Create app.py

app.py

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+import streamlit as st
+import os
+from PIL import Image
+import pytesseract
+from pdf2image import convert_from_path
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
+from langchain_groq import ChatGroq
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.vectorstores import VectorStoreRetriever
+import streamlit.components.v1 as components
+from streamlit_pdf_viewer import pdf_viewer
+from io import BytesIO
+import base64 
+
+if 'pdf_ref' not in st.session_state:
+    st.session_state.pdf_ref = None
+
+# Initialize the Groq API Key and the model
+os.environ["GROQ_API_KEY"] = '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'
+# config = {'max_new_tokens': 512, 'context_length': 8000}
+llm = ChatGroq(
+    model='llama3-70b-8192',
+    temperature=0.5,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2
+)
+
+# Define OCR functions for image and PDF files
+def ocr_image(image_path, language='eng+guj'):
+    img = Image.open(image_path)
+    text = pytesseract.image_to_string(img, lang=language)
+    return text
+
+def ocr_pdf(pdf_path, language='eng+guj'):
+    images = convert_from_path(pdf_path)
+    all_text = ""
+    for img in images:
+        text = pytesseract.image_to_string(img, lang=language)
+        all_text += text + "\n"
+    return all_text
+
+def ocr_file(file_path):
+    file_extension = os.path.splitext(file_path)[1].lower()
+
+    if file_extension == ".pdf":
+        text_re = ocr_pdf(file_path, language='guj+eng')
+    elif file_extension in [".jpg", ".jpeg", ".png", ".bmp"]:
+        text_re = ocr_image(file_path, language='guj+eng')
+    else:
+        raise ValueError("Unsupported file format. Supported formats are PDF, JPG, JPEG, PNG, BMP.")
+
+    return text_re
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Function to create or update the vector store
+def get_vector_store(text_chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    
+    # Ensure the directory exists before saving the vector store
+    os.makedirs("faiss_index", exist_ok=True)
+    vector_store.save_local("faiss_index")
+    
+    return vector_store
+
+# Function to process multiple files and extract vector store
+def process_ocr_and_pdf_files(file_paths):
+    raw_text = ""
+    for file_path in file_paths:
+        raw_text += ocr_file(file_path) + "\n"
+    text_chunks = get_text_chunks(raw_text)
+    return get_vector_store(text_chunks)
+
+# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
+# new_vector_store = FAISS.load_local(
+#     "faiss_index", embeddings, allow_dangerous_deserialization=True
+# )
+
+# docs = new_vector_store.similarity_search("qux")
+# Conversational chain for Q&A
+def get_conversational_chain():
+    template = """Core Identity & Responsibilities
+
+Role: Official AI Assistant for Admission Committee for Professional Courses (ACPC), Gujarat
+Mission: Process OCR-extracted text and provide clear, direct guidance on admissions and scholarships
+Focus: Deliver user-friendly responses while handling OCR complexities internally
+
+Processing Framework
+1. Text & Document Processing
+
+Process OCR-extracted text from various document types with attention to tables and structured data
+Internally identify and handle OCR errors without explicitly mentioning them unless critical
+Preserve tabular structures and relationships between data points
+Present information in clean, readable formats regardless of source OCR quality
+
+2. Language Handling
+
+Support seamless communication in both Gujarati and English
+Respond in the same language as the user's query
+Present technical terms in both languages when relevant
+Adjust language complexity to user comprehension level
+
+3. Response Principles
+
+Provide direct, concise answers (2-3 sentences for simple queries)
+Skip unnecessary OCR quality disclaimers unless information is critically ambiguous
+Present information in user-friendly formats, especially for tables and numerical data
+Maintain professional yet conversational tone
+
+Query Handling Strategies
+1. Direct Information Queries
+
+Provide straightforward answers without mentioning OCR processing
+Example:
+User: "What is the last date for application submission?"
+Response: "The last date for application submission is June 15, 2025."
+(NOT: "Based on the OCR-processed text, the last date appears to be...")
+
+2. Table Data Extraction
+
+Present tabular information in clean, structured format
+Preserve relationships between data points
+Example:
+User: "What are the fees for different courses?"
+Response:
+"The fees for various courses are:
+
+B.Tech: ₹1,15,000 (General), ₹58,000 (SC/ST)
+B.Pharm: ₹85,000 (General), ₹42,500 (SC/ST)"
+(NOT: "According to the OCR-extracted table, which may have quality issues...")
+
+
+
+3. Ambiguous Information Handling
+
+If OCR quality affects critical information (like dates, amounts, eligibility):
+
+Provide the most likely correct information
+Add a brief note suggesting verification only for critical information
+Example: "The application deadline is June 15, 2025. For this important deadline, we recommend confirming on the official ACPC website."
+
+
+
+4. Uncertain Information Protocol
+
+For critically unclear OCR content:
+
+State the most probable information
+Add a simple verification suggestion without mentioning OCR
+Example: "Based on the available information, the income limit appears to be ₹6,00,000. For this critical criterion, please verify on the official ACPC portal."
+
+
+
+5. Structured Document Navigation
+
+Present information in the same logical structure as the original document
+Use headings and bullet points for clarity when appropriate
+Maintain document hierarchies when explaining multi-step processes
+
+6. Out-of-Scope Queries
+
+Politely redirect without mentioning document or OCR limitations
+Example: "This query is outside the scope of ACPC admission guidelines. For information about [topic], please contact [appropriate authority]."
+
+7. Key Information Emphasis
+
+Highlight critical information like deadlines, eligibility criteria, and document requirements
+Make important numerical data visually distinct
+Prioritize accuracy for dates, amounts, and eligibility requirements
+
+8. Multi-Part Query Handling
+
+Address each component of multi-part queries separately
+Maintain logical flow between related pieces of information
+Preserve context when explaining complex processes
+
+9. Completeness Guidelines
+
+Ensure responses cover all aspects of user queries
+Provide step-by-step guidance for procedural questions
+Include relevant related information that users might need
+
+10. Response Quality Control
+
+Internally verify numerical data consistency
+Apply contextual understanding to identify potential OCR errors without mentioning them
+Present information with confidence unless critically uncertain
+Focus on delivering actionable information rather than discussing document limitations
+
+Input:
+OCR-processed text from uploaded documents: {context}
+Chat History: {history}
+Current Question: {question}
+Output:
+Give a clear, direct, and user-friendly response that focuses on the information itself rather than its OCR source. Present information confidently, mentioning verification only for critically important or potentially ambiguous details.
+"""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
+    new_vector_store = FAISS.load_local(
+        "faiss_index", embeddings, allow_dangerous_deserialization=True
+    )
+    QA_CHAIN_PROMPT = PromptTemplate(input_variables=["history", "context", "question"], template=template)
+    qa_chain = RetrievalQA.from_chain_type(llm, retriever=new_vector_store.as_retriever(), chain_type='stuff', verbose=True, chain_type_kwargs={"verbose": True,"prompt": QA_CHAIN_PROMPT,"memory": ConversationBufferMemory(memory_key="history",input_key="question"),})
+    return qa_chain
+     
+def handle_uploaded_file(uploaded_file, show_in_sidebar=False):
+    file_extension = os.path.splitext(uploaded_file.name)[1].lower()
+    file_path = os.path.join("temp", uploaded_file.name)
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Show document in the main panel and optionally in the sidebar
+    if show_in_sidebar:
+        st.sidebar.write(f"### File: {uploaded_file.name}")
+
+        # if file_extension == ".pdf":
+        #     st.session_state.pdf_ref = uploaded_file  # Save the PDF to session state
+        #     binary_data = st.session_state.pdf_ref.getvalue()  # Get the binary data of the PDF
+        #     # Use the pdf_viewer to display the PDF
+        #     # sidebar.pdf_viewer(input=binary_data, width=700)
+        if file_extension == ".pdf":
+            # Display the PDF in the sidebar by embedding the PDF file
+            with open(file_path, "rb") as pdf_file:
+                pdf_data = pdf_file.read()
+            # Use the HTML iframe to display the PDF in the sidebar
+            pdf_base64 = base64.b64encode(pdf_data).decode('utf-8')
+            st.sidebar.markdown(f'<iframe src="data:application/pdf;base64,{pdf_base64}" width="500" height="500"></iframe>', unsafe_allow_html=True)
+   
+        elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+            img = Image.open(file_path)
+            st.sidebar.image(img, caption=f"Uploaded Image: {uploaded_file.name}", use_container_width=True)  # Updated here
+        else:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                content = f.read()
+            st.sidebar.text_area("File Content", content, height=300)
+
+        
+    
+    # Optionally show document in the main content area
+    # st.write(f"### Main Panel - {uploaded_file.name}")
+    # if file_extension == '.pdf':
+    #     st.write("Displaying PDF:")
+    #     st.components.v1.html(f'<embed src="{file_path}" width="700" height="500" type="application/pdf">')
+    # elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+    #     img = Image.open(file_path)
+    #     st.image(img, caption=f"Uploaded Image: {uploaded_file.name}", use_column_width=True)
+    # else:
+    #     with open(file_path, 'r', encoding='utf-8') as f:
+    #         content = f.read()
+    #     st.text_area("File Content", content, height=300)
+
+def user_input(user_question):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "query": user_question}, return_only_outputs=True)
+    result = response.get("result", "No result found")
+    
+    # Save the question and answer to session state for history tracking
+    if 'conversation_history' not in st.session_state:
+        st.session_state.conversation_history = []
+    
+    # Append new question and response to the history
+    st.session_state.conversation_history.append({'question': user_question, 'answer': result})
+    
+    return result
+    
+# def handle_uploaded_file(uploaded_file, show_in_sidebar=False):
+#     file_extension = os.path.splitext(uploaded_file.name)[1].lower()
+#     file_path = os.path.join("temp", uploaded_file.name)
+#     os.makedirs(os.path.dirname(file_path), exist_ok=True)
+
+#     with open(file_path, "wb") as f:
+#         f.write(uploaded_file.getbuffer())
+
+#     # Show document in the main panel and optionally in the sidebar
+#     if show_in_sidebar:
+#         st.sidebar.write(f"### File: {uploaded_file.name}")
+#         if file_extension == '.pdf':
+#             st.sidebar.write("Displaying PDF:")
+#             st.sidebar.components.html(f'<embed src="{file_path}" width="700" height="500" type="application/pdf">')
+
+#             # st.sidebar.components.v1.html(f'<embed src="{file_path}" width="700" height="500" type="application/pdf">')
+#         elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+#             img = Image.open(file_path)
+#             st.sidebar.image(img, caption=f"Uploaded Image: {uploaded_file.name}", use_column_width=True)
+#         else:
+#             with open(file_path, 'r', encoding='utf-8') as f:
+#                 content = f.read()
+#             st.sidebar.text_area("File Content", content, height=300)
+    
+    # Optionally show document in the main content area
+    # st.write(f"### Main Panel - {uploaded_file.name}")
+    # if file_extension == '.pdf':
+    #     st.write("Displaying PDF:")
+    #     st.components.v1.html(f'<embed src="{file_path}" width="700" height="500" type="application/pdf">')
+    # elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+    #     img = Image.open(file_path)
+    #     st.image(img, caption=f"Uploaded Image: {uploaded_file.name}", use_column_width=True)
+    # else:
+    #     with open(file_path, 'r', encoding='utf-8') as f:
+    #         content = f.read()
+    #     st.text_area("File Content", content, height=300)
+
+# Streamlit app to upload files and interact with the Q&A system
+def main():
+    st.title("File Upload and OCR Processing")
+    st.write("Upload up to 5 files (PDF, JPG, JPEG, PNG, BMP)")
+
+
+    uploaded_files = st.file_uploader("Choose files", type=["pdf", "jpg", "jpeg", "png", "bmp"], accept_multiple_files=True)
+
+    if len(uploaded_files) > 0:
+        file_paths = []
+
+        # Save uploaded files and process them
+        for uploaded_file in uploaded_files[:5]:  # Limit to 5 files
+            file_path = os.path.join("temp", uploaded_file.name)
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            file_paths.append(file_path)
+
+
+        # Process the OCR and PDF files and store the vector data
+        st.write("Processing files...")
+        vector_store = process_ocr_and_pdf_files(file_paths)
+        st.write("Processing completed! The vector store has been updated.")
+        
+    show_in_sidebar = st.sidebar.checkbox("Show files in Sidebar", value=True)
+
+    if len(uploaded_files) > 0:
+        # Process and display each uploaded file in its format
+        for uploaded_file in uploaded_files:
+            handle_uploaded_file(uploaded_file, show_in_sidebar)
+
+        # Ask user for a question related to the documents
+        user_question = st.text_input("Ask a question related to the uploaded documents:")
+
+        if user_question:
+            response = user_input(user_question)
+            st.write("Answer:", response)
+
+            # Button to display chat history
+
+            # if st.button("Show Chat History"):
+            #     history = st.session_state.get('history', [])
+            #     if history:
+            #         st.write("Conversation History:")
+            #         for idx, (q, a) in enumerate(history):
+            #             st.write(f"Q{idx+1}: {q}")
+            #             st.write(f"A{idx+1}: {a}")
+            #     else:
+            #         st.write("No conversation history.")
+            with st.expander('Conversation History'):
+                for entry in st.session_state.conversation_history:
+                    st.info(f"Q: {entry['question']}\nA: {entry['answer']}")
+    
+
+if __name__ == "__main__":
+    main()