Update src/streamlit_app.py

src/streamlit_app.py

@@ -9,7 +9,7 @@ import pandas as pd
 import logging
 
 # Backend API Key Configuration
-GEMINI_API_KEY =  os.getenv("GEMENI_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_KEY")
 
 # Page configuration
 st.set_page_config(
@@ -33,10 +33,9 @@ def configure_gemini():
     """Configure Gemini API with backend key"""
     return genai.Client(api_key=GEMINI_API_KEY)
 
-# The main prompt template with structured output requirements
+# Enhanced system prompt with timestamp-based improvements
 SYSTEM_PROMPT = f"""{os.getenv("SYS_PROMPT")}"""
 
-@st.cache_data
 def analyze_video_and_generate_script(
     video_bytes,
     video_name,
@@ -81,8 +80,8 @@ def analyze_video_and_generate_script(
         upload_progress.progress(80)
         upload_status.text("Generating script variations...")
             
-        # Build the user prompt with additional context
-        user_prompt = f"""Analyze this reference video and generate 3 high-converting direct response video script variations.
+        # Build the enhanced user prompt
+        user_prompt = f"""Analyze this reference video and generate 3 high-converting direct response video script variations with detailed timestamp-based improvements.
 
 ADDITIONAL CONTEXT:
 - Offer Details: {offer_details if offer_details else 'Extract from video'}
@@ -90,17 +89,24 @@ ADDITIONAL CONTEXT:
 - Specific Hooks to Consider: {specific_hooks if specific_hooks else 'Create based on video analysis'}
 - Additional Context: {additional_context}
 
-Please analyze the video's:
-1. Hook strategy and opening seconds
-2. Pacing and visual transitions
-3. Claims and promises made
-4. Authority elements used
-5. Urgency/scarcity tactics
-6. CTA approach
+Please provide a comprehensive analysis including:
 
-Then create 2-3 script variations that would perform 30-50% cheaper while maintaining conversion effectiveness.
+1. DETAILED VIDEO ANALYSIS with timestamp-based metrics:
+   - Break down the video into 5-10 second segments
+   - Rate each segment's effectiveness (1-10 scale)
+   - Identify specific elements (hook, transition, proof, CTA, etc.)
 
-IMPORTANT: Return only valid JSON in the exact format specified in the system prompt. Do not include any text before or after the JSON."""
+2. TIMESTAMP-BASED IMPROVEMENTS:
+   - Specific recommendations for each time segment
+   - Priority level for each improvement
+   - Expected impact of implementing changes
+
+3. SCRIPT VARIATIONS:
+   - Create 2-3 complete script variations
+   - Each with timestamp-by-timestamp breakdown
+   - Different psychological triggers and approaches
+
+IMPORTANT: Return only valid JSON in the exact format specified in the system prompt. Analyze the video second-by-second for maximum detail."""
 
         # Generate response
         response = client.models.generate_content(
@@ -179,13 +185,14 @@ def display_script_variations(json_data):
         st.divider()
 
 def display_video_analysis(json_data):
-    """Display video analysis in formatted sections"""
+    """Display video analysis in tabular format"""
     if not json_data or "video_analysis" not in json_data:
         st.error("No video analysis found in the response")
         return
     
     analysis = json_data["video_analysis"]
     
+    # Display general analysis
     col1, col2 = st.columns(2)
     
     with col1:
@@ -198,40 +205,116 @@ def display_video_analysis(json_data):
     with col2:
         st.subheader("Psychological Triggers")
         st.write(analysis.get('psychological_triggers', 'N/A'))
+    
+    # Display video metrics in tabular format
+    st.subheader("Detailed Video Metrics (Timestamp Analysis)")
+    video_metrics = analysis.get('video_metrics', [])
+    if video_metrics:
+        metrics_df = pd.DataFrame(video_metrics)
+        
+        # Rename columns for better display
+        column_mapping = {
+            'timestamp': 'Timestamp',
+            'element': 'Element',
+            'current_approach': 'Current Approach',
+            'effectiveness_score': 'Score',
+            'notes': 'Analysis Notes'
+        }
         
-        st.subheader("Improvement Recommendations")
-        st.write(analysis.get('improvement_recommendations', 'N/A'))
+        metrics_df = metrics_df.rename(columns=column_mapping)
+        
+        st.dataframe(
+            metrics_df,
+            use_container_width=True,
+            hide_index=True,
+            column_config={
+                "Timestamp": st.column_config.TextColumn(width="small"),
+                "Element": st.column_config.TextColumn(width="medium"),
+                "Current Approach": st.column_config.TextColumn(width="large"),
+                "Score": st.column_config.TextColumn(width="small"),
+                "Analysis Notes": st.column_config.TextColumn(width="large")
+            }
+        )
+    else:
+        st.warning("No detailed video metrics available")
 
-def create_download_content(json_data):
-    """Create downloadable content from JSON data"""
-    content = "VIDEO ANALYSER AND SCRIPT GENERATOR OUTPUT\n"
-    content += "=" * 60 + "\n\n"
+def display_timestamp_improvements(json_data):
+    """Display timestamp-based improvements in tabular format"""
+    if not json_data or "timestamp_improvements" not in json_data:
+        st.error("No timestamp improvements found in the response")
+        return
+    
+    st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
     
-    # Add script variations
+    improvements = json_data["timestamp_improvements"]
+    if improvements:
+        improvements_df = pd.DataFrame(improvements)
+        
+        # Rename columns for better display
+        column_mapping = {
+            'timestamp': 'Timestamp',
+            'current_element': 'Current Element',
+            'improvement_type': 'Improvement Type',
+            'recommended_change': 'Recommended Change',
+            'expected_impact': 'Expected Impact',
+            'priority': 'Priority'
+        }
+        
+        improvements_df = improvements_df.rename(columns=column_mapping)
+        
+        # Color code priority
+        def color_priority(val):
+            if val == 'High':
+                return 'background-color: #ffcccb'
+            elif val == 'Medium':
+                return 'background-color: #ffffcc'
+            elif val == 'Low':
+                return 'background-color: #ccffcc'
+            return ''
+        
+        styled_df = improvements_df.style.applymap(color_priority, subset=['Priority'])
+        
+        st.dataframe(
+            styled_df,
+            use_container_width=True,
+            hide_index=True,
+            column_config={
+                "Timestamp": st.column_config.TextColumn(width="small"),
+                "Current Element": st.column_config.TextColumn(width="medium"),
+                "Improvement Type": st.column_config.TextColumn(width="medium"),
+                "Recommended Change": st.column_config.TextColumn(width="large"),
+                "Expected Impact": st.column_config.TextColumn(width="medium"),
+                "Priority": st.column_config.TextColumn(width="small")
+            }
+        )
+    else:
+        st.warning("No timestamp improvements available")
+
+def create_csv_download(json_data):
+    """Create CSV content with all scripts combined"""
+    all_scripts_data = []
+    
+    # Combine all script variations into one dataset
     for i, variation in enumerate(json_data.get("script_variations", []), 1):
         variation_name = variation.get("variation_name", f"Variation {i}")
-        content += f"{variation_name}\n"
-        content += "-" * 40 + "\n"
         
         for row in variation.get("script_table", []):
-            content += f"Timestamp: {row.get('timestamp', '')}\n"
-            content += f"Script: {row.get('script_voiceover', '')}\n"
-            content += f"Visual: {row.get('visual_direction', '')}\n"
-            content += f"Trigger: {row.get('psychological_trigger', '')}\n"
-            content += f"CTA: {row.get('cta_action', '')}\n\n"
-        
-        content += "\n"
-    
-    # Add analysis
-    analysis = json_data.get("video_analysis", {})
-    content += "VIDEO ANALYSIS\n"
-    content += "=" * 20 + "\n"
-    content += f"Effectiveness Factors: {analysis.get('effectiveness_factors', 'N/A')}\n\n"
-    content += f"Psychological Triggers: {analysis.get('psychological_triggers', 'N/A')}\n\n"
-    content += f"Target Audience: {analysis.get('target_audience', 'N/A')}\n\n"
-    content += f"Recommendations: {analysis.get('improvement_recommendations', 'N/A')}\n"
+            script_row = {
+                'Variation': variation_name,
+                'Timestamp': row.get('timestamp', ''),
+                'Script_Voiceover': row.get('script_voiceover', ''),
+                'Visual_Direction': row.get('visual_direction', ''),
+                'Psychological_Trigger': row.get('psychological_trigger', ''),
+                'CTA_Action': row.get('cta_action', '')
+            }
+            all_scripts_data.append(script_row)
     
-    return content
+    # Convert to DataFrame and then to CSV
+    if all_scripts_data:
+        df = pd.DataFrame(all_scripts_data)
+        return df.to_csv(index=False)
+    else:
+        return "No script data available"
 
 def check_token(user_token):
     ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
@@ -251,7 +334,6 @@ def main():
     st.title("Video Analyser and Script Generator")
     st.divider()
 
-    st.set_page_config(page_title="Bulk Creative Generation", layout="wide")
     if "authenticated" not in st.session_state:
         st.session_state["authenticated"] = False
 
@@ -364,24 +446,27 @@ def main():
                 st.success("Analysis complete! Here are your script variations:")
                 
                 # Create tabs for different outputs
-                tab1, tab2 = st.tabs(["Script Variations", "Video Analysis"])
+                tab1, tab2, tab3 = st.tabs(["Script Variations", "Video Analysis", "Improvement Recommendations"])
                 
                 with tab1:
                     display_script_variations(json_response)
                     
-                    # Download button
-                    download_content = create_download_content(json_response)
+                    # CSV Download button
+                    csv_content = create_csv_download(json_response)
                     st.download_button(
-                        label="Download All Scripts",
-                        data=download_content,
-                        file_name="video_script_variations.txt",
-                        mime="text/plain",
+                        label="Download All Scripts (CSV)",
+                        data=csv_content,
+                        file_name="video_script_variations.csv",
+                        mime="text/csv",
                         type="secondary",
                         use_container_width=True
                     )
                 
                 with tab2:
                     display_video_analysis(json_response)
+                
+                with tab3:
+                    display_timestamp_improvements(json_response)
             else:
                 st.error("Failed to generate script variations. Please try again.")