Update app.py

app.py

@@ -6,27 +6,22 @@ import json
 from datetime import datetime
 import time
 
-# Initialize Groq client
-
-
 @st.cache_resource
 def get_groq_client():
     return Groq(api_key=st.secrets["groq_api_key"])
 
-    
-
 class ContentAnalysisAgent:
     def __init__(self):
         """Initialize the agent with Groq client"""
         self.client = get_groq_client()
         self.system_prompt = """You are an expert social media content analyzer with deep understanding of engagement, 
         audience psychology, and content optimization. Analyze content step by step using a systematic approach."""
-        
+
     def _think(self, thought_process: str) -> None:
         """Display agent's thinking process"""
         with st.expander("🤔 Agent's Thought Process", expanded=False):
             st.write(thought_process)
-    
+
     def _get_llm_response(self, messages: List[Dict]) -> str:
         """Get response from Groq LLM"""
         try:
@@ -44,135 +39,119 @@ class ContentAnalysisAgent:
     def _create_analysis_prompt(self, text: str) -> str:
         """Create a detailed analysis prompt with agentic thinking"""
         return f"""Let's analyze this social media post step by step:
-
 POST: {text}
-
-
-
 Think through the following aspects:
-
 1. CONTENT STRUCTURE ANALYSIS
 - Examine length, formatting, and organization
 - Identify key message components
 - Note special characters and emoji usage
-
 2. AUDIENCE PSYCHOLOGY
 - Who is the target audience?
 - What emotional triggers are present?
 - What call-to-actions exist?
-
 3. ENGAGEMENT POTENTIAL
 - Analyze hook effectiveness
 - Evaluate storytelling elements
 - Assess viral potential
-
 4. STYLE AND TONE
 - Determine primary content style
 - Identify emotional undertones
 - Evaluate brand voice consistency
-
-
 5. OPTIMIZATION OPPORTUNITIES
 - Identify areas for improvement
 - Suggest engagement boosters
 - Note potential risks or concerns
-
-
-
 Return a JSON structured response with:
-{
+{{
     "style": "primary posting style",
     "tones": ["list of detected tones"],
     "rating": "1-5 rating for content appropriateness",
     "engagement_score": "0-100 engagement potential",
-    "analysis": {
+    "analysis": {{
         "strengths": ["list of strong points"],
         "improvements": ["areas to enhance"],
         "audience_fit": "target audience match score"
-    }
-}"""
+    }}
+}}"""
 
     def analyze_post(self, post_text: str) -> Dict:
         """Perform comprehensive post analysis"""
         # First thinking phase - Initial Assessment
         self._think("🔍 Phase 1: Initial Assessment\nAnalyzing post structure and basic elements...")
-        
+
         # Create conversation with system prompt and analysis request
         messages = [
             {"role": "system", "content": self.system_prompt},
             {"role": "user", "content": self._create_analysis_prompt(post_text)}
         ]
-        
+
         # Get initial analysis
         with st.spinner("🤖 Analyzing content..."):
             analysis_response = self._get_llm_response(messages)
-            
+
         if not analysis_response:
             return None
 
-
-            
         # Parse JSON response
         try:
             analysis_result = json.loads(analysis_response)
         except json.JSONDecodeError:
             st.error("Error parsing LLM response")
             return None
-            
+
         # Second thinking phase - Refinement
         self._think("🎯 Phase 2: Refinement\nRefining analysis and generating specific recommendations...")
-        
+
         # Get specific recommendations
         recommendation_prompt = f"""Based on the initial analysis of this post:
         {post_text}
-        
+
         Provide 3 specific, actionable recommendations to improve engagement."""
-        
+
         messages.append({"role": "user", "content": recommendation_prompt})
         recommendations = self._get_llm_response(messages)
-        
+
         if recommendations:
             analysis_result["recommendations"] = recommendations
-            
+
         return analysis_result
 
+
 class GraicieApp:
     def __init__(self):
         self.agent = ContentAnalysisAgent()
-        
+
     def display_header(self):
         st.title("🤖 Project Graicie - Advanced Content Analyzer")
         st.markdown("""
         ### Powered by LLaMA 3 & Agentic AI
         Get deep, AI-powered insights into your social media content using advanced language models.
         """)
-    
+
     def display_example_posts(self):
         examples = {
             "Viral Post": "🚀 HUGE ANNOUNCEMENT! After months of work, my online course is finally LIVE! 🎉\n"
                          "Learn how I grew from 0 to 100K followers in 6 months! Early bird pricing ends tomorrow! 💫\n"
                          "#socialmedia #digitalmarketing #success",
-            
             "Personal Story": "Sometimes life throws you curveballs... Today I faced my biggest fear and went "
-                            "skydiving! 🪂 Swipe to see my reaction! Remember: growth happens outside your comfort zone 💕\n"
-                            "#personalgrowth #motivation",
-            
+                              "skydiving! 🪂 Swipe to see my reaction! Remember: growth happens outside your comfort zone 💕\n"
+                              "#personalgrowth #motivation",
             "Educational": "🧠 5 Python Tips You Didn't Know:\n1. List comprehensions\n2. f-strings\n3. Walrus operator\n"
-                          "4. Context managers\n5. Lambda functions\nSave this for later! 💡\n#coding #programming"
+                           "4. Context managers\n5. Lambda functions\nSave this for later! 💡\n#coding #programming"
         }
-        
+
         st.subheader("📱 Try an Example Post")
         selected_example = st.selectbox("Select an example post:", list(examples.keys()))
-        
+
         if selected_example:
             st.text_area("Example Post", examples[selected_example], height=100, disabled=True)
             if st.button("Analyze Example", use_container_width=True):
                 self.analyze_and_display(examples[selected_example])
-    
+
     def display_results(self, results: Dict):
         if not results:
             return
-            
+
         # Display main metrics
         col1, col2, col3, col4 = st.columns(4)
         with col1:
@@ -183,57 +162,57 @@ class GraicieApp:
             st.metric("Content Rating", f"{results['rating']}/5")
         with col4:
             st.metric("Audience Fit", results["analysis"]["audience_fit"])
-            
+
         # Display tones
         st.subheader("📊 Content Tones")
         for tone in results["tones"]:
             st.markdown(f"<span style='background-color: #e6f3ff; padding: 5px 10px; "
-                      f"margin: 5px; border-radius: 15px;'>{tone}</span>", unsafe_allow_html=True)
-            
+                        f"margin: 5px; border-radius: 15px;'>{tone}</span>", unsafe_allow_html=True)
+
         # Display strengths and improvements
         col1, col2 = st.columns(2)
         with col1:
             st.subheader("💪 Strengths")
             for strength in results["analysis"]["strengths"]:
                 st.markdown(f"✅ {strength}")
-                
+
         with col2:
             st.subheader("🎯 Areas to Improve")
             for improvement in results["analysis"]["improvements"]:
                 st.markdown(f"📌 {improvement}")
-                
+
         # Display recommendations
         if "recommendations" in results:
             st.subheader("🚀 Specific Recommendations")
             st.markdown(results["recommendations"])
-    
+
     def analyze_and_display(self, text: str):
         results = self.agent.analyze_post(text)
         if results:
             self.display_results(results)
-    
+
     def run(self):
         self.display_header()
-        
+
         # Main content area
         col1, col2 = st.columns([2, 1])
-        
+
         with col1:
             self.display_example_posts()
-            
+
             st.subheader("📝 Analyze Your Post")
             user_post = st.text_area(
                 "Enter your post content:",
                 height=150,
                 placeholder="Type or paste your content here..."
             )
-            
+
             if st.button("🔍 Analyze My Post", use_container_width=True):
                 if user_post:
                     self.analyze_and_display(user_post)
                 else:
                     st.warning("Please enter some content to analyze!")
-                    
+
         with col2:
             st.subheader("💡 Pro Tips")
             st.info("""
@@ -244,7 +223,7 @@ class GraicieApp:
             4. Add visual elements
             5. Engage with questions
             """)
-            
+
             st.markdown("### 📊 Optimal Post Elements")
             st.markdown("""
             - Length: 80-150 characters
@@ -252,7 +231,7 @@ class GraicieApp:
             - Emojis: 2-3 key emojis
             - CTA: One clear action
             """)
-        
+
         # Footer
         st.markdown(
             """
@@ -264,15 +243,10 @@ class GraicieApp:
                 </p>
             </div>
             """,
-            unsafe_allow_html=True
+            unsafe_allow_html=True,
         )
 
+
 if __name__ == "__main__":
-    st.set_page_config(
-        page_title="Project Graicie - AI Content Analyzer",
-        page_icon="🤖",
-        layout="wide"
-    )
-    
     app = GraicieApp()
-    app.run()
+    app.run()