Create app.py

app.py

@@ -0,0 +1,291 @@
+class AbbyyVantage:
+    """
+    A client to interact with the ABBYY Vantage public API.
+    Handles authentication, skill listing, transaction initiation, and result retrieval.
+    """
+
+    def __init__(self, client_id, client_secret, region="au"):
+        """
+        Initializes the AbbyyVantageClient by authenticating using client credentials.
+
+        Args:
+            client_id (str): Your ABBYY Vantage client ID.
+            client_secret (str): Your ABBYY Vantage client secret.
+            region (str): ABBYY Vantage region ('eu', 'us', 'au', etc.). Defaults to 'au'.
+
+        Raises:
+            Exception: If authentication fails or access token is not returned.
+        """
+        self.client_id = client_id
+        self.client_secret = client_secret
+        self.token_url = f"https://vantage-{region}.abbyy.com/auth2/connect/token"
+        self.api_base = f"https://vantage-{region}.abbyy.com/api/publicapi/v1"
+
+        try:
+            # Prepare data for token request using client credentials
+            data = {
+                'grant_type': 'client_credentials',
+                'client_id': self.client_id,
+                'client_secret': self.client_secret
+            }
+
+            # Request access token from ABBYY OAuth2 endpoint
+            res = requests.post(self.token_url, data=data)
+            res.raise_for_status()
+
+            # Extract access token from response
+            token = res.json().get('access_token')
+            if not token:
+                raise ValueError("No access token returned from ABBYY")
+
+            # Set authorization headers for future API calls
+            self._headers = {
+                "Authorization": f"Bearer {token}",
+                "accept": "application/json"
+            }
+        except Exception as e:
+            print(f"Error during authentication: {e}")
+            raise
+
+    def get_skills(self):
+        """
+        Retrieves a list of available document processing skills from ABBYY Vantage.
+
+        Returns:
+            dict or None: A JSON object containing skill metadata or None if the request fails.
+        """
+        try:
+            # Send GET request to fetch all available skills
+            res = requests.get(f'{self.api_base}/skills', headers=self._headers)
+            res.raise_for_status()
+            return res.json()
+        except Exception as e:
+            print(f"Failed to fetch skills: {e}")
+            return None
+
+    def process_document(self, file_path, skill_id):
+        """
+        Starts a new transaction by uploading a file to be processed using a specific skill.
+
+        Args:
+            file_path (str): Path to the local PDF file to be uploaded.
+            skill_id (str): The ID of the skill to be used for processing.
+
+        Returns:
+            str or None: The transaction ID returned by the API or None if the request fails.
+        """
+        try:
+            # Prepare API URL with query parameter for the skill ID
+            url = f"{self.api_base}/transactions/launch?skillId={skill_id}"
+
+            # Open the file in binary mode for upload
+            with open(file_path, "rb") as f:
+                files = {
+                    "Files": (os.path.basename(file_path), f, "application/pdf")
+                }
+
+                # Post the file to ABBYY API to start a transaction
+                res = requests.post(url, headers=self._headers, files=files)
+                res.raise_for_status()
+
+                # Extract and return the transaction ID
+                return res.json().get('transactionId')
+        except Exception as e:
+            print(f"Failed to start transaction: {e}")
+            return None
+
+    def get_document_results(self, transaction_id, output_path="result_file.txt"):
+        """
+        Checks the transaction status and downloads the result file if processing is complete.
+
+        Args:
+            transaction_id (str): The transaction ID to monitor.
+            output_path (str): Local file path to save the result file. Defaults to "result_file.txt".
+
+        Returns:
+            str or None: Path to the saved result file, or None if processing is incomplete or fails.
+        """
+        try:
+            # Get transaction status and metadata
+            url = f"{self.api_base}/transactions/{transaction_id}"
+            res = requests.get(url, headers=self._headers)
+            res.raise_for_status()
+            data = res.json()
+        except Exception as e:
+            print(f"Failed to fetch transaction details: {e}")
+            return None
+
+        # Extract processing status
+        status = data.get('status')
+        print(f"Transaction status: {status}")
+
+        # Handle status outcomes
+        if status == 'Processing':
+            print("File is still being processed. Try again later.")
+            return 'Processing'
+        elif status != 'Processed':
+            print(f"Unexpected status: {status}")
+            return f"Unexpected status: {status}"
+
+        try:
+            # Navigate to the result file ID in the JSON structure
+            file_id = data['documents'][0]['resultFiles'][0]['fileId']
+
+            # Build the download URL using transaction ID and file ID
+            download_url = f"{self.api_base}/transactions/{transaction_id}/files/{file_id}/download"
+
+            # Download the result file
+            res = requests.get(download_url, headers=self._headers)
+            res.raise_for_status()
+
+            # Save the file to the specified path
+            with open(output_path, 'wb') as f:
+                f.write(res.content)
+
+            print(f"File downloaded and saved to: {output_path}")
+            return 'Processed'
+
+        except (KeyError, IndexError) as e:
+            print(f"Error accessing file ID in response JSON: {e}")
+        except Exception as e:
+            print(f"Failed to download or save file: {e}")
+
+# df = pd.DataFrame(client.get_skills())
+# df
+
+# ----------- Process OCR & Setup Retrieval Agent -------------
+def process_pdf_ocr(file):
+    print('process_pdf_ocr', file)
+    client = AbbyyVantage(client_id=os.getenv("ABBY_CLIENT_ID"),
+                          client_secret=os.getenv("ABBY_CLIENT_SECRET"),
+                          region="au"  # or "us", "au", etc.
+                          )
+    skill_id = '1681402d-2931-41cb-9717-bb7612bc09aa'
+    trans_id = client.process_document(file_path=file, skill_id=skill_id)
+
+    @retry(stop=stop_after_delay(60), wait=wait_fixed(3))
+    def wait_for_processing():
+        status = client.get_document_results(trans_id, output_path="/tmp/result_file.txt")
+        print(f"Status: {status}")
+        if status == 'Processed':
+            print("|-- Processed")
+            return status
+        raise Exception("Still Processing")
+
+    try:
+        status = wait_for_processing()
+        print("|--OCR Successful")
+        setup_agent("/tmp/result_file.txt")
+        print("|--Chatbot is ready")
+        return "OCR Successful. Chatbot is ready"
+    except RetryError:
+        print("|--OCR Failed or Timed Out")
+        return "OCR Failed or Timed Out"
+
+
+# Global state
+retrieval_chain = None
+agent_executor = None
+
+
+# ----------- Setup LangChain Retrieval Agent -------------
+def setup_agent(file):
+    global retrieval_chain, agent_executor
+
+    if not os.path.exists(file):
+        return "Please process a PDF first."
+
+    loader = TextLoader(file)
+    documents = loader.load()
+
+    splitter = SpacyTextSplitter()
+    chunks = splitter.split_documents(documents)
+
+    embeddings = OpenAIEmbeddings()
+    vectordb = Chroma.from_documents(chunks, embedding=embeddings, collection_name=f"temp_collection_{uuid.uuid4().hex}")
+    retriever = vectordb.as_retriever(search_kwargs={"k": 10})
+
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+    retrieval_chain = ConversationalRetrievalChain.from_llm(
+        llm=ChatOpenAI(model="gpt-3.5-turbo"),
+        retriever=retriever,
+        memory=memory,
+        return_source_documents=False
+    )
+
+    tools = [
+        Tool(
+            name="PolicyRetrievalRAG",
+            func=retrieval_chain.run,
+            description="Use this to retreive policy clauses from the policy."
+        )
+    ]
+
+    agent_executor = initialize_agent(
+        tools=tools,
+        llm=ChatOpenAI(model="gpt-3.5-turbo"),
+        agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
+        #agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
+        memory=memory,
+        verbose=True
+    )
+
+    print("|--Agent setup complete")
+    return "Agent is ready."
+
+# ----------- Chat Interface Handler -------------
+def ask_question(message, history):
+    if agent_executor is None:
+        return "❗ Chatbot not ready. Please upload and process a PDF first."
+
+    advisory_prompt = (
+        "Reterive policy information using PolicyRetrievalRAG tool and answer the user questions."
+        "Always use data returned by the policy. Do not makeup information."
+        #"In addition to answering the question based on the insurance policy, "
+        #"give practical advice to the user on how they might use or take advantage of any relevant clause."
+    )
+    prompt = f"{advisory_prompt}\nQuestion: {message}"
+
+    try:
+        response = agent_executor.run(prompt)
+        return response
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# ----------- Gradio UI -------------
+with gr.Blocks(gr.themes.Soft(), title="📄 Insurance Policy AIdvisor") as demo:
+    gr.Markdown("# Welcome to the Insurance Policy AIdvisor App")
+    gr.Markdown("## Upload policy and converse")
+    with gr.Tab("📄 Upload PDF"):
+        gr.Markdown("### Upload a PDF. And Intellignet Automation Processng will automatically processing it using ABBYY Vantage, ChromaDB and LangChain")
+        pdf_file = gr.File(label="📤 Upload a PDF", file_types=[".pdf"])
+        ocr_status = gr.Textbox(label="Processing Status", interactive=False)
+
+        pdf_file.change(process_pdf_ocr, inputs=[pdf_file], outputs=[ocr_status])
+
+        gr.Examples(
+            examples=[["small-insudoc.pdf"],["Principal-Sample-Life-Insurance-Policy.pdf"]],
+            inputs=[pdf_file],
+            label="Example PDFs"
+        )
+
+    with gr.Tab("💬 Chatbot"):
+        gr.Markdown("### Ask about the policy and get advice.")
+        chat = gr.ChatInterface(fn=ask_question, chatbot=gr.Chatbot())
+
+        gr.Examples(
+            examples=[
+                "In what forms are the certificate avalaible?",
+                "How many employees should enroll if the member is to not contribute premium?",
+                "Can insurer contest this policy?",
+                "when can insurer make changes to the policy?",
+                "I gave incorrect age in the policy, what to do now?",
+                "Can the data I filled in the application form to get the insurance policy be used against me?"
+            ],
+            inputs=chat.textbox
+        )
+    with gr.Tab("System Sequence Design"):
+        gr.Markdown("[![](https://mermaid.ink/img/pako:eNrtVl1v2jAU_SuWn1qJIkIIhUirRGEgHrZVDCZ1QkImMcFqYme2s41V_e-7jhOapKnW91VIwbHP_b7HuY84ECHFPlb0R0Z5QGeMRJIkW45QSqRmAUsJ12i5mk3WiKh8sWyebhSV5tD8N88WkoRMbJbmvFw3MakUAVVql4aHnQhyVcvZXRM12e9Pp2-wIhE1kMnt7f09KjaaYEV1lu7gANYGmy_mJNBCnprY6VGKhMxuDbBcNzErqiWjP22cq8misrEWIn7ha83wlptzkmnBs2RvcyRpoJGM9hd9x-mg50ev27805wh9FpqimB40Eoc8tb7NPprH4peF2LJc3dw0U-ijTRoLEiISx-huNlcW30y0kaym1UeTTB_BYxYQME54iCKqkRYPlL9VQ4kIRZAloOriwGLaQeqBxfGOhUVwtWJetUagJeEKKsYEBzkrFguRog34B1FZPA2RgBqwhIpMW9BbvISwzh7uJFVZrNVF3eJlqe0NvipNdKbQh9IrxiPj1tlHq4rysC36NoVle6HCt9ezX-l0H301LyCTC5MYEdt-RrbKiFaTC6j0rEjJa_ZeGGwINI2UdPJhlfGHvKM-AgXCsB1_ZhUISGp6UJY77QIT60YBNsSclCEX2X4mmucBx_JHf_APouU3GvCmwrV8y5gsb7EzxwBnIee7rjW_a8miqFD7ZboqAdAq7-R8J-f_Sc6cesCLY869Ar-4WyO366F1JveiRmHn2jWfySE83N6rFK4Q1HB2Kjh4qIjpnCqVG1w2ezADqWdYjc6FbzUY-sX00dQhSYs6TlqTZT7CJvpyUkCV6aFeCNinkjJorbwkReHOA0pTcQVT0Vhxd1Vt0MK5etx2E3Cp4Iq2BG4vxDnj0LGygMGlg7Ji2MsraX64gyPJQuxrmdEOTqhMiHnFjwa2xXB3JXSLfViG9EDAry3e8icQg5HpuxBJKSlFFh2xfyCxgrcsDaF7irn0DAGrVE5FxjX2R6NcBfYf8W_sX426o6HrjLzr_nDoek5v3MEn2HaHTtfz-uOB43g9Z-iNnzr4T27V6Q561-7A7Q9H7ngwuO71O5iGDEbFT3Y4zmfkp7_uT8Ws?type=png)](https://mermaid.live/edit#pako:eNrtVl1v2jAU_SuWn1qJIkIIhUirRGEgHrZVDCZ1QkImMcFqYme2s41V_e-7jhOapKnW91VIwbHP_b7HuY84ECHFPlb0R0Z5QGeMRJIkW45QSqRmAUsJ12i5mk3WiKh8sWyebhSV5tD8N88WkoRMbJbmvFw3MakUAVVql4aHnQhyVcvZXRM12e9Pp2-wIhE1kMnt7f09KjaaYEV1lu7gANYGmy_mJNBCnprY6VGKhMxuDbBcNzErqiWjP22cq8misrEWIn7ha83wlptzkmnBs2RvcyRpoJGM9hd9x-mg50ev27805wh9FpqimB40Eoc8tb7NPprH4peF2LJc3dw0U-ijTRoLEiISx-huNlcW30y0kaym1UeTTB_BYxYQME54iCKqkRYPlL9VQ4kIRZAloOriwGLaQeqBxfGOhUVwtWJetUagJeEKKsYEBzkrFguRog34B1FZPA2RgBqwhIpMW9BbvISwzh7uJFVZrNVF3eJlqe0NvipNdKbQh9IrxiPj1tlHq4rysC36NoVle6HCt9ezX-l0H301LyCTC5MYEdt-RrbKiFaTC6j0rEjJa_ZeGGwINI2UdPJhlfGHvKM-AgXCsB1_ZhUISGp6UJY77QIT60YBNsSclCEX2X4mmucBx_JHf_APouU3GvCmwrV8y5gsb7EzxwBnIee7rjW_a8miqFD7ZboqAdAq7-R8J-f_Sc6cesCLY869Ar-4WyO366F1JveiRmHn2jWfySE83N6rFK4Q1HB2Kjh4qIjpnCqVG1w2ezADqWdYjc6FbzUY-sX00dQhSYs6TlqTZT7CJvpyUkCV6aFeCNinkjJorbwkReHOA0pTcQVT0Vhxd1Vt0MK5etx2E3Cp4Iq2BG4vxDnj0LGygMGlg7Ji2MsraX64gyPJQuxrmdEOTqhMiHnFjwa2xXB3JXSLfViG9EDAry3e8icQg5HpuxBJKSlFFh2xfyCxgrcsDaF7irn0DAGrVE5FxjX2R6NcBfYf8W_sX426o6HrjLzr_nDoek5v3MEn2HaHTtfz-uOB43g9Z-iNnzr4T27V6Q561-7A7Q9H7ngwuO71O5iGDEbFT3Y4zmfkp7_uT8Ws)")
+
+demo.launch(debug=True)