Update app.py

app.py

@@ -7,27 +7,39 @@ import torch
 import base64
 import tempfile
 import os
+import logging
 from datetime import datetime
 
+# Setup comprehensive logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
 # Initialize models
-print("Loading Whisper model...")
+logger.info("Loading Whisper model...")
 whisper_model = WhisperModel("tiny", device="cpu", compute_type="int8")
 
-print("Loading LLM...")
-model_name = "Qwen/Qwen2.5-1.5B-Instruct"  # Upgraded to 1.5B for better quality
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+logger.info("Loading Phi-2 model (faster inference)...")
+model_name = "microsoft/phi-2"  # 2.7B - Faster CPU inference than Qwen
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype=torch.float32,
     device_map="cpu",
-    low_cpu_mem_usage=True
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
 )
+tokenizer.pad_token = tokenizer.eos_token
 
 # Initialize DuckDuckGo Search
 ddgs = DDGS(timeout=3)
+logger.info("All models loaded successfully!")
 
 def search_web(query, max_results=3):
     """Perform web search using DuckDuckGo"""
+    logger.info(f"[SEARCH] Query: {query}")
     try:
         results = ddgs.text(
             keywords=query,
@@ -42,108 +54,122 @@ def search_web(query, max_results=3):
             title = result.get('title', '')
             body = result.get('body', '')
             context += f"\n[Source {i}] {title}\n{body}\n"
+            logger.info(f"[SEARCH] Result {i}: {title[:50]}...")
         
-        return context.strip() if context else "No search results found."
+        if not context:
+            logger.warning("[SEARCH] No results found!")
+            return "No search results found."
+            
+        logger.info(f"[SEARCH] Successfully retrieved {max_results} results")
+        return context.strip()
     
     except Exception as e:
+        logger.error(f"[SEARCH] Error: {str(e)}")
         return f"Search failed: {str(e)}"
 
 def transcribe_audio_base64(audio_base64):
     """Transcribe audio from base64 string (for Pluely STT endpoint)"""
+    logger.info("[PLUELY STT] Received audio transcription request")
     try:
         audio_bytes = base64.b64decode(audio_base64)
+        logger.info(f"[PLUELY STT] Decoded audio size: {len(audio_bytes)} bytes")
         
         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
             temp_audio.write(audio_bytes)
             temp_path = temp_audio.name
         
+        logger.info(f"[PLUELY STT] Transcribing audio...")
         segments, _ = whisper_model.transcribe(temp_path, language="en", beam_size=1)
         transcription = " ".join([seg.text for seg in segments])
         
         os.unlink(temp_path)
         
+        logger.info(f"[PLUELY STT] Transcription successful: {transcription[:50]}...")
         return {"text": transcription.strip()}
     
     except Exception as e:
+        logger.error(f"[PLUELY STT] Error: {str(e)}")
         return {"error": f"Transcription failed: {str(e)}"}
 
 def generate_answer(text_input):
-    """Generate complete answer with context"""
+    """Generate answer using ONLY search results"""
+    logger.info(f"[PLUELY AI] Received question: {text_input}")
     try:
         if not text_input or text_input.strip() == "":
             return "No input provided"
         
-        # Get current date for context
         current_date = datetime.now().strftime("%B %d, %Y")
         
-        # Web search for current information
+        # Web search - CRITICAL for answer
+        logger.info("[PLUELY AI] Starting web search...")
         search_results = search_web(text_input, max_results=3)
+        logger.info(f"[PLUELY AI] Search results length: {len(search_results)} chars")
         
-        # Enhanced prompt for comprehensive responses
-        messages = [
-            {"role": "system", "content": f"""You are a knowledgeable assistant providing comprehensive, well-researched answers. Today's date is {current_date}.
+        # Strict prompt - MUST use search results
+        prompt = f"""You are a fact-checker assistant. Today is {current_date}.
 
-When answering:
-1. Provide the direct answer first
-2. Add relevant context and background information
-3. Include recent developments or current status when applicable
-4. Be informative but concise (150-200 words)
-5. Use the web search results to ensure accuracy and currency"""},
-            {"role": "user", "content": f"""Based on these current web search results:
+CRITICAL INSTRUCTION: You MUST ONLY use information from the search results below. DO NOT use your training knowledge.
 
+Web Search Results:
 {search_results}
 
 Question: {text_input}
 
-Provide a comprehensive answer that includes:
-- Direct answer to the question
-- Relevant context and background
-- Recent developments (as of {current_date})
-- Key points the user should know
+Instructions:
+1. Read the search results carefully
+2. Answer ONLY based on what's in the search results
+3. If search results don't contain the answer, say "The search results don't provide enough information"
+4. Include relevant dates and facts from the search results
+5. Keep answer to 100-150 words
 
-Answer:"""}
-        ]
-        
-        text = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
-        )
-        
-        inputs = tokenizer([text], return_tensors="pt").to("cpu")
+Answer based STRICTLY on search results:"""
+
+        logger.info("[PLUELY AI] Generating answer...")
+        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1500).to("cpu")
         
         with torch.no_grad():
             outputs = model.generate(
                 **inputs,
-                max_new_tokens=250,  # Increased from 80 to 250
-                temperature=0.3,     # Slightly higher for more natural responses
+                max_new_tokens=200,
+                temperature=0.4,
                 do_sample=True,
                 top_p=0.9,
-                repetition_penalty=1.1,
+                repetition_penalty=1.2,
                 pad_token_id=tokenizer.eos_token_id
             )
         
         response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
-        return response.strip()
+        answer = response.strip()
+        
+        logger.info(f"[PLUELY AI] Answer generated ({len(answer)} chars): {answer[:100]}...")
+        return answer
         
     except Exception as e:
+        logger.error(f"[PLUELY AI] Error: {str(e)}")
         return f"Error: {str(e)}"
 
 def process_audio(audio_path, question_text):
     """Main pipeline - returns tuple (answer, time)"""
     start_time = time.time()
+    logger.info("="*50)
+    logger.info("[MAIN] New request received")
     
     # Transcribe if audio provided
     if audio_path:
+        logger.info(f"[MAIN] Audio file provided: {audio_path}")
         try:
             segments, _ = whisper_model.transcribe(audio_path, language="en", beam_size=1)
             question = " ".join([seg.text for seg in segments])
+            logger.info(f"[MAIN] Transcription: {question}")
         except Exception as e:
+            logger.error(f"[MAIN] Transcription error: {str(e)}")
             return f"❌ Transcription error: {str(e)}", 0.0
     else:
         question = question_text
+        logger.info(f"[MAIN] Text input: {question}")
     
     if not question or question.strip() == "":
+        logger.warning("[MAIN] No input provided")
         return "❌ No input provided", 0.0
     
     transcription_time = time.time() - start_time
@@ -159,7 +185,10 @@ def process_audio(audio_path, question_text):
     llm_time = time.time() - llm_start
     
     total_time = time.time() - start_time
-    time_emoji = "🟢" if total_time < 5.0 else "🟡" if total_time < 7.0 else "🔴"
+    time_emoji = "🟢" if total_time < 4.0 else "🟡" if total_time < 6.0 else "🔴"
+    
+    logger.info(f"[MAIN] Total time: {total_time:.2f}s (Trans={transcription_time:.2f}s, Search={search_time:.2f}s, LLM={llm_time:.2f}s)")
+    logger.info("="*50)
     
     timing_info = f"\n\n{time_emoji} **Performance:** Trans={transcription_time:.2f}s | Search={search_time:.2f}s | LLM={llm_time:.2f}s | **Total={total_time:.2f}s**"
     
@@ -175,12 +204,12 @@ def text_handler(text_input):
     return process_audio(None, text_input)
 
 # Gradio interface
-with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="Fast Political Q&A - Phi-2", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
-    # 🎯 Enhanced Political Q&A System
-    **Comprehensive answers with context** - Powered by Qwen2.5-1.5B
+    # ⚡ Fast Political Q&A System
+    **Search-grounded answers** - Powered by Phi-2 (2.7B)
     
-    **Features:** Whisper-tiny + Qwen2.5-1.5B + DuckDuckGo + Rich contextual responses
+    **Features:** Whisper-tiny + Phi-2 (fast CPU inference) + DuckDuckGo + Search-only responses
     """)
     
     with gr.Tab("🎙️ Audio Input"):
@@ -194,7 +223,7 @@ with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
                 audio_submit = gr.Button("🚀 Submit Audio", variant="primary", size="lg")
             
             with gr.Column():
-                audio_output = gr.Textbox(label="Comprehensive Answer", lines=12, show_copy_button=True)
+                audio_output = gr.Textbox(label="Search-Grounded Answer", lines=10, show_copy_button=True)
                 audio_time = gr.Number(label="Response Time (seconds)", precision=2)
         
         audio_submit.click(
@@ -209,13 +238,13 @@ with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
             with gr.Column():
                 text_input = gr.Textbox(
                     label="Type your question",
-                    placeholder="Who is the current US president?",
+                    placeholder="Is internet shut down in Bareilly today?",
                     lines=3
                 )
                 text_submit = gr.Button("🚀 Submit Text", variant="primary", size="lg")
             
             with gr.Column():
-                text_output = gr.Textbox(label="Comprehensive Answer", lines=12, show_copy_button=True)
+                text_output = gr.Textbox(label="Search-Grounded Answer", lines=10, show_copy_button=True)
                 text_time = gr.Number(label="Response Time (seconds)", precision=2)
         
         text_submit.click(
@@ -227,10 +256,10 @@ with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
         
         gr.Examples(
             examples=[
+                ["Is internet shut down in Bareilly today?"],
                 ["Who won the 2024 US presidential election?"],
                 ["What is the current inflation rate in India?"],
-                ["Who is the prime minister of UK and what are their key policies?"],
-                ["Explain the latest developments in AI regulation"]
+                ["What happened in Israel Palestine conflict today?"]
             ],
             inputs=text_input
         )
@@ -238,37 +267,35 @@ with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
     # API endpoints for Pluely
     with gr.Tab("🔌 Pluely Integration"):
         gr.Markdown("""
-        ## API Endpoints for Pluely
+        ## API Endpoints (All requests logged in console)
         
-        ### STT Endpoint (Audio Transcription)
+        ### STT Endpoint
         ```
         curl -X POST https://archcoder-basic-app.hf.space/call/transcribe_stt \\
           -H "Content-Type: application/json" \\
           -d '{"data": ["BASE64_AUDIO_DATA"]}'
         ```
-        **Response:** `{"data": [{"text": "transcribed text"}]}`
         
-        ### AI Endpoint (Enhanced Responses)
+        ### AI Endpoint
         ```
         curl -X POST https://archcoder-basic-app.hf.space/call/answer_ai \\
           -H "Content-Type: application/json" \\
           -d '{"data": ["Your question here"]}'
         ```
-        **Response:** `{"data": ["Comprehensive answer with context"]}`
         
         ## Pluely Configuration
         
-        ### Custom STT Provider:
+        **STT Provider:**
         ```
         curl https://archcoder-basic-app.hf.space/call/transcribe_stt -H "Content-Type: application/json" -d '{"data": ["{{AUDIO_BASE64}}"]}'
         ```
-        **Response Path:** `data[0].text` | **Streaming:** OFF
+        **Response Path:** `data[0].text`
         
-        ### Custom AI Provider:
+        **AI Provider:**
         ```
         curl https://archcoder-basic-app.hf.space/call/answer_ai -H "Content-Type: application/json" -d '{"data": ["{{TEXT}}"]}'
         ```
-        **Response Path:** `data[0]` | **Streaming:** OFF
+        **Response Path:** `data[0]`
         """)
         
         # Hidden components for API endpoints
@@ -296,12 +323,12 @@ with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
     
     gr.Markdown("""
     ---
-    **Model:** Qwen2.5-1.5B-Instruct (3x larger for better answers)  
-    **Output:** 150-200 words with context and background  
-    **Date-aware:** Responses reference current date ({})  
+    **Model:** Phi-2 (2.7B) - Fast CPU inference, excellent reasoning  
+    **Output:** 100-150 words based STRICTLY on web search results  
+    **Logging:** All Pluely requests logged in console (check Logs tab)  
     
-    🟢 = Under 5s | 🟡 = 5-7s | 🔴 = Over 7s
-    """.format(datetime.now().strftime("%B %d, %Y")))
+    🟢 = Under 4s | 🟡 = 4-6s | 🔴 = Over 6s
+    """)
 
 if __name__ == "__main__":
     demo.queue(max_size=5)