Update app.py

app.py

@@ -44,30 +44,44 @@ def query_model(model_id, prompt, max_tokens=300, temperature=0.7):
     except Exception as e:
         return f"Exception: {str(e)}"
 
-def collect_responses(prompt_text, max_tokens=300, temperature=0.7):
-    """Collect responses from all models"""
+def collect_batch_responses(prompts_text, max_tokens=300, temperature=0.7):
+    """Collect responses from all models for multiple prompts"""
+    # Split prompts by newline and filter empty lines
+    prompts = [p.strip() for p in prompts_text.split('\n') if p.strip()]
+    
+    if not prompts:
+        return pd.DataFrame(), None, "⚠️ No prompts provided"
+    
     results = []
+    status_msg = f"Processing {len(prompts)} prompt(s) across {len(MODELS)} model(s)..."
     
-    for model in MODELS:
-        response = query_model(model, prompt_text, max_tokens, temperature)
-        
-        results.append({
-            'timestamp': datetime.now().isoformat(),
-            'prompt': prompt_text,
-            'model': model.split('/')[-1],  # Short model name
-            'response': response
-        })
+    # Process each prompt independently
+    for prompt_idx, prompt_text in enumerate(prompts, 1):
+        # Each prompt gets fresh responses from all models
+        for model in MODELS:
+            response = query_model(model, prompt_text, max_tokens, temperature)
+            
+            results.append({
+                'timestamp': datetime.now().isoformat(),
+                'prompt_number': prompt_idx,
+                'prompt': prompt_text,
+                'model': model.split('/')[-1],  # Short model name
+                'full_model': model,
+                'response': response
+            })
     
     df = pd.DataFrame(results)
-    csv_filename = f"responses_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+    csv_filename = f"batch_responses_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
     df.to_csv(csv_filename, index=False)
     
-    return df, csv_filename
+    completion_msg = f"✅ Completed! Processed {len(prompts)} prompt(s) × {len(MODELS)} models = {len(results)} total responses"
+    
+    return df, csv_filename, completion_msg
 
 # Gradio interface
-with gr.Blocks(title="LLM Response Collector") as demo:
+with gr.Blocks(title="Batch LLM Response Collector") as demo:
     gr.Markdown("""
-    # 🤖 Multi-LLM Response Collector
+    # 🤖 Batch Multi-LLM Response Collector
     
     Compare responses from 4 different LLMs (2 US, 2 China):
     - **Llama 3.3 70B** 🇺🇸 - Meta's latest model (USA)
@@ -75,15 +89,15 @@ with gr.Blocks(title="LLM Response Collector") as demo:
     - **Qwen 2.5 72B** 🇨🇳 - Alibaba's flagship model (China)
     - **DeepSeek R1** 🇨🇳 - DeepSeek's reasoning model (China)
     
-    Each query is independent with no conversation history.
+    **Batch Processing:** Enter multiple prompts (one per line). Each prompt is processed independently with no conversation history or cross-contamination.
     """)
     
     with gr.Row():
         with gr.Column():
             prompt_input = gr.Textbox(
-                label="Enter your prompt",
-                placeholder="What is artificial intelligence?",
-                lines=4
+                label="Enter your prompts (one per line)",
+                placeholder="What is artificial intelligence?\nExplain quantum computing.\nDescribe machine learning.",
+                lines=8
             )
             
             with gr.Row():
@@ -102,23 +116,32 @@ with gr.Blocks(title="LLM Response Collector") as demo:
                     label="Temperature"
                 )
             
-            submit_btn = gr.Button("Get Responses", variant="primary", size="lg")
+            submit_btn = gr.Button("Process Batch", variant="primary", size="lg")
     
+    status_output = gr.Textbox(label="Status", interactive=False)
     df_output = gr.Dataframe(label="Results", wrap=True)
     csv_output = gr.File(label="Download CSV")
     
     submit_btn.click(
-        fn=collect_responses,
+        fn=collect_batch_responses,
         inputs=[prompt_input, max_tokens, temperature],
-        outputs=[df_output, csv_output]
+        outputs=[df_output, csv_output, status_output]
     )
     
     gr.Markdown("""
     ---
     ### 📝 About
     - Uses Hugging Face Router API
-    - Each response is independent (no chat history)
-    - Results saved to CSV for analysis
+    - **Each prompt is completely independent** - no conversation history
+    - Multiple prompts processed sequentially (one per line)
+    - Each prompt gets fresh responses from all 4 models
+    - Results include prompt_number for easy tracking
+    - All results saved to timestamped CSV for analysis
+    
+    ### 💡 Tips
+    - Separate prompts with line breaks
+    - Empty lines are automatically ignored
+    - Processing time scales with: (number of prompts) × (number of models)
     """)
 
 if __name__ == "__main__":