Update app.py

app.py

@@ -1,388 +1,42 @@
-import gradio as gr
-import openai
-import fitz  # PyMuPDF for PDF processing
-import base64
-import io
+from Crypto.Cipher import AES
+from Crypto.Protocol.KDF import PBKDF2
+import os
+import tempfile
+from dotenv import load_dotenv
 
-# Variable to store API key
-api_key = ""
+load_dotenv()  # Load all environment variables
 
-# Function to update API key
-def set_api_key(key):
-    global api_key
-    api_key = key
-    return "API Key Set Successfully!"
+def unpad(data):
+    return data[:-data[-1]]
 
-# Function to interact with OpenAI API
-def query_openai(messages, temperature, top_p, max_output_tokens):
-    if not api_key:
-        return "Please enter your OpenAI API key first."
+def decrypt_and_run():
+    # Get password from Hugging Face Secrets environment variable
+    password = os.getenv("PASSWORD")
+    if not password:
+        raise ValueError("PASSWORD secret not found in environment variables")
 
-    try:
-        openai.api_key = api_key  # Set API key dynamically
+    password = password.encode()
 
-        # Ensure numeric values for OpenAI parameters
-        temperature = float(temperature) if temperature else 1.0
-        top_p = float(top_p) if top_p else 1.0
-        max_output_tokens = int(max_output_tokens) if max_output_tokens else 2048
+    with open("code.enc", "rb") as f:
+        encrypted = f.read()
 
-        response = openai.ChatCompletion.create(
-            model="gpt-4.1",
-            messages=messages,
-            temperature=temperature,
-            top_p=top_p,
-            max_tokens=max_output_tokens
-        )
-        return response["choices"][0]["message"]["content"]
-    except Exception as e:
-        return f"Error: {str(e)}"
+    salt = encrypted[:16]
+    iv = encrypted[16:32]
+    ciphertext = encrypted[32:]
 
-# Function to process image URL input
-def image_url_chat(image_url, text_query, temperature, top_p, max_output_tokens):
-    if not image_url or not text_query:
-        return "Please provide an image URL and a query."
+    key = PBKDF2(password, salt, dkLen=32, count=1000000)
+    cipher = AES.new(key, AES.MODE_CBC, iv)
 
-    messages = [
-        {"role": "user", "content": [
-            {"type": "image_url", "image_url": {"url": image_url}},
-            {"type": "text", "text": text_query}
-        ]},
-    ]
-    return query_openai(messages, temperature, top_p, max_output_tokens)
+    plaintext = unpad(cipher.decrypt(ciphertext))
 
-# Function to process text input
-def text_chat(text_query, temperature, top_p, max_output_tokens):
-    if not text_query:
-        return "Please enter a query."
+    with tempfile.NamedTemporaryFile(suffix=".py", delete=False, mode='wb') as tmp:
+        tmp.write(plaintext)
+        tmp.flush()
+        print(f"[INFO] Running decrypted code from {tmp.name}")
+        os.system(f"python {tmp.name}")
 
-    messages = [{"role": "user", "content": [{"type": "text", "text": text_query}]}]
-    return query_openai(messages, temperature, top_p, max_output_tokens)
-
-# Function to process uploaded image input
-def image_chat(image_file, text_query, temperature, top_p, max_output_tokens):
-    if image_file is None or not text_query:
-        return "Please upload an image and provide a query."
-
-    # Encode image as base64
-    with open(image_file, "rb") as img:
-        base64_image = base64.b64encode(img.read()).decode("utf-8")
-
-    image_data = f"data:image/jpeg;base64,{base64_image}"
-
-    messages = [
-        {"role": "user", "content": [
-            {"type": "image_url", "image_url": {"url": image_data}},
-            {"type": "text", "text": text_query}
-        ]},
-    ]
-    return query_openai(messages, temperature, top_p, max_output_tokens)
-
-# Function to process uploaded PDF input
-def pdf_chat(pdf_file, text_query, temperature, top_p, max_output_tokens):
-    if pdf_file is None or not text_query:
-        return "Please upload a PDF and provide a query."
-
-    try:
-        # Extract text from all pages of the PDF
-        doc = fitz.open(pdf_file.name)
-        text = "\n".join([page.get_text("text") for page in doc])  # Extract text from all pages
-
-        # If no text found, return an error
-        if not text.strip():
-            return "No text found in the PDF."
-
-        # Create the query message with the extracted text and the user's query
-        messages = [
-            {"role": "user", "content": [
-                {"type": "text", "text": text},  # The extracted text from the PDF
-                {"type": "text", "text": text_query}
-            ]},
-        ]
-        return query_openai(messages, temperature, top_p, max_output_tokens)
-    
-    except Exception as e:
-        return f"Error processing the PDF: {str(e)}"
-
-# Function to transcribe audio to text using OpenAI Whisper API
-def transcribe_audio(audio_binary, openai_api_key):
-    if not openai_api_key:
-        return "Error: No API key provided."
-    
-    openai.api_key = openai_api_key
-    
-    try:
-        # Use the correct transcription API call
-        audio_file_obj = io.BytesIO(audio_binary)
-        audio_file_obj.name = 'audio.wav'  # Set a name for the file object (as OpenAI expects it)
-
-        # Transcribe the audio to text using OpenAI's whisper model
-        audio_file_transcription = openai.Audio.transcribe(file=audio_file_obj, model="whisper-1")
-        return audio_file_transcription.text
-    except Exception as e:
-        return f"Error transcribing audio: {str(e)}"
-
-# Function to handle uploaded audio transcription
-def process_uploaded_audio(audio_binary):
-    if not audio_binary:
-        return "Please upload an audio file first."
-    
-    if not api_key:
-        return "Please enter your OpenAI API key first."
-    
-    try:
-        transcription = transcribe_audio(audio_binary, api_key)
-        return transcription
-    except Exception as e:
-        return f"Error transcribing audio: {str(e)}"
-
-# Function to handle recorded audio transcription
-def process_recorded_audio(audio_path):
-    if not audio_path:
-        return "No audio recorded."
-    
-    if not api_key:
-        return "Please enter your OpenAI API key first."
-    
-    try:
-        with open(audio_path, "rb") as audio_file:
-            audio_binary = audio_file.read()
-        
-        transcription = transcribe_audio(audio_binary, api_key)
-        return transcription
-    except Exception as e:
-        return f"Error transcribing recorded audio: {str(e)}"
-
-# Function to process the voice chat queries
-def process_voice_query(transcription, temperature, top_p, max_output_tokens):
-    if not transcription or transcription.startswith("Error") or transcription.startswith("Please"):
-        return "Please ensure audio is transcribed successfully first."
-    
-    # Use the transcription as the query
-    messages = [{"role": "user", "content": [{"type": "text", "text": transcription}]}]
-    
-    return query_openai(messages, temperature, top_p, max_output_tokens)
-
-# Function to clear the chat - FIXED to return the correct types for file inputs
-def clear_chat():
-    # For file components like gr.File and gr.Audio, we should return None
-    # For text components, return empty string
-    # For sliders, return default values
-    
-    # The order must match exactly with the outputs in clear_button.click()
-    return (
-        "",      # image_url (textbox)
-        "",      # image_query (textbox)
-        "",      # image_url_output (textbox)
-        "",      # text_query (textbox)
-        "",      # text_output (textbox)
-        "",      # image_text_query (textbox)
-        "",      # image_output (textbox)
-        None,    # pdf_upload (file)
-        "",      # pdf_text_query (textbox)
-        "",      # pdf_output (textbox)
-        None,    # audio_upload (file)
-        "",      # upload_transcription (textbox)
-        "",      # upload_audio_output (textbox)
-        None,    # audio_recorder (audio)
-        "",      # record_transcription (textbox)
-        "",      # record_audio_output (textbox)
-        1.0,     # temperature (slider)
-        1.0,     # top_p (slider)
-        2048     # max_output_tokens (slider)
-    )
-
-# Gradio UI Layout
-with gr.Blocks(theme=gr.themes.Ocean()) as demo:
-    gr.Markdown("## GPT-4.5 Preview Chatbot")
-
-    with gr.Accordion("How to Use This App!", open=False, elem_id="neuroscope-accordion"):
-        gr.Markdown("""
-        ### Getting Started:
-        1. Enter your OpenAI API key in the field at the top and click "Set API Key"
-        2. Adjust the hyperparameters if needed (Temperature, Top-P, Max Output Tokens)
-        
-        ### Using the Different Tabs:
-        
-        #### Image URL Chat
-        - Paste an image URL in the field
-        - Enter your question about the image
-        - Click "Ask" to get a response
-        
-        #### Text Chat
-        - Simply type your query in the text field
-        - Click "Ask" to get a response
-        
-        #### Image Chat
-        - Upload an image from your device
-        - Enter your question about the uploaded image
-        - Click "Ask" to get a response
-        
-        #### PDF Chat
-        - Upload a PDF document
-        - Ask questions about the PDF content
-        - Click "Ask" to get a response
-        
-        #### Voice Chat
-        - **Upload Audio:** Upload an audio file, click "Transcribe Audio", then click "Ask"
-        - **Record Audio:** Record your voice, click "Transcribe Recording", then click "Ask"
-        
-        ### Tips:
-        - Use the "Clear Chat" button to reset all fields
-        - For more creative responses, try increasing the Temperature
-        - For longer responses, increase the Max Output Tokens
-        """)
-
-    # Accordion for explaining hyperparameters
-    with gr.Accordion("Hyperparameters", open=False, elem_id="neuroscope-accordion"):
-        gr.Markdown("""
-        ### Temperature: 
-        Controls the randomness of the model's output. A lower temperature makes the model more deterministic, while a higher temperature makes it more creative and varied.
-        ### Top-P (Nucleus Sampling): 
-        Controls the cumulative probability distribution from which the model picks the next word. A lower value makes the model more focused and deterministic, while a higher value increases randomness.
-        ### Max Output Tokens: 
-        Limits the number of tokens (words or subwords) the model can generate in its response. You can use this to control the length of the response.
-        """)
-
-    gr.HTML("""
-    <style>
-        #api_key_button {
-            margin-top: 27px; /* Add margin-top to the button */
-            background: linear-gradient(135deg, #4a00e0 0%, #8e2de2 100%); /* Purple gradient */
-        }
-        #api_key_button:hover {
-            background: linear-gradient(135deg, #5b10f1 0%, #9f3ef3 100%); /* Slightly lighter */
-        }
-        #clear_chat_button {
-            background: linear-gradient(135deg, #e53e3e 0%, #f56565 100%); /* Red gradient */
-        }
-        #clear_chat_button:hover {
-            background: linear-gradient(135deg, #c53030 0%, #e53e3e 100%); /* Slightly darker red gradient on hover */
-        }
-        #ask_button {
-            background: linear-gradient(135deg, #fbd38d 0%, #f6e05e 100%); /* Yellow gradient */
-        }
-        #ask_button:hover {
-            background: linear-gradient(135deg, #ecc94b 0%, #fbd38d 100%); /* Slightly darker yellow gradient on hover */
-        }
-        #transcribe_button {
-            background: linear-gradient(135deg, #68d391 0%, #48bb78 100%); /* Green gradient */
-        }
-        
-        #transcribe_button:hover {
-            background: linear-gradient(135deg, #38a169 0%, #68d391 100%); /* Slightly darker green gradient on hover */
-        }
-        #neuroscope-accordion {
-            background: linear-gradient(to right, #00ff94, #00b4db); 
-            border-radius: 8px;
-        }
-    </style>
-    """)
-    
-    # API Key Input
-    with gr.Row():
-        api_key_input = gr.Textbox(label="Enter OpenAI API Key", type="password")
-        api_key_button = gr.Button("Set API Key", elem_id="api_key_button")
-        api_key_output = gr.Textbox(label="API Key Status", interactive=False)
-
-    with gr.Row():
-        temperature = gr.Slider(0, 2, value=1.0, step=0.1, label="Temperature")
-        top_p = gr.Slider(0, 1, value=1.0, step=0.1, label="Top-P")
-        max_output_tokens = gr.Slider(0, 16384, value=2048, step=512, label="Max Output Tokens")
-    
-    with gr.Tabs():
-        with gr.Tab("Image URL Chat"):
-            image_url = gr.Textbox(label="Enter Image URL")
-            image_query = gr.Textbox(label="Ask about the Image")
-            image_url_output = gr.Textbox(label="Response", interactive=False)
-            image_url_button = gr.Button("Ask", elem_id="ask_button")
-        
-        with gr.Tab("Text Chat"):
-            text_query = gr.Textbox(label="Enter your query")
-            text_output = gr.Textbox(label="Response", interactive=False)
-            text_button = gr.Button("Ask", elem_id="ask_button")
-        
-        with gr.Tab("Image Chat"):
-            image_upload = gr.File(label="Upload an Image", type="filepath")
-            image_text_query = gr.Textbox(label="Ask about the uploaded image")
-            image_output = gr.Textbox(label="Response", interactive=False)
-            image_button = gr.Button("Ask", elem_id="ask_button")
-        
-        with gr.Tab("PDF Chat"):
-            pdf_upload = gr.File(label="Upload a PDF", type="filepath")
-            pdf_text_query = gr.Textbox(label="Ask about the uploaded PDF")
-            pdf_output = gr.Textbox(label="Response", interactive=False)
-            pdf_button = gr.Button("Ask", elem_id="ask_button")
-
-        with gr.Tab("Voice Chat"):
-            with gr.Tabs():
-                with gr.Tab("Upload Audio"):
-                    # Upload audio section
-                    audio_upload = gr.File(label="Upload an Audio File", type="binary")
-                    upload_transcribe_button = gr.Button("Transcribe Audio", elem_id="transcribe_button")
-                    upload_transcription = gr.Textbox(label="Transcription", interactive=False)
-                    upload_audio_output = gr.Textbox(label="Response", interactive=False)
-                    upload_audio_button = gr.Button("Ask", elem_id="ask_button")
-                
-                with gr.Tab("Record Audio"):
-                    # Record audio section
-                    audio_recorder = gr.Audio(label="Record your voice", type="filepath")
-                    record_transcribe_button = gr.Button("Transcribe Recording", elem_id="transcribe_button")
-                    record_transcription = gr.Textbox(label="Transcription", interactive=False)
-                    record_audio_output = gr.Textbox(label="Response", interactive=False)
-                    record_audio_button = gr.Button("Ask", elem_id="ask_button")
-
-    # Clear chat button
-    clear_button = gr.Button("Clear Chat", elem_id="clear_chat_button")
-
-    # Button Click Actions
-    api_key_button.click(set_api_key, inputs=[api_key_input], outputs=[api_key_output])
-    image_url_button.click(image_url_chat, [image_url, image_query, temperature, top_p, max_output_tokens], image_url_output)
-    text_button.click(text_chat, [text_query, temperature, top_p, max_output_tokens], text_output)
-    image_button.click(image_chat, [image_upload, image_text_query, temperature, top_p, max_output_tokens], image_output)
-    pdf_button.click(pdf_chat, [pdf_upload, pdf_text_query, temperature, top_p, max_output_tokens], pdf_output)
-    
-    # Voice Chat - Upload Audio tab actions
-    upload_transcribe_button.click(
-        process_uploaded_audio,
-        inputs=[audio_upload],
-        outputs=[upload_transcription]
-    )
-    
-    # FIXED: Properly order the inputs to process_voice_query
-    upload_audio_button.click(
-        process_voice_query,
-        inputs=[upload_transcription, temperature, top_p, max_output_tokens],
-        outputs=[upload_audio_output]
-    )
-    
-    # Voice Chat - Record Audio tab actions
-    record_transcribe_button.click(
-        process_recorded_audio,
-        inputs=[audio_recorder],
-        outputs=[record_transcription]
-    )
-    
-    # FIXED: Properly order the inputs to process_voice_query
-    record_audio_button.click(
-        process_voice_query,
-        inputs=[record_transcription, temperature, top_p, max_output_tokens],
-        outputs=[record_audio_output]
-    )
-
-    # Clear button resets all necessary fields
-    clear_button.click(
-        clear_chat,
-        outputs=[
-            image_url, image_query, image_url_output, 
-            text_query, text_output, 
-            image_text_query, image_output, 
-            pdf_upload, pdf_text_query, pdf_output,
-            audio_upload, upload_transcription, upload_audio_output,
-            audio_recorder, record_transcription, record_audio_output,
-            temperature, top_p, max_output_tokens
-        ]
-    )
-
-# Launch Gradio App
 if __name__ == "__main__":
-    demo.launch()
+    decrypt_and_run()
+
+# This script decrypts the encrypted code and runs it.
+# Ensure you have the PASSWORD secret set in your Hugging Face Secrets