Create app.py

app.py

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+import streamlit as st
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from pydantic import BaseModel, Field
+from typing import List
+from dotenv import load_dotenv
+import os
+import time
+from datetime import datetime
+import PyPDF2
+from fpdf import FPDF
+from docx import Document
+import io
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+ 
+load_dotenv()
+api_key = os.getenv("GOOGLE_API_KEY")
+ 
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=api_key)
+ 
+class KeyPoint(BaseModel):
+    point: str = Field(description="A key point extracted from the document.")
+
+class Summary(BaseModel):
+    summary: str = Field(description="A brief summary of the document content.")
+
+class DocumentAnalysis(BaseModel):
+    key_points: List[KeyPoint] = Field(description="List of key points from the document.")
+    summary: Summary = Field(description="Summary of the document.")
+ 
+parser = PydanticOutputParser(pydantic_object=DocumentAnalysis)
+
+prompt_template = """
+Analyze the following text and extract key points and a summary.
+{format_instructions}
+Text: {text}
+"""
+prompt = PromptTemplate(
+    template=prompt_template,
+    input_variables=["text"],
+    partial_variables={"format_instructions": parser.get_format_instructions()}
+)
+ 
+chain = LLMChain(llm=llm, prompt=prompt, output_parser=parser)
+ 
+def analyze_text_structured(text):
+    output = chain.run(text=text)
+    return output
+
+ 
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+ 
+def json_to_text(analysis):
+    text_output = "=== Summary ===\n" + f"{analysis.summary.summary}\n\n"
+    text_output += "=== Key Points ===\n"
+    for i, key_point in enumerate(analysis.key_points, start=1):
+        text_output += f"{i}. {key_point.point}\n"
+    return text_output
+ 
+def create_pdf_report(analysis):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font('Helvetica', '', 12)
+    pdf.cell(200, 10, txt="PDF Analysis Report", ln=True, align='C')
+    pdf.cell(200, 10, txt=f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align='C')
+    clean_text = json_to_text(analysis)
+    pdf.multi_cell(0, 10, txt=clean_text)
+    return pdf.output(dest='S')
+ 
+def create_word_report(analysis):
+    doc = Document()
+    doc.add_heading('PDF Analysis Report', 0)
+    doc.add_paragraph(f'Generated on: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
+    clean_text = json_to_text(analysis)
+    doc.add_heading('Analysis', level=1)
+    doc.add_paragraph(clean_text)
+    docx_bytes = io.BytesIO()
+    doc.save(docx_bytes)
+    docx_bytes.seek(0)
+    return docx_bytes.getvalue()
+ 
+st.set_page_config(page_title="Chat With PDF", page_icon="😒")
+
+ 
+def local_css():
+    st.markdown("""
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&family=Orbitron:wght@400;700&display=swap');
+        body {
+            font-family: 'Montserrat', sans-serif;
+            background: linear-gradient(135deg, #0A0A0A 0%, #1A1A1A 100%);
+            color: #FFFFFF;
+        }
+        h1, h2, h3 { font-family: 'Orbitron', sans-serif; }
+        .main-header {
+            position: fixed;
+            top: 0;
+            width: 100%;
+            text-align: center;
+            padding: 1.5rem;
+            background: rgba(0, 0, 0, 0.8);
+            border-bottom: 2px solid #00FFFF;
+            box-shadow: 0 0 15px #00FFFF;
+            animation: slideInLeft 0.5s ease-in;
+            z-index: 1000;
+        }
+        .flag-stripe {
+            height: 6px;
+            background: linear-gradient(90deg, #FF00FF 33%, #00FFFF 66%, #00FF00 100%);
+            animation: slideInLeft 0.5s ease-in;
+        }
+        .stTextInput > div > input {
+            border-radius: 20px;
+            padding: 0.8rem 2rem;
+            background: rgba(0, 0, 0, 0.7);
+            border: 2px solid #00FFFF;
+            color: #FFFFFF;
+            transition: all 0.3s ease;
+        }
+        .stTextInput > div > input:focus {
+            border-color: #FF00FF;
+            box-shadow: 0 0 15px #FF00FF;
+        }
+        .stButton > button {
+            border-radius: 20px;
+            padding: 0.6rem 1.5rem;
+            background: linear-gradient(135deg, #00FFFF, #FF00FF);
+            color: #000000;
+            border: none;
+            font-weight: bold;
+            text-transform: uppercase;
+            box-shadow: 0 0 10px #00FFFF;
+            transition: all 0.3s ease;
+        }
+        .stButton > button:hover {
+            transform: scale(1.05);
+            box-shadow: 0 0 20px #FF00FF;
+        }
+        .card {
+            background: rgba(255, 255, 255, 0.1);
+            backdrop-filter: blur(10px);
+            border-radius: 15px;
+            border: 1px solid rgba(0, 255, 255, 0.3);
+            padding: 1.5rem;
+            margin: 1rem 0;
+            transition: all 0.3s ease;
+        }
+        .card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 0 20px #FF00FF;
+        }
+        .footer {
+            position: fixed;
+            bottom: 0;
+            width: 100%;
+            background: rgba(0, 0, 0, 0.9);
+            padding: 1rem;
+            text-align: center;
+            border-top: 2px solid #00FFFF;
+            animation: fadeIn 0.5s ease-in;
+        }
+        @keyframes fadeIn { from { opacity: 0; } to { opacity: 1; } }
+        @keyframes slideInLeft { from { transform: translateX(-100%); } to { transform: translateX(0); } }
+    </style>
+    """, unsafe_allow_html=True)
+
+local_css()
+ 
+if "current_file" not in st.session_state:
+    st.session_state.current_file = None
+if "pdf_summary" not in st.session_state:
+    st.session_state.pdf_summary = None
+if "analysis_time" not in st.session_state:
+    st.session_state.analysis_time = 0
+if "pdf_report" not in st.session_state:
+    st.session_state.pdf_report = None
+if "word_report" not in st.session_state:
+    st.session_state.word_report = None
+if "vectorstore" not in st.session_state:
+    st.session_state.vectorstore = None
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+ 
+st.markdown('<div class="main-header">', unsafe_allow_html=True)
+st.markdown('<div class="flag-stripe"></div>', unsafe_allow_html=True)
+st.title("😒 Chat With PDF")
+st.caption("Your AI-powered  Document Analyzer")
+st.markdown('</div>', unsafe_allow_html=True)
+ 
+st.markdown('<div class="card animate-fadeIn">', unsafe_allow_html=True)
+uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+if uploaded_file is not None:
+    if st.session_state.current_file != uploaded_file.name:
+        st.session_state.current_file = uploaded_file.name
+        st.session_state.pdf_summary = None
+        st.session_state.pdf_report = None
+        st.session_state.word_report = None
+        if "vectorstore" in st.session_state:
+            del st.session_state.vectorstore
+        if "messages" in st.session_state:
+            st.session_state.messages = []
+    text = extract_text_from_pdf(uploaded_file)
+    if st.button("Analyze Text"):
+        start_time = time.time()
+        with st.spinner("Analyzing..."):
+            analysis = analyze_text_structured(text)
+            st.session_state.pdf_summary = analysis
+             
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+            chunks = text_splitter.split_text(text)
+            embeddings = HuggingFaceInferenceAPIEmbeddings(
+                pi_key=os.getenv("HUGGINGFACE_ACCESS_TOKEN"),
+                model_name="BAAI/bge-small-en-v1.5"
+            )
+            st.session_state.vectorstore = FAISS.from_texts(chunks, embeddings)
+           
+            st.session_state.pdf_report = create_pdf_report(analysis)
+            st.session_state.word_report = create_word_report(analysis)
+        end_time = time.time()
+        st.session_state.analysis_time = end_time - start_time
+        st.subheader("Analysis Results")
+        st.text(json_to_text(analysis))
+        st.download_button(
+            label="Download PDF Report",
+            data=st.session_state.pdf_report,
+            file_name="analysis_report.pdf",
+            mime="application/pdf"
+        )
+        st.download_button(
+            label="Download Word Report",
+            data=st.session_state.word_report,
+            file_name="analysis_report.docx",
+            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+        )
+st.markdown('</div>', unsafe_allow_html=True)
+ 
+if "vectorstore" in st.session_state:
+    st.subheader("Chat with the Document")
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    if prompt := st.chat_input("Ask a question about the document"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                
+                docs = st.session_state.vectorstore.similarity_search(prompt, k=3)
+                context = "\n".join([doc.page_content for doc in docs])
+                 
+                messages = [
+                    SystemMessage(content="You are a  assistant. Answer the question based on the provided document context."),
+                    HumanMessage(content=f"Context: {context}\n\nQuestion: {prompt}")
+                ]
+                
+                response = llm.invoke(messages)
+                st.markdown(response.content)
+        st.session_state.messages.append({"role": "assistant", "content": response.content})
+
+ 
+st.markdown(f'<div class="footer">Analysis Time: {st.session_state.analysis_time:.1f}s | Powered by Google Generative AI</div>', unsafe_allow_html=True)