app.py

import os
import gradio as gr
from langchain_google_genai.chat_models import ChatGoogleGenerativeAI  # Import for Gemini
from PIL import Image
import torch
from torchvision import models, transforms
import json
import requests

# Load credentials (stringified JSON) from environment variable
credentials_string = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS")
if not credentials_string:
    raise ValueError("GOOGLE_APPLICATION_CREDENTIALS is not set in the environment!")

# Parse the stringified JSON back to a Python dictionary
credentials = json.loads(credentials_string)

# Save the credentials to a temporary JSON file (required by Google SDKs)
with open("service_account.json", "w") as f:
    json.dump(credentials, f)

# Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the temporary file
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "service_account.json"

# Initialize Gemini model
llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro')

# Load a pre-trained ResNet50 model for image classification
model = models.resnet50(pretrained=True)
model.eval()

# Transformation pipeline for image preprocessing
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load ImageNet labels
LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
labels = json.loads(requests.get(LABELS_URL).text)

# Global chat history variable
chat_history = []

def chat_with_gemini(message):
    global chat_history
    # Get a response from the language model
    bot_response = llm.predict(message)  # This will interact with the Gemini model
    chat_history.append((message, bot_response))
    return chat_history

def analyze_image(image_path):
    global chat_history
    # Open, preprocess, and classify the image
    image = Image.open(image_path).convert("RGB")
    image_tensor = transform(image).unsqueeze(0)

    with torch.no_grad():
        outputs = model(image_tensor)
        _, predicted_idx = outputs.max(1)
    
    label = labels[predicted_idx.item()]
    bot_response = f"The image seems to be: {label}."
    chat_history.append(("Uploaded an image for analysis", bot_response))
    return chat_history

# Build the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Ken Chatbot")
    gr.Markdown("Ask me anything or upload an image for analysis!")

    # Chatbot display without "User" or "Bot" labels
    chatbot = gr.Chatbot(elem_id="chatbot")

    # User input components
    msg = gr.Textbox(label="Type your message here...", placeholder="Enter your message...", show_label=False)
    send_btn = gr.Button("Send")
    img_upload = gr.Image(type="filepath", label="Upload an image for analysis")

    # Define interactions
    def handle_text_message(message):
        return chat_with_gemini(message)

    def handle_image_upload(image_path):
        return analyze_image(image_path)

    # Set up Gradio components with Enter key for sending
    msg.submit(handle_text_message, msg, chatbot)
    send_btn.click(handle_text_message, msg, chatbot)
    send_btn.click(lambda: "", None, msg)  # Clear input field
    img_upload.change(handle_image_upload, img_upload, chatbot)

    # Custom CSS for styling without usernames
    gr.HTML("""
    <style>
    #chatbot .message-container {
        display: flex;
        flex-direction: column;
        margin-bottom: 10px;
        max-width: 70%;
    }
    #chatbot .message {
        border-radius: 15px;
        padding: 10px;
        margin: 5px 0;
        word-wrap: break-word;
    }
    #chatbot .message.user {
        background-color: #DCF8C6;
        margin-left: auto;
        text-align: right;
    }
    #chatbot .message.bot {
        background-color: #E1E1E1;
        margin-right: auto;
        text-align: left;
    }
    </style>
    """)

# Launch for Hugging Face Spaces
demo.launch()