Create app.py

app.py

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+import os
+import gradio as gr
+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
+import base64
+from io import BytesIO
+
+# Set up Groq API Key and LLM 
+os.environ["GROQ_API_KEY"] = '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'
+llm = ChatGroq(
+    model='llama3-70b-8192',
+    temperature=0.5,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2
+)
+
+# OCR Functions
+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
+
+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)
+    
+    os.makedirs("faiss_index", exist_ok=True)
+    vector_store.save_local("faiss_index")
+    
+    return 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)
+
+def get_conversational_chain():
+    template = """You are an intelligent educational assistant specialized in handling queries about documents. You have been provided with OCR-processed text from the uploaded files that contains important educational information.
+
+Core Responsibilities:
+1. Language Processing:
+   - Identify the language of the user's query (English or Gujarati)
+   - Respond in the same language as the query
+   - If the query is in Gujarati, ensure the response maintains proper Gujarati grammar and terminology
+   - For technical terms, provide both English and Gujarati versions when relevant
+
+2. Document Understanding:
+   - Analyze the OCR-processed text from the uploaded files
+   - Account for potential OCR errors or misinterpretations
+   - Focus on extracting accurate information despite possible OCR imperfections
+
+3. Response Guidelines:
+   - Provide direct, clear answers based solely on the document content
+   - If information is unclear due to OCR quality, mention this limitation
+   - For numerical data (dates, percentages, marks), double-check accuracy before responding
+   - If information is not found in the documents, clearly state: "This information is not present in the uploaded documents"
+
+4. Educational Context:
+   - Maintain focus on educational queries related to the document content
+   - For admission-related queries, emphasize important deadlines and requirements
+   - For scholarship information, highlight eligibility criteria and application processes
+   - For course-related queries, provide detailed, accurate information from the documents
+
+5. Response Format:
+   - Structure responses clearly with relevant subpoints when necessary
+   - For complex information, break down the answer into digestible parts
+   - Include relevant reference points from the documents when applicable
+   - Format numerical data and dates clearly
+
+6. Quality Control:
+   - Verify that responses align with the document content
+   - Don't make assumptions beyond the provided information
+   - If multiple interpretations are possible due to OCR quality, mention all possibilities
+   - Maintain consistency in terminology throughout the conversation
+
+Important Rules:
+- Never make up information not present in the documents
+- Don't combine information from previous conversations or external knowledge
+- Always indicate if certain parts of the documents are unclear due to OCR quality
+- Maintain professional tone while being accessible to students and parents
+- If the query is out of scope of the uploaded documents, politely redirect to relevant official sources
+
+Context from uploaded documents:
+{context}
+
+Chat History:
+{history}
+
+Current Question: {question}
+Assistant: Let me provide a clear and accurate response based on the uploaded documents...
+"""
+    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 process_files_and_query(files, query):
+    if len(files) > 5:
+        return "Error: You can upload a maximum of 5 files only."
+    
+    # Ensure temp directory exists
+    os.makedirs("temp", exist_ok=True)
+    
+    # Save uploaded files
+    file_paths = []
+    for file in files:
+        file_path = os.path.join("temp", os.path.basename(file))
+        with open(file_path, "wb") as f:
+            f.write(open(file, 'rb').read())
+        file_paths.append(file_path)
+    
+    # Process files and create vector store
+    process_ocr_and_pdf_files(file_paths)
+    
+    # Perform query
+    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(query)
+    
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "query": query}, return_only_outputs=True)
+    result = response.get("result", "No result found")
+    
+    return result
+def handle_uploaded_file(uploaded_files, show_in_sidebar=False):
+    sidebar_content = ""
+    
+    if len(uploaded_files) > 5:
+        return "Error: You can upload a maximum of 5 files only."
+    
+    # If the uploaded_files is a list, process each file
+    for uploaded_file in uploaded_files:
+        # Determine the file extension
+        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)
+
+        # Check if the uploaded file is in 'NamedString' format (Gradio sometimes returns it this way)
+        if isinstance(uploaded_file, gr.File):
+            # In this case, read the file directly from the 'data' attribute
+            file_data = uploaded_file.read()  # This is the file content in bytes
+
+            # Save the file content to a local file
+            with open(file_path, "wb") as f:
+                f.write(file_data)
+
+        if file_extension == ".pdf":
+            # Read and encode the PDF as base64 to embed in the sidebar
+            with open(file_path, "rb") as pdf_file:
+                pdf_data = pdf_file.read()
+            pdf_base64 = base64.b64encode(pdf_data).decode('utf-8')
+            sidebar_content += f'<iframe src="data:application/pdf;base64,{pdf_base64}" width="500" height="500"></iframe>'
+        
+        elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+            # Display image in the sidebar
+            img = Image.open(file_path)
+            img_byte_array = BytesIO()
+            img.save(img_byte_array, format="PNG")
+            img_byte_array.seek(0)
+            sidebar_content += f'<img src="data:image/png;base64,{base64.b64encode(img_byte_array.getvalue()).decode()}" width="400" height="400"/>'
+
+        else:
+            # For text files, show the file content
+            with open(file_path, 'r', encoding='utf-8') as f:
+                content = f.read()
+            sidebar_content += f"<pre>{content}</pre>"
+
+    return sidebar_content
+
+# Gradio interface setup
+def upload_and_display(files):
+
+    if len(files) > 5:
+        return "Error: You can upload a maximum of 5 files only."
+    
+    sidebar_content = handle_uploaded_file(files, show_in_sidebar=True)
+    return sidebar_content
+
+def launch_gradio_app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Document OCR and Q&A Assistant")
+        
+        with gr.Row():
+            with gr.Column(scale=1):  # Main content area (adjusted scale to an integer)
+                file_input = gr.File(
+                    file_count="multiple", 
+                    type="filepath",  # Changed from 'filepath' to 'file'
+                    file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp"],
+                    label="Upload Documents (PDF/Images)"
+                )
+                
+                query_input = gr.Textbox(
+                    label="Ask a Question about the Documents", 
+                    lines=3
+                )
+                
+                submit_btn = gr.Button("Process and Query")
+                
+                output = gr.Textbox(label="Answer", lines=5)
+                
+                submit_btn.click(
+                    fn=process_files_and_query, 
+                    inputs=[file_input, query_input], 
+                    outputs=[output]
+                )
+                
+            with gr.Column(scale=1):  # Sidebar (adjusted scale to an integer)
+                gr.Markdown("## Sidebar")
+                file_preview = gr.HTML(label="File Preview")  # Display the preview content here
+                file_input.change(fn=upload_and_display, inputs=file_input, outputs=file_preview)
+    
+    return demo
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    app = launch_gradio_app()
+    app.launch(share=True)  # Set share=True to create a public link
+
+
+
+# # Launch the Gradio app
+# if __name__ == "__main__":
+#     app = launch_gradio_app()
+#     # app.launch()
+#     app.launch(share=True) 
+    # demo.launch()