Update app.py

app.py

@@ -1,64 +1,173 @@
+import os
+from dotenv import load_dotenv
+from langchain_community.utilities import SQLDatabase
+load_dotenv()
+db_uri = os.getenv("DB_CONNECTION_STRING", "")
+db_final = SQLDatabase.from_uri(db_uri)
 import gradio as gr
-from huggingface_hub import InferenceClient
+from file_upload import FileHandler
+from chat import ChatHandler
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Initialize the FileHandler inline
+VECTOR_DB_PATH = os.getenv("VECTOR_DB_PATH_DB", "")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+GROK_API_KEY = os.getenv("GROK_API_KEY", "")
 
+# Initialize FileHandler and ChatHandler
+file_handler = FileHandler(VECTOR_DB_PATH, OPENAI_API_KEY, GROK_API_KEY)
+chat_handler = ChatHandler(VECTOR_DB_PATH, OPENAI_API_KEY, GROK_API_KEY, db_final)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Chat history
+chat_history = []
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+# File Upload and Processing Function
+def handle_file_uploads(files):
+    try:
+        if not files:
+            return "Please upload a file.", chat_history
+        file_name = files.name  # Retrieve the name of the uploaded file
+        response = file_handler.handle_file_upload(file_name, files)
+        return response["message"]
+    except Exception as e:
+        return f"Error processing file: {e}", chat_history
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Chat Function
+def handle_chat(user_message):
+    global chat_history
+    try:
+        if not user_message.strip():
+            return [("system", "Please enter a question.")]
 
-        response += token
-        yield response
+        # Add user message to chat history
+        
+        if "chart" in user_message.strip():# or "graph" in user_message.strip() or "plot" in user_message.strip()):
+          
+            parts = user_message.split("for", 1)  # Split only at the first occurrence of "with"
 
+            # Assign the first part and second part to variables
+            visual_query = user_message  # Strip to remove leading/trailing spaces
+            user_message = parts[1].strip() if len(parts) > 1 else None
+        
+        else:
+            visual_query = None    
+        # Get AI response
+        ai_response, visual_response = chat_handler.answer_question(user_message.strip(),visual_query)
+        user_message = f"<b style='color:#6366f1;'>user:</b> {user_message.strip()}"
+        if visual_response:
+            from PIL import Image
+            import base64
+            from io import BytesIO
+            # Open the image file
+            img = Image.open(visual_response)
 
+            # Convert the PIL Image to a base64 encoded string
+            buffered = BytesIO()
+            img.save(buffered, format="PNG")
+            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+            img = f'<img src="data:image/png;base64,{img_str}" style="width:450px; height:400px;">'
+
+            ai_response =  img + ai_response 
+            
+            user_message = visual_query.strip()
+        
+        ai_response = f"<b style='color:#6366f1;'>AI:</b> {ai_response}"
+        # Add AI response to chat history
+        
+        chat_history.append((user_message,ai_response))
+       
+        return chat_history
+
+    except Exception as e:
+        chat_history.append((visual_query, f"Sorry, I couldn't process your request.Please try again."))
+        return chat_history
+ # Chat handling function
+def chat_interaction(user_message):
+    updated_history = handle_chat(user_message)
+    return updated_history, ""  # Return the chat history to the chatbot
+
+
+with gr.Blocks(theme="soft") as app:  # Use the 'soft' theme
+    gr.Markdown(
+        "<h1 style='color:blue;'>Agentic AI - Chat with Documents & Database</h1>", 
+        elem_id="title"
+    )
+
+    with gr.Tab("Chat"):
+        chat_box = gr.Chatbot(label="Chat History")
+        with gr.Row():  # Place elements in the same row
+            with gr.Column(scale=2):
+                user_input = gr.Textbox(
+                placeholder="Type your message here...", 
+                label="Your Message", 
+                elem_id="user-input", container=False)
+            with gr.Column(scale=1):
+                 with gr.Row():  # Place the buttons in the same row
+                    send_button = gr.Button("Send", elem_id="send-button")
+                    clear_button = gr.Button("Clear", elem_id="clear-button")
+
+       
+
+        send_button.click(
+            chat_interaction,
+            inputs=[user_input],
+            outputs=[chat_box, user_input]
+        )
+
+    with gr.Tab("File Upload"):
+        upload_button = gr.UploadButton(
+            label="Upload your file (PDF, Excel, Docx, Txt, CSV)",
+            file_types=[".pdf", ".xlsx", ".docx", ".txt", ".csv"],
+            file_count="single"
+        )
+        file_output = gr.Textbox(label="File Processing Output")
+        upload_button.upload(fn=handle_file_uploads, inputs=upload_button, outputs=[file_output])
+
+        # Clear input field function
+        def clear_input():
+            return ""  # Clear the input field
+
+        clear_button.click(
+            clear_input,
+            inputs=[],
+            outputs=[user_input]  # Clear only the user_input field
+        )
+
+# Custom CSS for styling
+app.css = """
+#send-button {
+    color: white;
+    border-radius: 20px; /* Round corners */
+    background-color: #6366f1;
+    transition: background-color 0.3s, transform 0.3s;
+}
+
+#send-button:hover {
+    background-color: #6366f0; /* Change background color on hover */
+    transform: scale(1.05); /* Slightly enlarge on hover */
+}
+#clear-button {
+    color: white;
+    border-radius: 20px; /* Round corners */
+    background-color: #6366f1;
+    transition: background-color 0.3s, transform 0.3s;
+}
+
+#clear-button:hover {
+    background-color: #6366f0; /* Change background color on hover */
+    transform: scale(1.05); /* Slightly enlarge on hover */
+}
+#user-input {
+    flex-grow: 1; /* Allow textbox to take remaining space */
+}
+
+#title {
+    margin-bottom: 10px; /* Space below the title */
+    text-align: center;
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+# Launch the app
+app.launch(debug=True)