Create app.py

app.py

@@ -0,0 +1,187 @@
+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  # ✅ Added LangSmith for Debugging
+from typing import TypedDict
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# ✅ Load API keys from Hugging Face Secrets
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")  
+LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
+
+# ✅ Set LangSmith Debugging
+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")  # ✅ Debugging with LangSmith
+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")  
+    
+    return {"titles": titles, "selected_title": titles[0]}  
+
+# ✅ Function to generate blog content with tone using Groq
+@traceable(name="Generate Content")  # ✅ Debugging with LangSmith
+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")  # ✅ Debugging with LangSmith
+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"
+    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")  # ✅ Debugging with LangSmith
+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 + ". "
+
+    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())
+
+    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)  
+
+    # 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")
+    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):
+    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"]
+
+    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
+app.launch(share=True)