunk)]
+    translations = []
+    for chunk in chunks:
+        translated = translator.translate(chunk, dest='en')
+        translations.append(translated.text)
+    return " ".join(translations)
+
+# Summarize text safely
+# -------------------------
+def safe_summarize(text, max_length=150, min_length=30):
+    if not text or len(text.split()) < 10:
+        return text  # too short to summarize
+    try:
+        summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        st.warning(f"⚠️ Summarization failed: {e}")
+        return text
+
+# Extract entities
+# -------------------------
+def extract_entities(text):
+    entities = {}
+
+    # PAN format: AAAAA9999A
+    pan_match = re.search(r"\b[A-Z]{5}\d{4}[A-Z]\b", text, re.IGNORECASE)
+
+    # Account Number
+    acc_match = re.search(r"account\s*number\s*[:\-]?\s*([A-Za-z0-9]+)", text, re.IGNORECASE)
+
+    # Penalty (accepts 'penalty' or 'penalties')
+    penalty_match = re.search(r"\bpenalt(?:y|ies)\s*[:\-]?\s*([\d,]+)", text, re.IGNORECASE)
+
+    # Deactivation keywords
+    deactivate_match = re.search(r"\bdeactivat(?:e|ed|ion)\b", text, re.IGNORECASE)
+
+    if pan_match:
+        entities["PAN"] = pan_match.group(0).upper()
+    if acc_match:
+        entities["Account_Number"] = acc_match.group(1)
+    if penalty_match:
+        entities["Penalty"] = penalty_match.group(1).replace(",", "")
+    if deactivate_match:
+        entities["Deactivate"] = deactivate_match.group(0).lower()
+
+    return entities
+
+ # Trigger actions
+# -------------------------
+def trigger_action(entities):
+    if "Penalty" in entities:
+        return f"Penalty of {entities['Penalty']} recorded for account {entities.get('Account_Number', 'N/A')} (PAN: {entities.get('PAN', 'N/A')})"
+    elif "Deactivate" in entities:
+        return f"Kindy Deactivate {entities.get('Account_Number', 'N/A')} as per request having (PAN: {entities.get('PAN', 'N/A')})"
+    elif "Account_Number" in entities:
+        return f"Account {entities['Account_Number']} flagged for review."
+    else:
+        return "No action required"
+
+# Process single PDF - adapted for Streamlit FileUploader
+# -------------------------
+def process_uploaded_pdf(pdf_file):
+    raw_text = extract_text_from_pdf(pdf_file)
+    lang = detect(raw_text)
+    translated_text = translate_text_google(raw_text) if lang != "en" else raw_text
+    summary = safe_summarize(translated_text)
+    entities = extract_entities(translated_text)
+    action_result = trigger_action(entities)
+
+    result = {
+        "file_name": pdf_file.name,
+        "detected_language": lang,
+        "raw_text_snippet": raw_text[:500] + ("..." if len(raw_text) > 500 else ""),
+        "translated_text_snippet": translated_text[:500] + ("..." if len(translated_text) > 500 else ""),
+        "summary": summary,
+        "entities": entities,
+        "action_triggered": action_result
+    }
+
+    return result
+
+
+st.title("PDF Document Processor")
+st.write("Upload a PDF file to extract text, translate (if needed), summarize, identify key entities, and suggest actions.")
+
+uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+if uploaded_file is not None:
+    st.write("Processing PDF...")
+    try:
+        processed_data = process_uploaded_pdf(uploaded_file)
+
+        st.subheader("Processing Results:")
+        st.write(f"**File Name:** {processed_data['file_name']}")
+        st.write(f"**Detected Language:** {processed_data['detected_language']}")
+        st.write(f"**Raw Text Snippet:** {processed_data['raw_text_snippet']}")
+        st.write(f"**Translated Text Snippet:** {processed_data['translated_text_snippet']}")
+        st.write(f"**Summary:** {processed_data['summary']}")
+        st.write(f"**Extracted Entities:**")
+        for key, value in processed_data['entities'].items():
+            st.write(f"- {key}: {value}")
+        st.write(f"**Action Triggered:** {processed_data['action_triggered']}")
+
+    except Exception as e:
+        st.error(f"An error occurred during processing: {e}")