Create app.py

app.py

@@ -0,0 +1,129 @@
+import gradio as gr
+from faster_whisper import WhisperModel
+from llama_cpp import Llama
+from brave import Brave
+import os
+import time
+
+# Initialize models
+print("Loading models...")
+whisper_model = WhisperModel("tiny", device="cpu", compute_type="int8")
+llm = Llama.from_pretrained(
+    repo_id="Qwen/Qwen2.5-0.5B-Instruct-GGUF",
+    filename="qwen2.5-0.5b-instruct-q4_k_m.gguf",
+    n_ctx=2048,
+    n_threads=4,
+    verbose=False
+)
+
+# Initialize Brave Search
+brave_client = Brave(api_key=os.getenv("BRAVE_API_KEY", ""))
+
+def search_web(query, max_results=3):
+    """Perform web search using Brave API"""
+    try:
+        results = brave_client.search(q=query, count=max_results)
+        web_results = results.web_results if hasattr(results, 'web_results') else []
+        
+        context = ""
+        for i, result in enumerate(web_results[:max_results], 1):
+            context += f"\n[{i}] {result.title}\n{result.description}\n"
+        return context.strip()
+    except Exception as e:
+        return f"Search failed: {str(e)}"
+
+def process_audio(audio_path, question_text=None):
+    """Main pipeline: audio -> text -> search -> answer"""
+    start_time = time.time()
+    
+    # Step 1: Transcribe audio if provided
+    if audio_path:
+        segments, _ = whisper_model.transcribe(audio_path, language="en")
+        question = " ".join([seg.text for seg in segments])
+    else:
+        question = question_text
+    
+    if not question:
+        return "No input provided", 0.0
+    
+    transcription_time = time.time() - start_time
+    
+    # Step 2: Web search for political/current info
+    search_start = time.time()
+    search_results = search_web(question)
+    search_time = time.time() - search_start
+    
+    # Step 3: Generate answer with LLM
+    llm_start = time.time()
+    prompt = f"""You are a helpful assistant. Answer the question based on the context below.
+
+Context from web search:
+{search_results}
+
+Question: {question}
+
+Answer briefly and accurately:"""
+    
+    response = llm(
+        prompt,
+        max_tokens=150,
+        temperature=0.3,
+        top_p=0.9,
+        stop=["Question:", "\n\n"],
+        echo=False
+    )
+    
+    answer = response['choices'][0]['text'].strip()
+    llm_time = time.time() - llm_start
+    
+    total_time = time.time() - start_time
+    
+    timing_info = f"\n\n⏱️ Timing: Transcription={transcription_time:.2f}s | Search={search_time:.2f}s | LLM={llm_time:.2f}s | Total={total_time:.2f}s"
+    
+    return answer + timing_info, total_time
+
+# Create Gradio interface
+with gr.Blocks(title="Fast Q&A with Web Search") as demo:
+    gr.Markdown("# 🎤 Fast Political Q&A System\nAsk questions via audio or text. Answers in ~3 seconds!")
+    
+    with gr.Tab("Audio Input"):
+        audio_input = gr.Audio(type="filepath", label="Record or upload audio question")
+        audio_submit = gr.Button("Submit Audio", variant="primary")
+        audio_output = gr.Textbox(label="Answer", lines=6)
+        audio_time = gr.Number(label="Response Time (seconds)")
+        
+        audio_submit.click(
+            fn=lambda x: process_audio(x, None),
+            inputs=[audio_input],
+            outputs=[audio_output, audio_time],
+            api_name="audio_query"
+        )
+    
+    with gr.Tab("Text Input"):
+        text_input = gr.Textbox(label="Type your question", placeholder="Who won the 2024 elections?")
+        text_submit = gr.Button("Submit Text", variant="primary")
+        text_output = gr.Textbox(label="Answer", lines=6)
+        text_time = gr.Number(label="Response Time (seconds)")
+        
+        text_submit.click(
+            fn=lambda x: process_audio(None, x),
+            inputs=[text_input],
+            outputs=[text_output, text_time],
+            api_name="text_query"
+        )
+    
+    gr.Markdown("""
+    ### 📡 API Usage
+    ```
+    # Upload audio file
+    curl -F "files=@audio.mp3" https://YOUR-SPACE-URL/upload
+    
+    # Make query
+    curl -X POST https://YOUR-SPACE-URL/call/audio_query \\
+      -H "Content-Type: application/json" \\
+      -d '{"data": [{"path": "/tmp/uploaded_audio.mp3"}]}'
+    ```
+    """)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)