Added Files

app.py

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+import streamlit as st
+from PIL import Image
+from pytesseract import pytesseract
+import PyPDF2
+import enum
+import os
+import re
+from collections import defaultdict
+import folium
+from streamlit_folium import st_folium
+from geopy.geocoders import Nominatim
+from geopy.exc import GeocoderTimedOut
+import wikipedia
+from transformers import pipeline
+from openai import OpenAI
+
+# NVIDIA OpenAI API Setup
+client = OpenAI(
+    base_url="https://integrate.api.nvidia.com/v1",
+    api_key="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"  # Replace with actual API key
+)
+
+# Load Named Entity Recognition (NER) Model
+nlp = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
+
+st.set_page_config(page_title="OCR & Historical Analysis", page_icon="📜", layout="wide")
+
+# Custom Styling
+def style_text(text):
+    return f"""
+    <div style='padding:10px;border-radius:10px;
+                background-color:#e0e0e0;
+                color:#333;
+                font-weight:500;
+                font-size:16px;'>
+        {text}
+    </div>
+    """
+
+def find_related_documents(query):
+    try:
+        search_results = wikipedia.search(query, results=5)
+        links = [wikipedia.page(result).url for result in search_results]
+        return links
+    except Exception as e:
+        return [f"Error retrieving related documents: {str(e)}"]
+
+def geocode_location(location):
+    geolocator = Nominatim(user_agent="streamlit_app")
+    try:
+        loc = geolocator.geocode(location, timeout=10)
+        return (loc.latitude, loc.longitude) if loc else None
+    except GeocoderTimedOut:
+        return None
+
+def generate_historical_context_nvidia(text):
+    """Use NVIDIA OpenAI API to generate a structured, summarized historical context."""
+
+    prompt_analysis = f"""
+    Analyze the following text and provide a historical context. Identify:
+    - Key historical events
+    - Significant figures involved
+    - The broader historical significance
+
+    Text: {text}
+
+    Provide a detailed response.
+    """
+
+    prompt_summary = """
+    Summarize the historical context provided above in a concise and structured format:
+    - Limit to 5 bullet points
+    - Each bullet point should be under 100 words
+    - Avoid unnecessary explanations or preamble—return only the summary
+    """
+
+    try:
+        # Step 1: Generate Detailed Historical Context
+        completion = client.chat.completions.create(
+            model="deepseek-ai/deepseek-r1",
+            messages=[
+                {"role": "system", "content": "You are a historian providing detailed historical insights."},
+                {"role": "user", "content": prompt_analysis}
+            ],
+            temperature=0.4,
+            top_p=0.9,
+            max_tokens=4096,
+            stream=False
+        )
+        detailed_response = completion.choices[0].message.content.strip()
+
+        # Step 2: Summarize the Historical Context **without Monologue**
+        summary_completion = client.chat.completions.create(
+            model="deepseek-ai/deepseek-r1",
+            messages=[
+                {"role": "system", "content": "You are an expert summarizer."},
+                {"role": "user", "content": f"{detailed_response}\n\n{prompt_summary}"}
+            ],
+            temperature=0.4,
+            top_p=0.9,
+            max_tokens=2048,
+            stream=False
+        )
+        
+        # Extract only the structured summary
+        summary_response = summary_completion.choices[0].message.content.strip()
+        
+        # Remove AI-generated explanations or redundant preamble
+        clean_summary = re.sub(r"^.*?\n\n", "", summary_response, flags=re.DOTALL)
+
+        return clean_summary if clean_summary else "No historical context found."
+
+    except Exception as e:
+        return f"Error retrieving AI-generated historical context: {str(e)}"
+
+    
+class OS(enum.Enum):
+    Mac = 0
+    Windows = 1
+
+class Languages(enum.Enum):
+    English = "eng"
+    Filipino = "fil"
+    Spanish = "spa"
+
+class ImageReader:
+    def __init__(self, os):
+        if os == OS.Windows:
+            pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract.exe'
+
+    def extract_text(self, image: Image, lang: Languages):
+        extracted_text = pytesseract.image_to_string(image, lang=lang.value)
+        return ' '.join(extracted_text.split())
+
+    def extract_text_from_pdf(self, pdf_file, lang: Languages):
+        pdf_reader = PyPDF2.PdfReader(pdf_file)
+        text = "".join(page.extract_text() or "" for page in pdf_reader.pages)
+        return text
+
+    def extract_key_details(self, text):
+        details = {"dates": set(), "names": set(), "locations": set()}
+        date_pattern = r'\b(?:\d{1,2}[/\-]\d{1,2}[/\-]\d{2,4}|\d{4})\b'
+        details['dates'] = set(re.findall(date_pattern, text))
+        entities = nlp(text)
+        
+        for entity in entities:
+            if "PER" in entity['entity']:
+                details['names'].add(entity['word'])
+            elif "LOC" in entity['entity']:
+                details['locations'].add(entity['word'])
+        
+        return details
+
+# UI Layout
+st.title("📜 OCR & Historical Context Analyzer")
+st.markdown("Extract text from images and PDFs, analyze named entities, and retrieve historical context.")
+
+col1, col2 = st.columns([1, 2])
+
+with col1:
+    selected_os = st.selectbox("🖥️ Select your OS", [OS.Windows, OS.Mac], format_func=lambda x: x.name)
+    selected_lang = st.selectbox("🌍 Select language", list(Languages), format_func=lambda x: x.name)
+    uploaded_file = st.file_uploader("📂 Upload an image or PDF", type=["png", "jpg", "jpeg", "pdf"])
+
+if uploaded_file:
+    ir = ImageReader(selected_os)
+    extracted_text = ""
+    if uploaded_file.type in ["image/png", "image/jpeg"]:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        extracted_text = ir.extract_text(image, selected_lang)
+    else:
+        extracted_text = ir.extract_text_from_pdf(uploaded_file, selected_lang)
+
+    st.markdown("### 📝 Extracted Text:")
+    st.markdown(style_text(extracted_text), unsafe_allow_html=True)
+
+    key_details = ir.extract_key_details(extracted_text)
+    st.markdown("### 🔍 Extracted Key Details")
+    st.write(f"**📅 Dates:** {', '.join(key_details['dates']) if key_details['dates'] else 'None found'}")
+    st.write(f"**👤 Names:** {', '.join(key_details['names']) if key_details['names'] else 'None found'}")
+    st.write(f"**📍 Locations:** {', '.join(key_details['locations']) if key_details['locations'] else 'None found'}")
+    
+    combined_terms = ' '.join(key_details['dates'].union(key_details['locations']).union(key_details['names']))
+    historical_context = generate_historical_context_nvidia(combined_terms)
+    st.markdown("### 🏛️ Historical Context")
+    st.markdown(style_text(historical_context), unsafe_allow_html=True)
+    
+    st.markdown("### 🌐 Search the Web")
+    search_query = st.text_input("Enter a keyword or phrase:")
+    if search_query:
+        search_results = generate_historical_context_nvidia(search_query)
+        st.markdown(style_text(search_results), unsafe_allow_html=True)
+    
+    related_docs = find_related_documents(combined_terms)
+    st.markdown("### 📚 Related Historical Documents")
+    for link in related_docs:
+        st.markdown(f"[🔗 {link}]({link})")
+    
+    st.markdown("### 🗺️ Map of Key Locations")
+    map_center = [10.0, 10.0]
+    map_obj = folium.Map(location=map_center, zoom_start=2)
+    
+    for loc in key_details['locations']:
+        coords = geocode_location(loc)
+        if coords:
+            folium.Marker(coords, popup=loc).add_to(map_obj)
+    
+    st_folium(map_obj, width=700, height=500)

requirements.txt

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+streamlit
+pillow
+pytesseract
+pypdf2
+transformers
+openai
+wikipedia-api
+geopy
+folium
+streamlit-folium

setup.sh

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+#!/bin/bash
+apt-get update && apt-get install -y tesseract-ocr libtesseract-dev