Update app.py

app.py

@@ -1,33 +1,30 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import json
 
-# Initialize the client with a high-performance coding model
-# Qwen 2.5 Coder is excellent for understanding code logic and generating structured JSON.
-# If you experience rate limits, you can switch to "Qwen/Qwen2.5-7B-Instruct" or "meta-llama/Meta-Llama-3-8B-Instruct"
+# Use Qwen 2.5 Coder 32B (Excellent for code & JSON)
+# We will access it via the Chat API to avoid "Task not supported" errors.
 REPO_ID = "Qwen/Qwen2.5-Coder-32B-Instruct"
-client = InferenceClient(model=REPO_ID)
+client = InferenceClient()
 
 def generate_seo(code_snippet, file_type):
     """
-    Analyzes code and generates SEO metadata + JSON-LD.
+    Analyzes code and generates SEO metadata + JSON-LD using Chat API.
     """
     if not code_snippet.strip():
         return "Please paste some code first."
 
-    # Construct a strict prompt for the LLM
-    prompt = f"""
-    You are an expert SEO specialist and Software Engineer. Analyze the following {file_type} code.
-    
-    Your task is to generate SEO metadata and Google Search compliant JSON-LD structured data.
+    # 1. Define the System Prompt (The instructions)
+    system_instruction = f"""
+    You are an expert SEO specialist and Software Engineer. 
+    Analyze the user's {file_type} code.
     
     Output Format requirements:
     1. Title: A clickable, SEO-optimized title tag (max 60 chars).
     2. Meta Description: A summary for search engines (max 160 chars).
     3. Keywords: 5-8 comma-separated high-value keywords.
     4. JSON-LD: A valid <script type="application/ld+json"> block. 
-       - If it is Python code, use Schema.org 'SoftwareSourceCode'.
-       - If it is HTML, use Schema.org 'WebPage' or 'TechArticle'.
+       - If Python: use Schema.org 'SoftwareSourceCode'.
+       - If HTML: use Schema.org 'WebPage' or 'TechArticle'.
     
     Strictly follow this output format (Markdown):
     
@@ -40,32 +37,36 @@ def generate_seo(code_snippet, file_type):
     ```json
     [Insert strictly valid JSON code here, no comments]
     ```
-
-    Here is the code to analyze:
-    
-    ```{file_type}
-    {code_snippet}
-    ```
     """
 
+    # 2. Define the User Message (The Code)
+    user_message = f"Here is the {file_type} code:\n\n{code_snippet}"
+
     try:
-        # Call the Inference API
-        response = client.text_generation(
-            prompt,
-            max_new_tokens=1024,
-            temperature=0.3, # Low temperature for factual/structural consistency
-            stop_sequences=["<|endoftext|>"]
+        # 3. Use chat_completion (matches 'conversational' task)
+        response = client.chat_completion(
+            model=REPO_ID,
+            messages=[
+                {"role": "system", "content": system_instruction},
+                {"role": "user", "content": user_message}
+            ],
+            max_tokens=1024,
+            temperature=0.3
         )
-        return response
+        
+        # Extract the actual message content
+        return response.choices[0].message.content
+
     except Exception as e:
-        return f"Error: {str(e)}\n\nPossible fix: The free model tier might be busy. Wait 1 minute and try again, or create a 'HF_TOKEN' secret in your Space settings."
+        # Fallback error handling
+        return f"Error: {str(e)}\n\nTry switching the model in the code to 'meta-llama/Meta-Llama-3-8B-Instruct' if this persists."
 
 # Design the UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
         # 🚀 AI Code-to-SEO Generator
-        Paste your **Python** or **HTML** code below to generate optimized meta tags and JSON-LD Schema for Google Search.
+        Paste your **Python** or **HTML** code below to generate optimized meta tags and JSON-LD Schema.
         """
     )
     
@@ -78,7 +79,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         with gr.Column():
             output_markdown = gr.Markdown(label="Generated SEO Strategy")
 
-    # Update syntax highlighting based on radio button
+    # Update syntax highlighting dynamically
     input_type.change(lambda x: gr.Code(language=x), inputs=input_type, outputs=code_input)
     
     # Run logic
@@ -88,6 +89,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         outputs=output_markdown
     )
 
-# Launch the app
+# Launch
 if __name__ == "__main__":
     demo.launch()