Update app.py

app.py

@@ -1,179 +1,178 @@
-import os
-import gradio as gr
-import traceback
-import torch
-from langgraph.graph import StateGraph, START, END
-from langchain.schema import HumanMessage
-from langchain_groq import ChatGroq
-from langsmith import traceable
-from typing import TypedDict
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-from google.colab import userdata  # Only needed in Google Colab
-
-import os
-
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Get from Hugging Face secrets
-LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
-
-
-# ✅ 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)
-llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
-
-# ✅ Define State for LangGraph
-class State(TypedDict):
-    topic: str
-    titles: list
-    selected_title: str
-    content: str
-    summary: str
-    translated_content: str
-    tone: str
-    language: str
-
-# ✅ Function to generate multiple blog titles using Groq
-@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")  # Get three titles as a list
-    
-    return {"titles": titles, "selected_title": titles[0]}  # Default to first title
-
-# ✅ Function to generate blog content with tone using Groq
-@traceable(name="Generate Content")
-def generate_content(data):
-    title = data.get("selected_title", "")
-    tone = data.get("tone", "Neutral")
-    prompt = f"Write a detailed and engaging blog post in a {tone} tone based on the title: {title}"
-    
-    response = llm([HumanMessage(content=prompt)])
-    return {"content": response.content.strip()}
-
-# ✅ Function to generate summary using Groq
-@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}"
-    
-    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"  # Efficient model for 200+ languages
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    return tokenizer, model
-
-tokenizer, model = load_translation_model()
-
-# ✅ Language codes for NLLB-200
-language_codes = {
-    "English": "eng_Latn",
-    "Hindi": "hin_Deva",
-    "Telugu": "tel_Telu",
-    "Spanish": "spa_Latn",
-    "French": "fra_Latn"
-}
-
-# ✅ Function to translate blog content using NLLB-200
-@traceable(name="Translate Content")
-def translate_content(data):
-    content = data.get("content", "")
-    language = data.get("language", "English")
-
-    if language == "English":
-        return {"translated_content": content}  # No translation needed
-
-    tgt_lang = language_codes.get(language, "eng_Latn")  # Default to English if not found
-
-    # ✅ Split content into smaller chunks (Avoids token limit issues)
-    max_length = 512  # Adjust based on model limitations
-    sentences = content.split(". ")  # Split at sentence level
-    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 + ". "
-
-    if current_chunk:
-        chunks.append(current_chunk.strip())
-
-    # ✅ 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())
-
-    # ✅ Combine all translated chunks into final text
-    full_translation = " ".join(translated_chunks)
-
-    return {"translated_content": full_translation}
-
-# ✅ 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)  # Ensures only blog content is translated
-
-    # Define Execution Order
-    graph_workflow.add_edge(START, "title_generation")
-    graph_workflow.add_edge("title_generation", "content_generation")
-    graph_workflow.add_edge("content_generation", "summary_generation")  # Summary only generated from content
-    graph_workflow.add_edge("content_generation", "translation")  # Translation happens for content only
-    graph_workflow.add_edge("summary_generation", END)
-    graph_workflow.add_edge("translation", END)
-
-    return graph_workflow.compile()
-
-# ✅ Gradio Interface with "Why Translate?" Section
-with gr.Blocks() as 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.  
-        """
-    )
-
-    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 (Choose One)"),  # Displays multiple title suggestions
-            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 with Multi-Title Suggestions",
-        description="Generate high-quality blogs using Groq AI, customize tone, translate using NLLB-200, and get interactive summaries. Select from multiple title suggestions!",
-    )
-
-# ✅ Launch the Gradio App
-app.launch(share=True)
+import os
+import gradio as gr
+import traceback
+import torch
+from langgraph.graph import StateGraph, START, END
+from langchain.schema import HumanMessage
+from langchain_groq import ChatGroq
+from langsmith import traceable
+from typing import TypedDict
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+import os
+
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Get from Hugging Face secrets
+LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
+
+
+# ✅ 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)
+llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
+
+# ✅ Define State for LangGraph
+class State(TypedDict):
+    topic: str
+    titles: list
+    selected_title: str
+    content: str
+    summary: str
+    translated_content: str
+    tone: str
+    language: str
+
+# ✅ Function to generate multiple blog titles using Groq
+@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")  # Get three titles as a list
+    
+    return {"titles": titles, "selected_title": titles[0]}  # Default to first title
+
+# ✅ Function to generate blog content with tone using Groq
+@traceable(name="Generate Content")
+def generate_content(data):
+    title = data.get("selected_title", "")
+    tone = data.get("tone", "Neutral")
+    prompt = f"Write a detailed and engaging blog post in a {tone} tone based on the title: {title}"
+    
+    response = llm([HumanMessage(content=prompt)])
+    return {"content": response.content.strip()}
+
+# ✅ Function to generate summary using Groq
+@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}"
+    
+    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"  # Efficient model for 200+ languages
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_translation_model()
+
+# ✅ Language codes for NLLB-200
+language_codes = {
+    "English": "eng_Latn",
+    "Hindi": "hin_Deva",
+    "Telugu": "tel_Telu",
+    "Spanish": "spa_Latn",
+    "French": "fra_Latn"
+}
+
+# ✅ Function to translate blog content using NLLB-200
+@traceable(name="Translate Content")
+def translate_content(data):
+    content = data.get("content", "")
+    language = data.get("language", "English")
+
+    if language == "English":
+        return {"translated_content": content}  # No translation needed
+
+    tgt_lang = language_codes.get(language, "eng_Latn")  # Default to English if not found
+
+    # ✅ Split content into smaller chunks (Avoids token limit issues)
+    max_length = 512  # Adjust based on model limitations
+    sentences = content.split(". ")  # Split at sentence level
+    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 + ". "
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    # ✅ 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())
+
+    # ✅ Combine all translated chunks into final text
+    full_translation = " ".join(translated_chunks)
+
+    return {"translated_content": full_translation}
+
+# ✅ 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)  # Ensures only blog content is translated
+
+    # Define Execution Order
+    graph_workflow.add_edge(START, "title_generation")
+    graph_workflow.add_edge("title_generation", "content_generation")
+    graph_workflow.add_edge("content_generation", "summary_generation")  # Summary only generated from content
+    graph_workflow.add_edge("content_generation", "translation")  # Translation happens for content only
+    graph_workflow.add_edge("summary_generation", END)
+    graph_workflow.add_edge("translation", END)
+
+    return graph_workflow.compile()
+
+# ✅ Gradio Interface with "Why Translate?" Section
+with gr.Blocks() as 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.  
+        """
+    )
+
+    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 (Choose One)"),  # Displays multiple title suggestions
+            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 with Multi-Title Suggestions",
+        description="Generate high-quality blogs using Groq AI, customize tone, translate using NLLB-200, and get interactive summaries. Select from multiple title suggestions!",
+    )
+
+# ✅ Launch the Gradio App
+app.launch(share=True)