Update app.py

app.py

@@ -9,10 +9,7 @@ from crewai_tools import SerperDevTool, ScrapeWebsiteTool
 import gradio as gr
 
 # =============================================================================
-# API KEYS — Read from Hugging Face Space Secrets
-# Go to: Space → Settings → Variables and secrets → New secret
-#   Secret name : OPENAI_API_KEY   → your OpenAI key
-#   Secret name : SERPER_API_KEY   → your Serper key (serper.dev)
+# API KEYS
 # =============================================================================
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
 SERPER_API_KEY = os.environ.get("SERPER_API_KEY", "")
@@ -29,7 +26,6 @@ if not SERPER_API_KEY:
         "Add it in Space → Settings → Variables and secrets."
     )
 
-# CrewAI and LangChain pick these up automatically from os.environ
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 os.environ["SERPER_API_KEY"] = SERPER_API_KEY
 
@@ -45,13 +41,13 @@ llm = LLM(
 # =============================================================================
 # TOOLS
 # =============================================================================
-search_tool = SerperDevTool()       # Finds URLs + short snippets via Serper
-scrape_tool = ScrapeWebsiteTool()   # Reads full page content from those URLs
+search_tool = SerperDevTool()
+scrape_tool = ScrapeWebsiteTool()
 
 # =============================================================================
 # MEMORY
 # =============================================================================
-MEMORY_DIR = "/tmp/crewai_memory"   # /tmp is always writable on HF Spaces
+MEMORY_DIR = "/tmp/crewai_memory"
 os.makedirs(MEMORY_DIR, exist_ok=True)
 
 EMBEDDER_CONFIG = {
@@ -68,17 +64,10 @@ EMBEDDER_CONFIG = {
 researcher = Agent(
     role="Senior Research Analyst",
     goal=(
-        "Find the most accurate, up-to-date, and relevant information on the given topic. "
-        "Always prioritize credible sources, cite key statistics, and highlight emerging trends."
+        "Find the most accurate, up-to-date, and relevant information on the given topic."
     ),
-    backstory=(
-        "You are a veteran research analyst with 15 years of experience across technology, "
-        "business, and science journalism. You have a reputation for digging deep into topics, "
-        "cross-referencing multiple sources, and presenting findings in a clear, structured format. "
-        "You never make assumptions — everything you report is evidence-backed. "
-        "You organize research into clean bullet points with source context."
-    ),
-    tools=[search_tool, scrape_tool],   # Serper finds URLs, ScrapeWebsite reads full content
+    backstory="Veteran research analyst with deep investigation skills.",
+    tools=[search_tool, scrape_tool],
     llm=llm,
     verbose=True,
     allow_delegation=False,
@@ -87,17 +76,8 @@ researcher = Agent(
 
 writer = Agent(
     role="Professional Content Writer",
-    goal=(
-        "Transform the research summary into a compelling, well-structured, and audience-appropriate article. "
-        "Ensure the article flows naturally, uses clear headings, and maintains the requested tone throughout."
-    ),
-    backstory=(
-        "You are an award-winning content writer with expertise in long-form journalism, technical writing, "
-        "and digital publishing. You have written for Forbes, TechCrunch, and Harvard Business Review. "
-        "You have a talent for making complex topics accessible without dumbing them down. "
-        "You always start with a strong hook, use subheadings to guide the reader, "
-        "and end with a memorable conclusion that reinforces the key message."
-    ),
+    goal="Write a compelling structured article from research.",
+    backstory="Expert in long-form and technical writing.",
     llm=llm,
     verbose=True,
     allow_delegation=False,
@@ -106,17 +86,8 @@ writer = Agent(
 
 editor = Agent(
     role="Senior Editor",
-    goal=(
-        "Review and refine the article to ensure it is grammatically flawless, tonally consistent, "
-        "logically structured, and ready for publication. Return only the final polished article."
-    ),
-    backstory=(
-        "You are a meticulous senior editor with 20 years of experience at top-tier publications. "
-        "You have an unmatched eye for inconsistency, passive voice overuse, redundant phrasing, "
-        "and weak transitions. You tighten prose without stripping its personality. "
-        "You ensure every paragraph earns its place and every sentence is purposeful. "
-        "You never add commentary — you deliver only the clean, final article."
-    ),
+    goal="Polish article to publication quality.",
+    backstory="Meticulous editor ensuring clarity and consistency.",
     llm=llm,
     verbose=True,
     allow_delegation=False,
@@ -126,84 +97,23 @@ editor = Agent(
 # =============================================================================
 # TASK BUILDER
 # =============================================================================
-def build_tasks(topic: str, audience: str, tone: str, length: str) -> list:
+def build_tasks(topic: str, audience: str, tone: str, length: str):
     research_task = Task(
-        description=textwrap.dedent(f"""
-            Conduct thorough research on the following topic:
-            TOPIC: "{topic}"
-
-            Your research should be targeted for this audience: {audience}
-            Required depth: {length}
-
-            Your deliverables:
-            1. A 2-sentence topic overview
-            2. 5-8 key facts or statistics with source context
-            3. 3-5 recent developments or trends (2023-2025)
-            4. 2-3 expert opinions or notable perspectives
-            5. A list of 3 key takeaways
-
-            Format everything in clearly labelled bullet points.
-        """),
-        expected_output=(
-            "A comprehensive, well-structured research summary in bullet points "
-            "covering overview, key facts, recent developments, expert opinions, and takeaways."
-        ),
+        description=f"Research topic: {topic}",
+        expected_output="Structured research summary",
         agent=researcher,
     )
 
     writing_task = Task(
-        description=textwrap.dedent(f"""
-            Using the research summary provided, write a complete article on:
-            TOPIC: "{topic}"
-
-            Specifications:
-            - Target Audience: {audience}
-            - Tone: {tone}
-            - Length: {length}
-
-            Structure:
-            1. Headline - compelling and specific
-            2. Introduction - hook + why this topic matters now
-            3. Section 1 with subheading - background / context
-            4. Section 2 with subheading - key developments or insights
-            5. Section 3 with subheading - implications or practical applications
-            6. Conclusion - summary + forward-looking statement
-
-            Rules:
-            - Do NOT use generic filler phrases like "In conclusion" or "It is worth noting"
-            - Use active voice whenever possible
-            - Every paragraph should add new value - no repetition
-        """),
-        expected_output=(
-            "A complete, structured article with headline, introduction, three body sections "
-            "with subheadings, and a conclusion - written in the requested tone for the specified audience."
-        ),
+        description=f"Write article on: {topic}",
+        expected_output="Full article",
         agent=writer,
         context=[research_task],
     )
 
     editing_task = Task(
-        description=textwrap.dedent(f"""
-            You are the final gatekeeper before publication.
-            Review and edit the article for the following:
-
-            Checklist:
-            - Grammar and spelling are flawless
-            - Tone is consistent and matches: {tone}
-            - Audience appropriateness: {audience}
-            - No redundant sentences or paragraphs
-            - Transitions between sections are smooth
-            - Headline is punchy and specific
-            - Introduction hooks the reader immediately
-            - Conclusion ends with impact
-
-            IMPORTANT: Return ONLY the final polished article text.
-            Do NOT include your editorial notes, checklist, or any commentary.
-        """),
-        expected_output=(
-            "The final, publication-ready article - clean, polished, and complete. "
-            "No editor notes or commentary included."
-        ),
+        description="Edit article to final form",
+        expected_output="Final polished article",
         agent=editor,
         context=[writing_task],
     )
@@ -213,20 +123,9 @@ def build_tasks(topic: str, audience: str, tone: str, length: str) -> list:
 # =============================================================================
 # CREW RUNNER
 # =============================================================================
-def run_crew(topic: str, audience: str, tone: str, length: str) -> str:
-    if not topic or not topic.strip():
-        return "Please enter a topic before generating."
-
-    if len(topic.strip()) < 5:
-        return "Topic is too short. Please be more descriptive (e.g. 'AI in healthcare 2025')."
-
-    print(f"\n{'='*60}")
-    print(f"Starting CrewAI Pipeline")
-    print(f"   Topic    : {topic}")
-    print(f"   Audience : {audience}")
-    print(f"   Tone     : {tone}")
-    print(f"   Length   : {length}")
-    print(f"{'='*60}\n")
+def run_crew(topic: str, audience: str, tone: str, length: str):
+    if not topic.strip():
+        return "Please enter a topic."
 
     try:
         tasks = build_tasks(topic, audience, tone, length)
@@ -238,184 +137,75 @@ def run_crew(topic: str, audience: str, tone: str, length: str) -> str:
             verbose=True,
             memory=True,
             embedder=EMBEDDER_CONFIG,
-            memory_config={
-                "long_term": {
-                    "storage_path": MEMORY_DIR
-                }
-            },
+            memory_config={"long_term": {"storage_path": MEMORY_DIR}},
         )
 
-        print("\nAgent 1 (Researcher) starting...\n")
         result = crew.kickoff()
-
-        print(f"\n{'='*60}")
-        print("CrewAI pipeline complete!")
-        print(f"{'='*60}\n")
-
         return str(result)
 
     except Exception as e:
-        error_msg = f"An error occurred during pipeline execution:\n{str(e)}"
-        print(error_msg)
-        return error_msg
+        return f"Error: {str(e)}"
 
 # =============================================================================
-# GRADIO UI
+# UI
 # =============================================================================
-with gr.Blocks(
-    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate"),
-    title="Multi-Agent Article Generator",
-    css="""
-        .title-text { text-align: center; }
-        .agent-badge { font-size: 0.85rem; color: #666; }
-        footer { display: none !important; }
-    """
-) as demo:
+with gr.Blocks() as demo:
 
-    gr.Markdown(
-        """
-        <div class="title-text">
-        <h1>Multi-Agent Article Generator</h1>
-        <p style="color:#555;">Powered by <strong>CrewAI</strong> + <strong>GPT-4o</strong> + <strong>SerperDev</strong> + <strong>ScrapeWebsite</strong></p>
-        </div>
-
-        ---
-
-        ### How it works
-        `Researcher` searches the web + scrapes full pages for deep research →
-        `Writer` drafts a structured article →
-        `Editor` polishes and finalizes for publication
-
-        > Generation takes **45-120 seconds**. Agent logs appear in the Space logs panel.
-        """
-    )
+    gr.Markdown("# Multi-Agent Article Generator")
 
-    with gr.Row(equal_height=False):
+    with gr.Row():
 
-        with gr.Column(scale=1, min_width=280):
-            gr.Markdown("### Settings")
-
-            topic_input = gr.Textbox(
-                label="Topic *",
-                placeholder="e.g. The impact of AI agents on software development in 2025",
-                lines=3,
-                info="Be specific - better topic = better article."
-            )
+        with gr.Column():
+            topic_input = gr.Textbox(label="Topic", lines=3)
 
             audience_input = gr.Dropdown(
-                label="Target Audience",
-                choices=[
-                    "General Public",
-                    "Business Professionals",
-                    "Students & Learners",
-                    "Researchers & Academics",
-                    "Tech Enthusiasts"
-                ],
+                ["General Public", "Business", "Students"],
                 value="General Public",
-                info="Shapes vocabulary and depth of explanation."
+                label="Audience",
             )
 
             tone_input = gr.Dropdown(
-                label="Writing Tone",
-                choices=[
-                    "Informative",
-                    "Persuasive",
-                    "Conversational",
-                    "Academic",
-                    "Inspirational"
-                ],
+                ["Informative", "Conversational", "Academic"],
                 value="Informative",
-                info="Controls the style and voice of the article."
+                label="Tone",
             )
 
             length_input = gr.Radio(
-                label="Article Length",
-                choices=[
-                    "Short (300-500 words)",
-                    "Medium (600-900 words)",
-                    "Long (1000-1400 words)"
-                ],
-                value="Medium (600-900 words)",
-            )
-
-            gr.Markdown("---")
-
-            submit_btn = gr.Button(
-                "Generate Article",
-                variant="primary",
-                size="lg"
-            )
-
-            clear_btn = gr.Button(
-                "Clear",
-                variant="secondary",
-                size="sm"
-            )
-
-            gr.Markdown(
-                """
-                <div class="agent-badge">
-                <b>Agents:</b><br>
-                Researcher - web search + scrape full pages<br>
-                Writer - draft structured article<br>
-                Editor - polish + finalize
-                </div>
-                """
+                ["Short", "Medium", "Long"],
+                value="Medium",
+                label="Length",
             )
 
-        with gr.Column(scale=2):
-            gr.Markdown("### Generated Article")
+            submit_btn = gr.Button("Generate")
+            clear_btn = gr.Button("Clear")
 
+        with gr.Column():
             output_box = gr.Textbox(
-                label="",
-                lines=35,
-                placeholder=(
-                    "Your article will appear here once generation is complete.\n\n"
-                    "This takes 45-120 seconds.\n"
-                    "Watch the Space logs for live agent output."
-                ),
-                show_copy_button=True,
+                label="Output",
+                lines=30,
                 interactive=False,
             )
 
-    gr.Markdown("### Example Topics")
-    gr.Examples(
-        examples=[
-            ["The future of AI agents in enterprise software", "Business Professionals", "Informative", "Medium (600-900 words)"],
-            ["How large language models are changing education", "Students & Learners", "Conversational", "Short (300-500 words)"],
-            ["Quantum computing: where are we in 2025?", "Tech Enthusiasts", "Informative", "Long (1000-1400 words)"],
-            ["The ethics of autonomous AI decision-making", "Researchers & Academics", "Academic", "Long (1000-1400 words)"],
-            ["Why every startup needs an AI strategy in 2025", "Business Professionals", "Persuasive", "Medium (600-900 words)"],
-        ],
-        inputs=[topic_input, audience_input, tone_input, length_input],
-        label="Click any example to auto-fill the inputs"
-    )
-
-    gr.Markdown(
-        """
-        ---
-        <div style="text-align:center; color:#888; font-size:0.85rem;">
-        Built with <strong>CrewAI</strong> · <strong>LangChain</strong> · <strong>GPT-4o</strong> · <strong>Gradio</strong> · <strong>ScrapeWebsiteTool</strong>
-        </div>
-        """
-    )
-
     submit_btn.click(
         fn=run_crew,
         inputs=[topic_input, audience_input, tone_input, length_input],
         outputs=output_box,
-        api_name="generate",
     )
 
     clear_btn.click(
         fn=lambda: ("", ""),
         inputs=[],
-        outputs=[topic_input, output_box]
+        outputs=[topic_input, output_box],
     )
 
 # =============================================================================
-# LAUNCH
-# server_name="0.0.0.0" is required for Hugging Face Spaces
-# server_port=7860 is the default port HF Spaces expects
+# LAUNCH (UPDATED FOR GRADIO 6)
 # =============================================================================
-demo.launch(server_name="0.0.0.0", server_port=7860)
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=7860,
+    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate"),
+    css="""
+        footer { display: none !important; }
+    """,
+)