Converted the application to mutlimodel. Generate Image through OpenAI for the blog.

app.py

@@ -10,6 +10,7 @@ from langgraph.constants import Send
 import operator
 from langchain_core.messages import SystemMessage, HumanMessage
 from langsmith import traceable
+from openai import OpenAI
 
 # Load environment variables
 load_dotenv()
@@ -17,9 +18,13 @@ os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
 os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
 os.environ['LANGSMITH_TRACING_V2'] = 'true'
 os.environ['LANGCHAIN_PROJECT_NAME'] = os.getenv('LANGCHAIN_PROJECT_NAME')
+os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
 
 # Initialize LLM
-llm = ChatGroq(model='llama3-70b-8192')
+llm = ChatGroq(model='gemma2-9b-it')
+
+# Initialize OpenAI client for DALL·E
+client = OpenAI()
 
 # Define section structure
 class Section(BaseModel):
@@ -43,11 +48,35 @@ class BlogState(TypedDict):
     finalize_blog: str
     step: str
     final_blog: str
+    image_urls: list  
+    fallback_links: list 
 
 class BlogStateSection(TypedDict):
     section: Section
     completed_sections: Annotated[list, operator.add]
 
+# Function to generate an image using DALL·E
+def generate_image_with_dalle(prompt: str):
+    try:
+        response = client.images.generate(
+            model="dall-e-3",
+            prompt=prompt,
+            size="1024x1024",
+            quality="hd",
+            n=1,
+        )
+        image_url = response.data[0].url
+        return image_url
+    except Exception as e:
+        st.error(f"Failed to generate image: {e}")
+        return None
+
+# Function to provide a fallback link for image search
+def get_fallback_image_link(topic: str):
+    # Provide a Google Images search link for the topic
+    search_query = topic.replace(" ", "+")
+    return f"https://www.google.com/search?q={search_query}&tbm=isch"
+
 # Orchestrator node to generate an outline
 @traceable
 def generate_outline(state: BlogState):
@@ -68,6 +97,28 @@ def write_section(state: BlogStateSection):
     ])
     return {"completed_section": [section_content.content]}
 
+# Function to generate an image using DALL·E
+@traceable
+def generate_image(state: BlogState):
+    st.write("Generating an image for the section...")
+    if not state.get('completed_section'):
+        st.warning("No completed sections found to generate an image.")
+        return {"image_urls": state.get('image_urls', []), "fallback_links": state.get('fallback_links', [])}
+    
+    section = state['topic']
+    prompt = f"Generate an image for the blog section: {section} with no text. More of a representation and informative image"
+    
+    image_url = generate_image_with_dalle(prompt)
+    if image_url:
+        image_urls = state.get('image_urls', [])
+        image_urls.append(image_url)
+        return {"image_urls": image_urls, "fallback_links": state.get('fallback_links', [])}
+    else:
+        fallback_links = state.get('fallback_links', [])
+        fallback_link = get_fallback_image_link(state['topic'])
+        fallback_links.append(fallback_link)
+        return {"image_urls": state.get('image_urls', []), "fallback_links": fallback_links}
+
 # Review node to check the quality of sections
 @traceable
 def review_section(state: BlogState):
@@ -91,6 +142,10 @@ def review_section(state: BlogState):
 def revise_section(state: BlogState):
     st.write("Revising the section content...")
     if state['step'] == "revise_section_content":
+        if not state.get('sections'):
+            st.warning("No sections found to revise.")
+            return {"completed_section": state['completed_section']}
+        
         revised_content = llm.invoke([
             SystemMessage(content="Based on the review feedback, improve the content further."),
             HumanMessage(content=f"Section Name: {state['sections'][0].section_name}, Description: {state['sections'][0].description}")
@@ -101,6 +156,9 @@ def revise_section(state: BlogState):
 @traceable
 def assign_writers(state: BlogState):
     st.write("Assigning writers to sections...")
+    if not state.get('outline'):
+        st.warning("No outline found to assign writers.")
+        return []
     return [Send('write_section', {'section': s}) for s in state['outline']]
 
 # Decision function for routing after review
@@ -118,7 +176,21 @@ def seo_optimization(state: BlogState):
 @traceable
 def publish_blog(state: BlogState):
     st.write("Finalizing and publishing the blog...")
-    return {"final_blog": state['finalize_blog']}
+    final_blog = state['finalize_blog']
+    
+    # Add images to the blog
+    if state.get('image_urls'):
+        st.subheader("AI-Generated Images")
+        for image_url in state['image_urls']:
+            st.image(image_url, caption="AI-Generated Image")
+    
+    # Add fallback links if images were not generated
+    if state.get('fallback_links'):
+        st.subheader("Fallback Image Search Links")
+        for link in state['fallback_links']:
+            st.markdown(f"[Search for related images on Google]({link})")
+    
+    return {"final_blog": final_blog}
 
 # Build LangGraph workflow
 builder = StateGraph(BlogState)
@@ -128,6 +200,7 @@ builder.add_node('generate_outline', generate_outline)
 
 # Add worker and review nodes
 builder.add_node('write_section', write_section)
+builder.add_node('generate_image', generate_image)
 builder.add_node('review_section', review_section)
 builder.add_node('revise_section', revise_section)
 
@@ -138,7 +211,8 @@ builder.add_node('publish_blog', publish_blog)
 # Define workflow edges
 builder.add_edge(START, 'generate_outline')
 builder.add_conditional_edges('generate_outline', assign_writers, ['write_section'])
-builder.add_edge('write_section', 'review_section')
+builder.add_edge('write_section', 'generate_image')
+builder.add_edge('generate_image', 'review_section')
 builder.add_conditional_edges('review_section', should_revise, {'revise_section_content': 'revise_section', 'send_seo_optimization': 'seo_optimization'})
 builder.add_edge('revise_section', 'review_section')  # Loop back after revision
 builder.add_edge('seo_optimization', 'publish_blog')
@@ -167,7 +241,9 @@ def main():
                 'revise_section_content': [],
                 'finalize_blog': "",
                 'step': "",
-                'final_blog': ""
+                'final_blog': "",
+                'image_urls': [],  # Initialize image URLs list
+                'fallback_links': []  # Initialize fallback links list
             }
             
             # Invoke workflow

requirements.txt

@@ -1,8 +1,9 @@
 langchain
 langgraph
-langchain-openai
 langsmith
 streamlit
-langgraph
 langchain_groq
 dotenv
+langchain_openai
+langchain_community
+faiss-cpu