Create app.py

app.py

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+import gradio as gr
+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
+
+def setup_environment():
+    os.environ["GROQ_API_KEY"] = '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'
+
+# 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
+
+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 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)
+    return response.get("result", "No result found")
+
+def gradio_interface():
+    def process_files(files):
+        file_paths = []
+        for file in files:
+            file_path = os.path.join("temp", file.name)
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(file.read())
+            file_paths.append(file_path)
+        process_ocr_and_pdf_files(file_paths)
+        return "Files processed and vector store updated!"
+
+    def ask_question(user_question):
+        return user_input(user_question)
+
+    file_upload = gr.inputs.File(label="Upload Files", type="file", multiple=True)
+    text_input = gr.inputs.Textbox(label="Ask a question related to the uploaded documents:")
+
+    outputs = [gr.outputs.Textbox(label="Output"), gr.outputs.Textbox(label="Conversation History")]
+    interface = gr.Interface(
+        fn=[process_files, ask_question],
+        inputs=[file_upload, text_input],
+        outputs=outputs,
+        live=True
+    )
+    interface.launch()
+
+if __name__ == "__main__":
+    setup_environment()
+    gradio_interface()