Update app.py

app.py

@@ -13,7 +13,6 @@ from langchain_core.documents import Document
 # Retrieve API keys from environment variables
 google_api_key = os.getenv("GOOGLE_API_KEY")
 tavily_api_key = os.getenv("TAVILY_API_KEY")
-docusign_api_key = os.getenv("DOCUSIGN_API_KEY")
 
 # Configure Google Generative AI
 genai.configure(api_key=google_api_key)
@@ -100,17 +99,11 @@ def generate_response_with_rag(query, pdf_path, state):
         embedding_model = state["embedding_model"]
 
     # Retrieve relevant chunks based on the query
-    relevant_chunks = retrieve_relevant_chunks(query, chunks, embeddings, embedding_model, top_k=5)  # Increase top_k
+    relevant_chunks = retrieve_relevant_chunks(query, chunks, embeddings, embedding_model, top_k=5)
     
-    # Debug: Print relevant chunks
-    print(f"Relevant Chunks: {relevant_chunks}")
-
     # Combine the relevant chunks into a single context
     context = "\n\n".join(relevant_chunks)
 
-    # Debug: Print the context
-    print(f"Context from PDF: {context}")
-
     # Create a prompt that instructs the model to answer only from the context
     prompt = f"""
     You are a helpful assistant that answers questions based on the provided context. 
@@ -125,9 +118,6 @@ def generate_response_with_rag(query, pdf_path, state):
     **Answer:**
     """
 
-    # Debug: Print the prompt
-    print(f"Prompt for Gemini: {prompt}")
-
     # Send the prompt to the Gemini model
     try:
         response = chat_session.send_message(prompt)
@@ -135,14 +125,10 @@ def generate_response_with_rag(query, pdf_path, state):
 
         # Check if the initial answer is "I don't know"
         if "I don't know" in initial_answer or "i don't know" in initial_answer:
-            print("Initial answer is 'I don't know'. Performing web search...")
             docs = web_search_tool.invoke({"query": query})
             web_results = "\n".join([d["content"] for d in docs])
             web_results = Document(page_content=web_results)
             
-            # Debug: Print web search results
-            print(f"Web Search Results: {web_results.page_content}")
-
             # Create a prompt that instructs the model to answer from the web search results
             web_prompt = f"""
             You are a helpful assistant that answers questions based on the provided context. 
@@ -157,118 +143,52 @@ def generate_response_with_rag(query, pdf_path, state):
             **Answer:**
             """
             
-            # Debug: Print the prompt
-            print(f"Prompt for Gemini (Web Search): {web_prompt}")
-
-            # Send the prompt to the Gemini model
             web_response = chat_session.send_message(web_prompt)
-            # Add a note indicating the answer is based on a web search
             return f"{web_response.text}\n\n*Note: This answer is based on a web search.*"
         else:
             return initial_answer
     except Exception as e:
         return f"Error generating response: {e}"
 
-# Function to send document to DocuSign
-def send_to_docusign(file_path, recipient_email, recipient_name):
-    account_id = "184d0409-2626-4c48-98b5-d383b9854a47"
-    base_url = "https://demo.docusign.net/restapi"
-
-    with open(file_path, "rb") as file:
-        document_base64 = base64.b64encode(file.read()).decode()
-
-    envelope_definition = {
-        "emailSubject": "Please sign this document",
-        "documents": [
-            {
-                "documentId": "1",
-                "name": "document.pdf",
-                "fileExtension": "pdf",
-                "documentBase64": document_base64
-            }
-        ],
-        "recipients": {
-            "signers": [
-                {
-                    "email": recipient_email,
-                    "name": recipient_name,
-                    "recipientId": "1",
-                    "tabs": {
-                        "signHereTabs": [
-                            {
-                                "documentId": "1",
-                                "pageNumber": "1",
-                                "xPosition": "100",
-                                "yPosition": "100"
-                            }
-                        ]
-                    }
-                }
-            ]
-        },
-        "status": "sent"
-    }
-
-    headers = {
-        "Authorization": f"Bearer {docusign_api_key}",
-        "Content-Type": "application/json"
-    }
-    try:
-        response = requests.post(
-            f"{base_url}/v2.1/accounts/{account_id}/envelopes",
-            headers=headers,
-            json=envelope_definition
-        )
-        response.raise_for_status()
-        return response.json()
-    except requests.exceptions.RequestException as e:
-        return {"error": str(e)}
-
 # Function to process the agreement
-def process_agreement(file, recipient_email, recipient_name, state):
+def process_agreement(file, state):
     try:
         text = extract_text_from_pdf(file.name)
         if text.startswith("Error"):
-            return text, {}, {}, state
+            return text, {}, state
 
         # Use Gemini for summarization
         summary = summarize_agreement_with_gemini(text)
         if summary.startswith("Error"):
-            return summary, {}, {}, state
-
-        docusign_response = send_to_docusign(file.name, recipient_email, recipient_name)
-        if "error" in docusign_response:
-            return summary, {}, docusign_response, state
+            return summary, {}, state
 
-        return summary, {}, docusign_response, state
+        return summary, {}, state
     except Exception as e:
-        return f"Error: {e}", {}, {}, state
+        return f"Error: {e}", {}, state
 
 # Gradio interface
-def main_interface(file, recipient_email, recipient_name, question, state):
+def main_interface(file, question, state):
     if file is not None:
         state["file"] = file
         state["text"] = extract_text_from_pdf(file.name)
         state["chat_history"] = []  # Initialize chat history
 
     summary_output = ""
-    docusign_output = {}
     chatbot_output = ""
 
     if "file" in state:
-        if recipient_email and recipient_name:
-            summary_output, _, docusign_output, state = process_agreement(state["file"], recipient_email, recipient_name, state)
+        summary_output, _, state = process_agreement(state["file"], state)
 
         if question:
             chatbot_output = generate_response_with_rag(question, state["file"].name, state)
-            state["chat_history"].append((question, chatbot_output))  # Update chat history
+            state["chat_history"].append((question, chatbot_output))
 
-    return summary_output, docusign_output, chatbot_output, state
+    return summary_output, chatbot_output, state
 
 # CSS for styling
 css = """
 .gradio-container {
-    background-image: url('https://huggingface.co/spaces/Codequestt/DocWise/blob/main/DALL%C2%B7E%202025-01-26%2011.43.33%20-%20A%20futuristic%20and%20sleek%20magical%20animated%20GIF-style%20icon%20design%20for%20'DocWise'%2C%20representing%20knowledge%2C%20documents%2C%20and%20wisdom.%20The%20design%20includes%20a%20glow.jpg');
+    background-image: url('https://huggingface.co/spaces/Nadaazakaria/DocWise/resolve/main/DALL%C2%B7E%202025-01-26%2011.43.33%20-%20A%20futuristic%20and%20sleek%20magical%20animated%20GIF-style%20icon%20design%20for%20%27DocWise%27%2C%20representing%20knowledge%2C%20documents%2C%20and%20wisdom.%20The%20design%20includes%20a%20glow.jpg');
     background-size: cover;
     background-position: center;
     background-repeat: no-repeat;
@@ -324,7 +244,7 @@ with gr.Blocks(css=css) as app:
         """
     <div style="text-align: center;">
         <h1 id="main-title">
-            DocWise(Agreement Analyzer with Chatbot and Docusign Integration)
+            DocWise (Agreement Analyzer with Chatbot)
         </h1>
     </div>
         """,
@@ -333,12 +253,9 @@ with gr.Blocks(css=css) as app:
     state = gr.State({})
     file_input = gr.File(label="Upload Agreement (PDF)")
 
-    with gr.Tab("Agreement Processing", elem_id="agreement-tab"):
-        email_input = gr.Textbox(label="Recipient Email")
-        name_input = gr.Textbox(label="Recipient Name")
-        summary_output = gr.Textbox(label="Agreement Summary")
-        docusign_output = gr.JSON(label="DocuSign Response")
-        process_button = gr.Button("Process Agreement", elem_id="process-button")
+    with gr.Tab("Document Processing", elem_id="processing-tab"):
+        summary_output = gr.Textbox(label="Document Summary")
+        process_button = gr.Button("Process Document", elem_id="process-button")
 
     with gr.Tab("Chatbot", elem_id="chatbot-tab"):
         chatbot_question_input = gr.Textbox(label="Ask a Question")
@@ -347,13 +264,13 @@ with gr.Blocks(css=css) as app:
 
     process_button.click(
         main_interface,
-        inputs=[file_input, email_input, name_input, chatbot_question_input, state],
-        outputs=[summary_output, docusign_output, chatbot_answer_output, state]
+        inputs=[file_input, gr.State(), state],
+        outputs=[summary_output, chatbot_answer_output, state]
     )
     chatbot_button.click(
         main_interface,
-        inputs=[file_input, email_input, name_input, chatbot_question_input, state],
-        outputs=[summary_output, docusign_output, chatbot_answer_output, state]
+        inputs=[file_input, chatbot_question_input, state],
+        outputs=[summary_output, chatbot_answer_output, state]
     )
 
 app.launch(debug=True)