🚀 Initial upload of Streamlit app

Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,160 @@
+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
+
+# 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}")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+streamlit==1.43.2
+PyMuPDF==1.26.4
+langdetect==1.0.9
+googletrans==4.0.0-rc1
+transformers==4.46.3
+joblib==1.4.2
+torch==2.8.0