Update app.py

app.py

@@ -1,102 +1,94 @@
 import gradio as gr
 import spaces  # Required for ZeroGPU
-from transformers import pipeline, AutoTokenizer
+from transformers import pipeline
 from duckduckgo_search import DDGS
 from datetime import datetime
+import re  # Added for regular expressions
 
 # Initialize a lightweight text generation model on CPU
-generator = pipeline("text-generation", model="distilgpt2", device=-1)  # -1 ensures CPU by default
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")  # For better token handling
+generator = pipeline("text-generation", model="distilgpt2", device=-1)  # -1 ensures CPU
 
 # Web search function (CPU-based)
-def get_web_results(query: str, max_results: int = 5) -> list:
-    """Fetch web results synchronously for Zero GPU compatibility, focusing on high-quality sources."""
+def get_web_results(query: str, max_results: int = 3) -> list:
+    """Fetch web results synchronously for Zero GPU compatibility."""
     try:
         with DDGS() as ddgs:
             results = list(ddgs.text(query, max_results=max_results))
-            # Filter for high-quality, relevant results (e.g., educational, authoritative sites)
-            filtered_results = [
-                {"title": r.get("title", "No Title"), "snippet": r["body"], "url": r["href"]}
-                for r in results
-                if any(domain in r["href"] for domain in ["geeksforgeeks.org", "realpython.com", "coursera.org", "udemy.com", "stackexchange.com"])
-                or "edu" in r["href"]
-            ]
-            return filtered_results if filtered_results else results  # Fall back to all results if no high-quality ones found
+            return [{"title": r.get("title", "No Title"), "snippet": r["body"], "url": r["href"]} for r in results]
     except Exception as e:
         return [{"title": "Error", "snippet": f"Failed to fetch results: {str(e)}", "url": "#"}]
 
-# Format prompt for the AI model to generate high-quality, structured answers
+
+# Format prompt for the AI model (CPU-based) - IMPROVED
 def format_prompt(query: str, web_results: list) -> str:
-    """Create a detailed prompt with web context to guide the model toward high-quality answers."""
-    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S IST")
-    context = "\n".join([f"- {r['title']}: {r['snippet']}" for r in web_results])
-    return f"""Current Time: {current_time}
+    """Create a concise prompt with web context, explicitly instructing citation."""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    context = ""
+    for i, r in enumerate(web_results, 1):  # Start index at 1 for citations
+        context += f"- [{i}] {r['title']}: {r['snippet']}\n"
+
+    return f"""
+Time: {current_time}
 Query: {query}
-Web Context (High-Quality Sources):
+Web Context:
 {context}
-Provide a detailed, step-by-step answer in markdown format with clear headings (e.g., #, ##), bullet points, and citations [1], [2], etc. Ensure the answer is structured, relevant, and visually appealing, addressing the user's intent comprehensively. If the query is informational (e.g., 'what,' 'how,' 'why'), offer in-depth insights, examples, and practical advice. If no high-quality answer is possible, state, 'I couldn’t find sufficient high-quality information to provide a detailed answer, but here’s what I found:' followed by a summary of web results."""
+Provide a concise answer in markdown format.  Cite relevant sources using the bracketed numbers provided (e.g., [1], [2]).  Focus on direct answers.  If the context doesn't contain the answer, say that the information wasn't found in the provided sources.
+    """.strip()
+
+
+# GPU-decorated answer generation - IMPROVED
+@spaces.GPU(duration=120)  # Allow up to 120 seconds of GPU time
+def generate_answer(prompt: str, web_results: list) -> str:
+    """Generate and post-process the research answer."""
+    response = generator(prompt, max_new_tokens=150, num_return_sequences=1, truncation=True, return_full_text=False)[0]["generated_text"]
+
+    # Basic post-processing (can be expanded):
+    response = response.strip()
+
+    # Replace citation placeholders *if* they exist in the web_results.
+    for i in range(1, len(web_results) + 1):
+      response = response.replace(f"[{i}]", f"[^{i}^](#{i})")  #Markdown link to source
 
-# GPU-decorated answer generation for high-quality output
-@spaces.GPU(duration=180)  # Increased duration for more detailed generation
-def generate_answer(prompt: str) -> str:
-    """Generate a detailed, high-quality research answer using GPU."""
-    # Tokenize and truncate prompt to fit within limits
-    tokenized_prompt = tokenizer(prompt, truncation=True, max_length=200, return_tensors="pt")
-    input_ids = tokenized_prompt["input_ids"]
-    
-    # Generate response with more tokens and better sampling for quality
-    response = generator(
-        prompt,
-        max_new_tokens=400,  # Increased for more detailed output
-        num_return_sequences=1,
-        truncation=True,
-        do_sample=True,
-        temperature=0.7,  # Controlled randomness for coherent, detailed output
-        top_p=0.9,  # Focus on top probabilities for quality
-        top_k=50  # Limit to top 50 tokens for better coherence
-    )[0]["generated_text"]
-    
-    answer_start = response.find("Provide a detailed") + len("Provide a detailed, step-by-step answer in markdown format with clear headings (e.g., #, ##), bullet points, and citations [1], [2], etc. Ensure the answer is structured, relevant, and visually appealing, addressing the user's intent comprehensively. If the query is informational (e.g., 'what,' 'how,' 'why'), offer in-depth insights, examples, and practical advice. If no high-quality answer is possible, state, 'I couldn’t find sufficient high-quality information to provide a detailed answer, but here’s what I found:' followed by a summary of web results.")
-    return response[answer_start:].strip() if answer_start > -1 else "I couldn’t find sufficient high-quality information to provide a detailed answer, but here’s what I found:\n\n" + "\n".join([f"- {r['title']}: {r['snippet']}" for r in get_web_results(query, max_results=3)])
+    return response
 
-# Format sources for display with enhanced styling
+
+# Format sources for display (CPU-based) - IMPROVED
 def format_sources(web_results: list) -> str:
-    """Create a styled HTML list of sources with better visuals for high-quality presentation."""
+    """Create an HTML list of sources with anchors."""
     if not web_results:
-        return "<div class='no-sources'>No sources available</div>"
-    
+        return "<div>No sources available</div>"
     sources_html = "<div class='sources-list'>"
     for i, res in enumerate(web_results, 1):
         sources_html += f"""
-        <div class='source-item'>
+        <div class='source-item' id='{i}'>
             <span class='source-number'>[{i}]</span>
-            <a href='{res['url']}' target='_blank' class='source-link'>{res['title']}</a>
-            <p class='source-snippet'>{res['snippet'][:150]}...</p>
+            <a href='{res['url']}' target='_blank'>{res['title']}</a>: {res['snippet'][:100]}...
         </div>
         """
     sources_html += "</div>"
     return sources_html
 
-# Main processing function
+# Main processing function - IMPROVED
 def process_deep_research(query: str, history: list):
-    """Handle the deep research process for any query with high-quality output."""
-    if not history:
-        history = []
-    
+    """Handle the deep research process, including history updates."""
+
     # Fetch web results (CPU)
     web_results = get_web_results(query)
-    sources_html = format_sources(web_results)
 
-    # Generate answer (GPU via @spaces.GPU)
+    # Generate answer (GPU)
     prompt = format_prompt(query, web_results)
-    answer = generate_answer(prompt)
-    
-    # Convert history to messages format (role/content)
-    new_history = history + [{"role": "user", "content": query}, {"role": "assistant", "content": answer}]
-    
+    answer = generate_answer(prompt, web_results)
+
+    sources_html = format_sources(web_results)
+
+    # Update history (using the Gradio Chatbot's expected format)
+    new_history = history + [[query, answer + "\n\n" + sources_html]]
+
     return answer, sources_html, new_history
 
-# Enhanced CSS for a polished, visually appealing UI
+
+
+# Custom CSS -  Slightly adjusted for better spacing
 css = """
 body {
     font-family: 'Arial', sans-serif;
@@ -104,165 +96,104 @@ body {
     color: #ffffff;
 }
 .gradio-container {
-    max-width: 1200px;
+    max-width: 900px;
     margin: 0 auto;
-    padding: 20px;
+    padding: 15px;
 }
 .header {
     text-align: center;
-    padding: 20px;
+    padding: 15px;
     background: linear-gradient(135deg, #2c3e50, #3498db);
-    border-radius: 12px;
-    margin-bottom: 20px;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+    border-radius: 8px;
+    margin-bottom: 15px;
 }
-.header h1 { font-size: 2.5em; margin: 0; color: #ffffff; text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5); }
-.header p { color: #bdc3c7; font-size: 1.1em; }
+.header h1 { font-size: 2em; margin: 0; color: #ffffff; }
+.header p { color: #bdc3c7; font-size: 1em; }
 .search-box {
     background: #2c2c2c;
-    padding: 15px;
-    border-radius: 12px;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
-    margin-bottom: 20px;
+    padding: 10px;
+    border-radius: 8px;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
 }
 .search-box input {
-    background: #3a3a3e !important;
+    background: #3a3a3a !important;
     color: #ffffff !important;
     border: none !important;
-    border-radius: 8px !important;
-    padding: 10px;
-    font-size: 1em;
+    border-radius: 5px !important;
 }
 .search-box button {
     background: #3498db !important;
     border: none !important;
-    border-radius: 8px !important;
-    padding: 10px 20px;
-    font-size: 1em;
-    transition: background 0.3s;
+    border-radius: 5px !important;
 }
-.search-box button:hover { background: #2980b9 !important; }
 .results-container {
-    margin-top: 20px;
+    margin-top: 15px;
     display: flex;
-    gap: 20px;
+    flex-direction: column; /* Stack answer and sources vertically */
+    gap: 15px;
 }
 .answer-box {
-    flex: 2;
+    /* flex: 2; Removed flex property */
     background: #2c2c2c;
-    padding: 20px;
-    border-radius: 12px;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
-    overflow-y: auto;
-    max-height: 600px;
-}
-.answer-box .markdown {
-    color: #ecf0f1;
-    line-height: 1.6;
-}
-.answer-box .markdown h1 {
-    color: #ffffff;
-    border-bottom: 2px solid #3498db;
-    padding-bottom: 10px;
-}
-.answer-box .markdown h2 {
-    color: #a8b5c3;
-    margin-top: 20px;
-}
-.answer-box .markdown ul {
-    list-style-type: none;
-    padding-left: 20px;
-}
-.answer-box .markdown ul li::before {
-    content: "•";
-    color: #3498db;
-    display: inline-block;
-    width: 1em;
-    margin-left: -1em;
+    padding: 15px;
+    border-radius: 8px;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
 }
-.answer-box .markdown a { color: #60a5fa; text-decoration: underline; }
+.answer-box .markdown { color: #ecf0f1; line-height: 1.5; }
 .sources-list {
-    flex: 1;
+    /* flex: 1;  Removed flex property */
     background: #2c2c2c;
-    padding: 15px;
-    border-radius: 12px;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
-    max-height: 600px;
-    overflow-y: auto;
-}
-.no-sources { color: #a8a9ab; font-style: italic; }
-.source-item {
-    margin-bottom: 15px;
     padding: 10px;
-    background: #3a3a3e;
     border-radius: 8px;
-    transition: background 0.2s;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
 }
-.source-item:hover { background: #4a4b4e; }
-.source-number { color: #3498db; font-weight: bold; margin-right: 10px; }
-.source-link { color: #60a5fa; font-weight: 500; display: block; margin-bottom: 5px; }
-.source-snippet { color: #e5e7eb; font-size: 0.9em; line-height: 1.4; }
+.source-item { margin-bottom: 8px; }
+.source-number { color: #3498db; font-weight: bold; margin-right: 5px; }
+.source-item a { color: #3498db; text-decoration: none; }
+.source-item a:hover { text-decoration: underline; }
 .history-box {
-    margin-top: 20px;
+    margin-top: 15px;
     background: #2c2c2c;
-    padding: 15px;
-    border-radius: 12px;
-    max-height: 300px;
-    overflow-y: auto;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+    padding: 10px;
+    border-radius: 8px;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
 }
+
 """
 
 # Gradio app setup with Blocks
 with gr.Blocks(title="Deep Research Engine - ZeroGPU", css=css) as demo:
-    history_state = gr.State([])
-
     # Header
     with gr.Column(elem_classes="header"):
         gr.Markdown("# Deep Research Engine")
-        gr.Markdown("Your gateway to in-depth, high-quality research for any query with real-time web insights.")
+        gr.Markdown("Fast, in-depth answers powered by web insights (ZeroGPU).")
 
     # Search input and button
     with gr.Row(elem_classes="search-box"):
-        search_input = gr.Textbox(label="", placeholder="Ask anything (e.g., 'What are the latest AI trends in 2025?')", lines=2)
+        search_input = gr.Textbox(label="", placeholder="Ask anything...", lines=2)
         search_btn = gr.Button("Research", variant="primary")
 
-    # Results layout
-    with gr.Row(elem_classes="results-container"):
-        with gr.Column():
-            answer_output = gr.Markdown(label="Research Findings", elem_classes="answer-box")
-        with gr.Column():
-            sources_output = gr.HTML(label="Sources", elem_classes="sources-list")
+    # Results layout -  Now using a single Chatbot component
+    history = gr.Chatbot(elem_classes="history-box", label="Research Results & History")
 
-    # Chat history (using messages format)
-    with gr.Row():
-        history_display = gr.Chatbot(label="History", elem_classes="history-box", type="messages")
 
-    # Event handling
-    def handle_search(query, history):
-        answer, sources, new_history = process_deep_research(query, history)
-        return answer, sources, new_history
+    # Event handling - Simplified
+    def handle_search(query, history_data):
+        answer, sources, new_history = process_deep_research(query, history_data)
+        return new_history
 
     search_btn.click(
         fn=handle_search,
-        inputs=[search_input, history_state],
-        outputs=[answer_output, sources_output, history_display]
-    ).then(
-        fn=lambda x: x,
-        inputs=[history_display],
-        outputs=[history_state]
+        inputs=[search_input, history],
+        outputs=[history]
     )
-
     search_input.submit(
         fn=handle_search,
-        inputs=[search_input, history_state],
-        outputs=[answer_output, sources_output, history_display]
-    ).then(
-        fn=lambda x: x,
-        inputs=[history_display],
-        outputs=[history_state]
+        inputs=[search_input, history],
+        outputs=[history]
     )
 
+
 # Launch the app
 if __name__ == "__main__":
     demo.launch()