Create app.py

app.py

@@ -0,0 +1,174 @@
+import streamlit as st
+import os
+import tempfile
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_core.prompts import PromptTemplate
+
+# GLOBAL UI & STYLING CONFIGURATION
+#===================================
+
+st.set_page_config(page_title="MindFlow AI", layout="wide" , page_icon="💡")
+
+# Injecting specialized CSS to enhance User Experience (UX)
+st.markdown("""
+    <style>
+    div.stButton > button:first-child {
+        background-color: #5DADE2; 
+        color: white;
+        transition: all 0.3s ease; 
+    }
+    div.stButton > button:first-child:hover {
+        background-color: #2E86C1; 
+        border-color: #2E86C1;
+        color: #FFFFFF;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Custom Branding Header: Using HTML/CSS for advanced typography and branding alignment
+st.markdown("""
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=Fredoka+One&family=Montserrat:wght@400;700&display=swap');
+    </style>
+    <div style='text-align: center; margin-bottom: 20px;'>
+        <h1 style='font-family: "Fredoka One", cursive; font-size: 60px; color: #5D6D7E; letter-spacing: 2px; margin-bottom: 0px;'>
+            MindFlow <span style='color: #85C1E9;'>AI</span> <br>
+            <span style='text-align: center; font-family: "Segoe UI";font-size: 30px; color: #666;'>Driven Assistant Summarization</span>
+        </h1>
+    </div>
+    """, unsafe_allow_html=True)
+
+# BACKEND & MODEL INITIALIZATION
+#====================================
+
+
+# Setting the GOOGLE_API_KEY
+os.environ["GOOGLE_API_KEY"] = "AIzaSyA0rDJF-58ffKHiuVvUpd5HHc4_1NfvrOM"
+
+# Initialize Google Gemini Model
+# Temperature 0.01 is utilized to minimize variance and ensure factual consistency 
+llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.01)
+
+
+# SIDEBAR: Audience Type Controls 
+with st.sidebar:
+    st.title("User Settings")
+    st.markdown("### **Target Audience:**")
+    audience_type = st.radio("", ["Beginner", "Expert"])
+    st.info(f"Targeting: {audience_type} level.")
+
+# Data Ingestion Layer 
+# Drag and drop a pdf OR paste a text manually   
+st.header("Input Source")
+tab1, tab2 = st.tabs(["📄 Upload PDF", "✍️ Paste Text"])
+
+full_text = ""
+
+# Handling PDF uploads using LangChain loaders and temporary disk storage
+with tab1:
+    uploaded_file = st.file_uploader("Upload PDF Document", type="pdf")
+    if uploaded_file:
+        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.getvalue())
+            tmp_path = tmp_file.name
+        
+        # Extracting semantic content from PDF
+        loader = PyPDFLoader(tmp_path)
+        pages = loader.load()
+        # Merging PDF pages
+        full_text = " ".join([page.page_content for page in pages])
+        # Ensuring local storage cleanup
+        os.remove(tmp_path) 
+
+# Handling direct text input
+with tab2:
+    manual_text = st.text_area("Paste your article or text here:", height=300)
+    if manual_text:
+        full_text = manual_text
+
+# PROCESSING PIPELINE: Summarization & Evaluation
+if st.button("Generate & Evaluate"):
+    if full_text.strip():
+        with st.spinner("Processing..."):
+            # SEMANTIC CHUNKING PHASE
+            # Recursive splitting ensures text segments stay within LLM context windows
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=300)
+            chunks = text_splitter.split_text(full_text)
+            text_to_process = " ".join(chunks[:2])
+
+            # TAILORED SUMMARIZATION PHASE 
+            #Utilizing a persona-driven Prompt Template for audience-specific output
+            summary_prompt = PromptTemplate.from_template("""
+                [STRICT AUDIT MODE: ZERO HALLUCINATION TOLERANCE]
+            You are an elite expert. Your ONLY source of truth is the provided text. 
+            CRITICAL RESTRICTION: 
+            If a concept (like 'Regularization', 'Overfitting', or 'Lasso') is NOT explicitly mentioned in the source text, 
+            you are FORBIDDEN from mentioning it, even if it is factually related to the topic. 
+            Failure to follow this will result in an inaccurate evaluation.
+            
+            Act as an elite educational consultant and technical expert. Your goal is to transform complex information into a high-quality summary perfectly tailored for a {audience} audience.
+
+            Target Persona:
+            - If Audience is "Beginner": You are a supportive teacher. Use simple analogies, avoid technical jargon unless explained, and focus on the "Big Picture" and "Why it matters." Use friendly, encouraging tone and clear bullet points.
+            - If Audience is "Expert": You are a senior researcher. Use precise academic terminology, focus on methodology, data results, and nuanced conclusions. Maintain high information density and professional tone.
+
+            Task Instructions:
+            1. Core Essence**: Extract the most critical information without losing the original context.
+            2. Structural Integrity: Organize the output with clear headers (e.g., "Overview", "Key Findings", "Implications").
+            3. Contextual Adaptation: 
+            - For Beginners: Include a "Simple Definition" section for complex terms.
+            - For Experts: Include a "Technical Highlights" section focusing on metrics or logic.
+            4. Faithfulness: Ensure ( 100% ) accuracy to the source text; do not hallucinate or add external information[cite: 56].
+            CONSTRAINTS:
+            - STRICT ADHERENCE: Do NOT include any information, concepts, or terms that are NOT present in the source text. 
+            - NO OUTSIDE KNOWLEDGE: Even if you know more about the topic, ignore it. 
+            - FORBIDDEN TOPICS: If the source text does not mention things like 'Regularization' or 'Overfitting', you MUST NOT mention them.
+            - AUDIENCE ADAPTATION: 
+                - If {audience} is Beginner: Explain ONLY the concepts in the text using simple analogies.
+                - If {audience} is Expert: Focus ONLY on the technical details provided in the text.
+            Source Text:
+            {text}
+            Final Output Requirements:
+            - Format: Professional Markdown.
+            - Language: Clear and Concise English[cite: 6].
+            - Accuracy: Maintain strict adherence to the facts provided in the document[cite: 56].
+            """)
+            
+            summary_chain = summary_prompt | llm
+            summary_output = summary_chain.invoke({"audience": audience_type, "text": text_to_process})
+            
+            st.subheader(f"📝 Summary for {audience_type}")
+            st.markdown(summary_output.content)
+            
+            st.divider()
+
+            # AUTOMATED AI-AS-A-JUDGE EVALUATION PHASE
+            # Implementing a secondary LLM chain to audit the quality of the generated summary
+            eval_prompt = PromptTemplate.from_template("""
+            As an AI Auditor, evaluate the summary against the source text.
+            Return a Markdown table with scores (1-5) and justifications.
+            
+            | Criterion | Score | Justification |
+            | :--- | :--- | :--- |
+            | Faithfulness | | |
+            | Coherence | | |
+            | Audience Alignment | | |
+            
+            Level: {level}
+            Source: {source}
+            Summary: {summary}
+            """)
+            
+            eval_chain = eval_prompt | llm
+            eval_output = eval_chain.invoke({
+                "level": audience_type, 
+                "source": text_to_process[:4000], 
+                "summary": summary_output.content
+            })
+
+            st.subheader("📊 Automated Quality Evaluation")
+            st.markdown(eval_output.content)
+    else:
+        st.warning("Please upload a PDF or paste some text first!")