Update app.py

app.py

@@ -1,8 +1,40 @@
-gradio==3.28.0
-transformers==4.34.0
-torch==2.1.0
-stable-diffusion-v1-4==1.0
-groq==0.2.0  # Adjust this version according to the Groq API version you're using
-requests==2.31.0
-Pillow==10.0.0  # For image handling
-python-dotenv==1.0.0  # For managing environment variables
+import os
+from gradio import Interface
+from PIL import Image
+from groq import Groq
+from diffusers import StableDiffusionPipeline
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Groq API setup
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Stable Diffusion setup using Hugging Face's diffusers library
+model = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+
+# Function to generate an image and interact with Groq's API
+def generate_and_interact(prompt):
+    # Step 1: Generate image using Stable Diffusion
+    image = model(prompt).images[0]
+    
+    # Step 2: Save the generated image (optional, for further use)
+    image_path = "generated_image.png"
+    image.save(image_path)
+
+    # Step 3: Interact with Groq API (example: send metadata or a description of the image)
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": f"Analyze the generated image from this prompt: {prompt}"}],
+        model="llama3-8b-8192",
+        stream=False,
+    )
+    
+    # Return the Groq response and the generated image
+    return chat_completion.choices[0].message.content, image
+
+# Gradio interface setup
+iface = Interface(fn=generate_and_interact, inputs="text", outputs=["text", "image"])
+
+# Launch the app
+iface.launch()