Upload folder using huggingface_hub

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----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=
+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,7 @@
 ---
-title: Insagram Reel Analyzer
-emoji: 🐢
-colorFrom: blue
-colorTo: indigo
+title: Insagram_reel_Analyzer
+app_file: deploy.py
 sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
+sdk_version: 5.31.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Doom_Scroller_Instagram

deploy.py

@@ -0,0 +1,218 @@
+import io # Import io for handling image bytes
+
+# Global variables to maintain state across Gradio calls
+# Assuming cl and explore_reels_list are already defined and populated by login/fetch steps
+# from previous cells in a real execution environment.
+# For this cell's context, we ensure they are declared global.
+global cl
+global explore_reels_list
+global sentiment_analyzer_instance
+global content_classifier_pipeline
+
+# Initialize sentiment analyzer if not already done (can be done here or lazily in analyze_reels_gradio)
+# Doing it here ensures the model is loaded when this cell runs, potentially reducing latency on first analyze click.
+try:
+    sentiment_analyzer_instance = ReelSentimentAnalyzer()
+    print("Sentiment Analyzer initialized.")
+    # Optional: Train Hindi model if needed and data is available
+    # sample_train_data = [...] # Define your training data
+    # sentiment_analyzer_instance.train_hindi_model(sample_train_data)
+except Exception as e:
+    print(f"Error initializing Sentiment Analyzer globally: {e}")
+    sentiment_analyzer_instance = None
+
+
+# Initialize content classifier pipeline if not already done (can be done here or lazily)
+try:
+    print("Initializing Content Classifier Pipeline globally...")
+    content_classifier_pipeline = pipeline(
+        "zero-shot-classification",
+        model="facebook/bart-large-mnli",
+        device=0 if torch.cuda.is_available() else -1 # Use GPU if available
+    )
+    print("Content Classifier Pipeline Initialized.")
+except Exception as e:
+    print(f"Error initializing Content Classifier globally: {e}")
+    content_classifier_pipeline = None
+
+
+def analyze_reels_gradio(max_to_analyze):
+    """Gradio-compatible function to analyze fetched reels and generate plots."""
+    global explore_reels_list
+    global sentiment_analyzer_instance
+    global content_classifier_pipeline
+
+    if not explore_reels_list:
+        # Return None for plots if no reels
+        return "Error: No reels fetched yet. Please fetch reels first.", None, None
+
+    # Ensure max_to_analyze does not exceed the number of fetched reels
+    num_reels_to_process = min(max_to_analyze, len(explore_reels_list))
+    reels_to_analyze = explore_reels_list[:num_reels_to_process]
+
+    if not reels_to_analyze:
+         return "Error: No reels available to analyze.", None, None
+
+
+    # Check if analyzers are initialized
+    if sentiment_analyzer_instance is None:
+         return "Error: Sentiment Analyzer not initialized.", None, None
+    if content_classifier_pipeline is None:
+         return "Error: Content Classifier not initialized.", None, None
+
+
+    analysis_status_messages = []
+    sentiment_plot_figure = None # Changed to figure
+    content_plot_figure = None   # Changed to figure
+
+    # Perform Sentiment Analysis
+    try:
+        analysis_status_messages.append(f"Starting Sentiment Analysis for {len(reels_to_analyze)} reels...")
+        sentiment_results, detailed_sentiment_results = sentiment_analyzer_instance.analyze_reels(
+            reels_to_analyze,
+            max_to_analyze=len(reels_to_analyze) # Pass the actual number being processed
+        )
+        # Call the updated plotting function that returns a figure
+        sentiment_plot_figure = plot_sentiment_pie(sentiment_results, title=f"Sentiment of {len(reels_to_analyze)} Instagram Reels")
+        analysis_status_messages.append("Sentiment Analysis Complete.")
+    except Exception as e:
+        analysis_status_messages.append(f"Error during Sentiment Analysis: {e}")
+        sentiment_plot_figure = None # Ensure plot is None on error
+
+
+    # Perform Content Categorization
+    try:
+        analysis_status_messages.append(f"Starting Content Categorization for {len(reels_to_analyze)} reels...")
+        category_counts = Counter()
+        # Re-implement content analysis slightly to fit this flow using the global pipeline
+        print(f"\n⏳ Analyzing content for {len(reels_to_analyze)} reels...")
+        for i, reel in enumerate(reels_to_analyze, 1):
+            caption = getattr(reel, 'caption_text', '') or getattr(reel, 'caption', '') or ''
+            # Use the global classifier pipeline
+            category, details = classify_reel_content(caption)
+            category_counts[category] += 1
+
+        print("\n✅ Content Analysis complete!")
+        print("\n📊 Category Counts:")
+        for category, count in category_counts.most_common():
+            print(f"- {category.replace('_', ' ').title()}: {count}")
+
+        # Call the updated plotting function that returns a figure
+        content_plot_figure = plot_category_distribution(category_counts)
+        analysis_status_messages.append("Content Categorization Complete.")
+
+    except Exception as e:
+        analysis_status_messages.append(f"Error during Content Analysis: {e}")
+        content_plot_figure = None # Ensure plot is None on error
+
+
+    final_status_message = "\n".join(analysis_status_messages)
+    # Return the figure objects
+    return final_status_message, sentiment_plot_figure, content_plot_figure
+
+
+# Re-define plot functions to return bytes (if not already done in a previous cell)
+# Assuming they were defined in the previous subtask's code block.
+# If not, they would need to be included here.
+
+# --- Gradio Blocks Interface ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Instagram Reels Analysis")
+    with gr.Row():
+        username_input = gr.Textbox(label="Instagram Username")
+        login_button = gr.Button("Login")
+    login_status_output = gr.Label(label="Login Status")
+
+    with gr.Row():
+        fetch_button = gr.Button("Fetch Reels")
+    fetch_status_output = gr.Label(label="Fetch Status")
+
+    with gr.Row():
+        max_reels_input = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Number of Reels to Analyze")
+        analyze_button = gr.Button("Analyze Reels")
+
+    analyze_status_output = gr.Label(label="Analysis Status")
+
+    with gr.Row():
+        # Sentiment Analysis Outputs
+        with gr.Column():
+            gr.Markdown("## Sentiment Analysis")
+            sentiment_plot_output = gr.Plot(label="Sentiment Distribution")
+
+        # Content Analysis Outputs
+        with gr.Column():
+            gr.Markdown("## Content Analysis")
+            content_plot_output = gr.Plot(label="Content Distribution")
+
+
+    # Link login and fetch buttons (assuming login_gradio and fetch_reels_gradio are defined)
+    # Redefine login_gradio and fetch_reels_gradio here within the Blocks context
+    # to ensure they are linked correctly, even though they were defined above.
+    # This is a common pattern in Gradio Blocks.
+
+    def login_gradio_blocks(username):
+        """Gradio-compatible login function for Blocks."""
+        global cl
+        try:
+             PASSWORD = userdata.get('password')
+        except Exception as e:
+             return f"Error accessing password secret: {e}"
+
+
+        if not PASSWORD:
+            return "Error: Instagram password not found in Colab secrets."
+
+        cl = Client()
+
+        try:
+            cl.login(username, PASSWORD)
+            return f"Successfully logged in as {username}"
+        except Exception as e:
+            cl = None # Ensure cl is None on failure
+            return f"Error during login: {e}"
+
+    def fetch_reels_gradio_blocks():
+        """Gradio-compatible function to fetch explore reels for Blocks."""
+        global cl
+        global explore_reels_list
+
+        if cl is None:
+            explore_reels_list = [] # Ensure list is empty on failure
+            return "Error: Not logged in. Please log in first."
+
+        try:
+            # Fetch a limited number of reels for demonstration purposes
+            # You might want to make this number configurable later
+            fetched_reels = cl.explore_reels()[:100] # Fetch up to 100 for analysis
+            explore_reels_list = fetched_reels
+            if explore_reels_list:
+                return f"Successfully fetched {len(explore_reels_list)} explore reels."
+            else:
+                explore_reels_list = [] # Ensure it's an empty list
+                return "Fetched 0 explore reels."
+        except Exception as e:
+            explore_reels_list = [] # Ensure it's an empty list on error
+            return f"Error fetching explore reels: {e}"
+
+
+    login_button.click(
+        fn=login_gradio_blocks,
+        inputs=username_input,
+        outputs=login_status_output
+    )
+
+    fetch_button.click(
+        fn=fetch_reels_gradio_blocks,
+        inputs=None, # No direct inputs needed for fetching
+        outputs=fetch_status_output
+    )
+
+    # Link the Analyze button to the analysis function
+    analyze_button.click(
+        fn=analyze_reels_gradio,
+        inputs=max_reels_input, # Input is the slider value
+        outputs=[analyze_status_output, sentiment_plot_output, content_plot_output] # Outputs are status and the two plots
+    )
+
+# The demo is now fully defined. It can be launched in the next step.
+# demo.launch()