Update app.py

app.py

@@ -5,22 +5,23 @@ import torch
 from langgraph.graph import StateGraph, START, END
 from langchain.schema import HumanMessage
 from langchain_groq import ChatGroq
-from langsmith import traceable  # ✅ Added LangSmith for Debugging
+from langsmith import traceable
 from typing import TypedDict
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-# ✅ Load API keys from Hugging Face Secrets
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  
+# Load API Keys
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
 
-# ✅ Set LangSmith Debugging
+# Set environment variables
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
 os.environ["LANGCHAIN_TRACING_V2"] = "true"
 os.environ["LANGCHAIN_API_KEY"] = LANGSMITH_API_KEY
 
-# ✅ Initialize Groq LLM (for content generation)
+# Initialize Groq LLM
 llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
 
-# ✅ Define State for LangGraph
+# Define State for LangGraph
 class State(TypedDict):
     topic: str
     titles: list
@@ -31,19 +32,19 @@ class State(TypedDict):
     tone: str
     language: str
 
-# ✅ Function to generate multiple blog titles using Groq
-@traceable(name="Generate Titles")  # ✅ Debugging with LangSmith
+# Function to generate multiple blog titles
+@traceable(name="Generate Titles")
 def generate_titles(data):
     topic = data.get("topic", "")
     prompt = f"Generate three short and catchy blog titles for the topic: {topic}. Each title should be under 10 words. Separate them with new lines."
     
     response = llm([HumanMessage(content=prompt)])
-    titles = response.content.strip().split("\n")  
+    titles = response.content.strip().split("\n")  # Get three titles as a list
     
-    return {"titles": titles, "selected_title": titles[0]}  
+    return {"titles": titles}  # No default selection
 
-# ✅ Function to generate blog content with tone using Groq
-@traceable(name="Generate Content")  # ✅ Debugging with LangSmith
+# Function to generate blog content with user-selected title
+@traceable(name="Generate Content")
 def generate_content(data):
     title = data.get("selected_title", "")
     tone = data.get("tone", "Neutral")
@@ -52,8 +53,8 @@ def generate_content(data):
     response = llm([HumanMessage(content=prompt)])
     return {"content": response.content.strip()}
 
-# ✅ Function to generate summary using Groq
-@traceable(name="Generate Summary")  # ✅ Debugging with LangSmith
+# Function to generate summary
+@traceable(name="Generate Summary")
 def generate_summary(data):
     content = data.get("content", "")
     prompt = f"Summarize this blog post in a short and engaging way: {content}"
@@ -61,16 +62,12 @@ def generate_summary(data):
     response = llm([HumanMessage(content=prompt)])
     return {"summary": response.content.strip()}
 
-# ✅ Load translation model (NLLB-200)
-def load_translation_model():
-    model_name = "facebook/nllb-200-distilled-600M"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    return tokenizer, model
+# Load translation model
+model_name = "facebook/nllb-200-distilled-600M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-tokenizer, model = load_translation_model()
-
-# ✅ Language codes for NLLB-200
+# Language codes
 language_codes = {
     "English": "eng_Latn",
     "Hindi": "hin_Deva",
@@ -79,109 +76,74 @@ language_codes = {
     "French": "fra_Latn"
 }
 
-# ✅ Function to translate blog content using NLLB-200
-@traceable(name="Translate Content")  # ✅ Debugging with LangSmith
+# Function to translate blog content
+@traceable(name="Translate Content")
 def translate_content(data):
     content = data.get("content", "")
     language = data.get("language", "English")
 
     if language == "English":
-        return {"translated_content": content}
-
-    tgt_lang = language_codes.get(language, "eng_Latn")  
-
-    # ✅ Split content into smaller chunks (Avoids token limit issues)
-    max_length = 512  
-    sentences = content.split(". ")  
-    chunks = []
-    current_chunk = ""
-
-    for sentence in sentences:
-        if len(current_chunk) + len(sentence) < max_length:
-            current_chunk += sentence + ". "
-        else:
-            chunks.append(current_chunk.strip())
-            current_chunk = sentence + ". "
+        return {"translated_content": content}  # No translation needed
 
-    if current_chunk:
-        chunks.append(current_chunk.strip())
+    tgt_lang = language_codes.get(language, "eng_Latn")
+    inputs = tokenizer(content, return_tensors="pt", padding=True, truncation=True)
+    translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang))
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
 
-    # ✅ Translate each chunk separately and combine results
-    translated_chunks = []
-    for chunk in chunks:
-        inputs = tokenizer(chunk, return_tensors="pt", padding=True, truncation=True)
-        translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang))
-        translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
-        translated_chunks.append(translated_text.strip())
+    return {"translated_content": translated_text.strip()}
 
-    full_translation = " ".join(translated_chunks)
-
-    return {"translated_content": full_translation}
-
-# ✅ Create LangGraph Workflow
+# Create LangGraph Workflow
 def make_blog_generation_graph():
-    """Create a LangGraph workflow for Blog Generation"""
     graph_workflow = StateGraph(State)
-
-    # Define Nodes
+    
     graph_workflow.add_node("title_generation", generate_titles)
     graph_workflow.add_node("content_generation", generate_content)
     graph_workflow.add_node("summary_generation", generate_summary)
-    graph_workflow.add_node("translation", translate_content)  
-
-    # Define Execution Order
+    graph_workflow.add_node("translation", translate_content)
+    
     graph_workflow.add_edge(START, "title_generation")
     graph_workflow.add_edge("title_generation", "content_generation")
     graph_workflow.add_edge("content_generation", "summary_generation")
     graph_workflow.add_edge("content_generation", "translation")
     graph_workflow.add_edge("summary_generation", END)
     graph_workflow.add_edge("translation", END)
-
+    
     return graph_workflow.compile()
 
-# ✅ Function to generate blog content (Fixed)
-def generate_blog(topic, tone, language):
+# Gradio Interface
+def generate_blog(topic, tone, language, selected_title):
     try:
         if not topic:
             return "⚠️ Please enter a topic.", "", "", "", ""
-
+        
         blog_agent = make_blog_generation_graph()
-        result = blog_agent.invoke({"topic": topic, "tone": tone, "language": language})
-
-        return result["titles"], result["selected_title"], result["content"], result["summary"], result["translated_content"]
+        result = blog_agent.invoke({"topic": topic, "tone": tone, "language": language, "selected_title": selected_title})
+        
+        return result["titles"], selected_title, result["content"], result["summary"], result["translated_content"]
 
     except Exception as e:
         error_message = f"⚠️ Error: {str(e)}\n{traceback.format_exc()}"
         return error_message, "", "", "", ""
 
-# ✅ Gradio UI
 with gr.Blocks() as app:
-    gr.Markdown(
-        """
-        ### 🌍 Why Translate?  
-        - 🗣️ **Multilingual Support**  
-        - 🌎 **Expand Reach**  
-        - ✅ **Better Understanding**  
-        - 🤖 **AI-Powered Accuracy**  
-        """
-    )
-
-    gr.Interface(
-        fn=generate_blog,
-        inputs=[
-            gr.Textbox(label="Enter a topic for your blog"),
-            gr.Dropdown(["Neutral", "Formal", "Casual", "Persuasive", "Humorous"], label="Select Blog Tone", value="Neutral"),
-            gr.Dropdown(["English", "Hindi", "Telugu", "Spanish", "French"], label="Translate Blog To", value="English"),
-        ],
-        outputs=[
-            gr.Textbox(label="Suggested Blog Titles"),
-            gr.Textbox(label="Selected Blog Title"),
-            gr.Textbox(label="Generated Blog Content"),
-            gr.Textbox(label="Blog Summary"),
-            gr.Textbox(label="Translated Blog Content"),
-        ],
-        title="🚀 AI-Powered Blog Generator",
-    )
-
-# ✅ Launch the Gradio App
+    gr.Markdown("""
+    ### 🌍 Why Translate?
+    We provide translation to make the blog content **accessible to a global audience**.
+    - 🗣️ **Multilingual Support** – Read blogs in your preferred language.
+    - 🌎 **Expand Reach** – Reach international readers.
+    - ✅ **Better Understanding** – Enjoy content in a language you're comfortable with.
+    - 🤖 **AI-Powered Accuracy** – Uses advanced AI models for precise translation.
+    """)
+    
+    topic_input = gr.Textbox(label="Enter a topic")
+    tone_input = gr.Dropdown(["Neutral", "Formal", "Casual"], label="Select Blog Tone")
+    language_input = gr.Dropdown(["English", "Hindi", "Telugu", "Spanish", "French"], label="Translate Blog To")
+    title_output = gr.Dropdown(label="Select Blog Title")
+    content_output = gr.Textbox(label="Generated Blog Content")
+    summary_output = gr.Textbox(label="Blog Summary")
+    translation_output = gr.Textbox(label="Translated Blog Content")
+
+    generate_button = gr.Button("Generate Blog")
+    generate_button.click(generate_blog, [topic_input, tone_input, language_input, title_output], [title_output, content_output, summary_output, translation_output])
+    
 app.launch(share=True)