Update app.py

app.py

@@ -2,16 +2,21 @@ import streamlit as st
 from g4f.client import Client
 import sqlite3
 import google.generativeai as genai
-from diffusers import DiffusionPipeline
-import matplotlib.pyplot as plt
-import torch
 # import pyttsx3
 # import pyperclip
+import requests
+import cv2
+import numpy as np
+
+
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+headers = {"Authorization": "Bearer Your_hugging_face_Api_key"}
 
 def local_css(file_name):
     with open(file_name) as f:
         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
 
+
 local_css("style.css")
 
 # Create a connection to the database
@@ -25,24 +30,16 @@ try:
     conn.commit()
 except Exception as e:
     st.error(f"An error occurred: {e}")
-    
-
-def generate_image(pipe, prompt, params):
-  img = pipe(prompt, **params).images
 
-  num_images = len(img)
-  if num_images>1:
-    fig, ax = plt.subplots(nrows=1, ncols=num_images)
-    for i in range(num_images):
-      ax[i].imshow(img[i]);
-      ax[i].axis('off');
+def generate_image_from_model(prompt):
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    image_bytes = response.content
+    # Convert image bytes to a NumPy array
+    nparr = np.frombuffer(image_bytes, np.uint8)
+    # Decode the image array
+    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    return image
 
-  else:
-    fig = plt.figure()
-    plt.imshow(img[0]);
-    plt.axis('off');
-  plt.tight_layout()
-    
 
 # Streamlit app
 def main():
@@ -57,7 +54,7 @@ def main():
             "🚀 Airoboros 70B": "airoboros-70b",
             "🔮 Gemini Pro": "gemini-pro",
             "📷 StabilityAI": "stabilityai/stable-diffusion-xl-base-1.0"
-            
+
         }
 
         columns = st.columns(3)  # Split the layout into three columns
@@ -101,7 +98,7 @@ def main():
         if user_input:
             if selected_model == "gemini-pro":
                 try:
-                    GOOGLE_API_KEY = "Gemini"
+                    GOOGLE_API_KEY = "your_Gemini_Api_key"
                     genai.configure(api_key=GOOGLE_API_KEY)
                     model = genai.GenerativeModel('gemini-pro')
                     prompt = user_input
@@ -127,14 +124,15 @@ def main():
 
                 except Exception as e:
                     st.error(f"An error occurred: {e}")
-                    
+
             elif selected_model == "stabilityai/stable-diffusion-xl-base-1.0":
-                pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float32, use_safetensors=True, variant="fp16")
-                pipe.to("cpu")
+                prompt = user_input
+                generated_image = generate_image_from_model(prompt)
+                # Display the image using OpenCV
+                cv2.imshow("Generated Image", generated_image)
+                cv2.waitKey(0)
+                cv2.destroyAllWindows()
 
-                params = {'num_inference_steps': 100, 'num_images_per_prompt': 2}
-                generate_image(pipe, user_input, params)
-                
             else:
                 try:
                     client = Client()
@@ -170,6 +168,7 @@ def main():
     except Exception as e:
         st.error(f"An error occurred: {e}")
 
+
 def display_conversation(conversation_id):
     c.execute("SELECT * FROM chat_history WHERE conversation_id=?", (conversation_id,))
     chats = c.fetchall()
@@ -178,5 +177,6 @@ def display_conversation(conversation_id):
         st.markdown(f"{chat[1]}")
         st.markdown(f"{chat[2]}")
 
+
 if __name__ == "__main__":
     main()