Update app.py

app.py

@@ -1,75 +1,35 @@
-import streamlit as st
-from transformers import pipeline
-from langchain_huggingface import HuggingFacePipeline
-import requests
-from bs4 import BeautifulSoup
-from dotenv import load_dotenv
-from time import sleep
-from dataclasses import dataclass
-from typing import List
+import gradio as gr
+from agent import graph, State
 
-load_dotenv()
+def generate_post(topic, platform):
+    initial_state: State = {
+        "topic": topic,
+        "platform": platform
+    }
+    final_state = graph.run(initial_state)
 
-# Setup HuggingFace pipeline
-hf_pipeline = pipeline(
-    "text2text-generation",
-    model="google/flan-t5-small",
-    max_length=512,
-    temperature=0.7,
-)
-model = HuggingFacePipeline(pipeline=hf_pipeline)
+    post = final_state.get("post", "No post generated")
+    engagement = final_state.get("engagement_score", "N/A")
+    tone = final_state.get("tone_score", "N/A")
+    clarity = final_state.get("clarity_score", "N/A")
+    images = "\n".join(final_state.get("image_url", [])) or "No images found"
 
-@dataclass
-class Command:
-    update: dict = None
-    goto: str = None
+    return post, engagement, tone, clarity, images
 
-def scrape_startpage(query: str, max_results: int = 3) -> List[dict]:
-    """Scrape search results from Startpage."""
-    url = f"https://www.startpage.com/sp/search?query={query.replace(' ', '+')}"
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-    for attempt in range(3):
-        try:
-            response = requests.get(url, headers=headers, timeout=10)
-            response.raise_for_status()
-            soup = BeautifulSoup(response.text, "html.parser")
-            results = []
-            for result in soup.find_all("div", class_="result")[:max_results]:
-                title = result.find("h3") or result.find("a")
-                snippet = result.find("p", class_="desc")
-                title_text = title.get_text().strip() if title else "No title"
-                snippet_text = snippet.get_text().strip() if snippet else "No snippet"
-                results.append({"title": title_text, "snippet": snippet_text})
-            return results
-        except Exception as e:
-            print(f"Error: {str(e)}")
-            sleep(2 ** attempt)
-            continue
-    return []
+with gr.Blocks() as demo:
+    gr.Markdown("# Social Media Post Generator for B2B Banking")
 
-def generate_post(platform, topic, search_results):
-    prompt = f"""
-    You are a social media strategist for a B2B bank. Generate a {platform} post.
-    The post should:
-    - Be engaging but professional.
-    - Provide value to corporate clients.
-    - Focus on {topic}.
-    - Incorporate information from {search_results if search_results else "general knowledge"}.
-    Output as plain text.
-    """
-    return model.invoke(prompt)
+    topic = gr.Textbox(label="Topic", value="digital transformation in banking")
+    platform = gr.Dropdown(choices=["LinkedIn", "Twitter", "Facebook"], value="LinkedIn", label="Platform")
 
-# Streamlit UI
-st.title("Social Media Post Generator for B2B Bank")
+    btn = gr.Button("Generate Post")
+    output_post = gr.Textbox(label="Generated Post", lines=10)
+    output_engagement = gr.Textbox(label="Engagement Score")
+    output_tone = gr.Textbox(label="Tone Score")
+    output_clarity = gr.Textbox(label="Clarity Score")
+    output_images = gr.Textbox(label="Image URLs")
 
-platform = st.selectbox("Select Platform", ["LinkedIn", "Twitter", "Facebook"])
-topic = st.text_input("Enter Topic", "digital transformation in banking")
+    btn.click(generate_post, inputs=[topic, platform],
+              outputs=[output_post, output_engagement, output_tone, output_clarity, output_images])
 
-if st.button("Generate Post"):
-    with st.spinner("Scraping data and generating post..."):
-        results = scrape_startpage(topic)
-        post = generate_post(platform, topic, results)
-        st.success("Post Generated!")
-        st.text_area("Generated Post", post, height=200)
+demo.launch()