Create app.py

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Load the text model
+@st.cache_resource
+def load_text_model():
+    model_name = "meta-llama/Llama-2-7b-chat-hf"
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,  
+        bnb_4bit_compute_dtype=torch.float16,  
+        bnb_4bit_use_double_quant=True
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, 
+        quantization_config=bnb_config,  
+        device_map="auto"
+    )
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Load the image model
+@st.cache_resource
+def load_image_model():
+    model_id = "runwayml/stable-diffusion-v1-5"
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+    pipe.to("cuda")  # Use GPU
+    return pipe
+
+llama_pipe = load_text_model()
+image_pipe = load_image_model()
+
+# Streamlit UI
+st.title("🚀 Blog & Image Generator")
+
+topic = st.text_input("Enter a blog topic:", "The Future of AI in Healthcare")
+
+if st.button("Generate Blog"):
+    with st.spinner("Generating Blog..."):
+        blog = llama_pipe(f"Write a detailed blog on {topic}:", max_length=800, truncation=True)[0]['generated_text']
+        st.subheader("Generated Blog")
+        st.write(blog)
+
+        # Extract first 3 lines for image keywords
+        keywords = " ".join(blog.split("\n")[:3])
+
+        # Generate Image
+        with st.spinner("Generating Image..."):
+            image = image_pipe(keywords).images[0]
+            st.subheader("Generated Image")
+            st.image(image, caption="AI-Generated Image", use_column_width=True)
+