Update app.py

app.py

@@ -8,31 +8,30 @@ from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 from langchain_core.language_models.llms import LLM
 from langchain_core.callbacks import CallbackManagerForLLMRun
-from typing import Optional, List, Any
+from typing import Optional, List, Dict, Any
+import requests
+from dotenv import load_dotenv
 from groq import Groq
 import urllib.parse
-import feedparser
+import feedparser  # Added for the new function
 
-# Load Groq API Key from environment
-GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+# Load environment variables
+load_dotenv()
 
 
-# -------------------------------
-# LangChain Wrapper for Groq LLM
-# -------------------------------
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+
+# Custom wrapper for Groq to make it LangChain compatible
 class GroqWrapper(LLM):
     client: Any
     model_name: str = "llama-3.3-70b-versatile"
     temperature: float = 0.7
-
+    
     @property
     def _llm_type(self) -> str:
         return "groq"
-
-    @property
-    def _identifying_params(self):
-        return {"model_name": self.model_name}
-
+    
     def _call(
         self,
         prompt: str,
@@ -44,51 +43,44 @@ class GroqWrapper(LLM):
             messages=[{"role": "user", "content": prompt}],
             model=self.model_name,
             temperature=self.temperature,
+            **kwargs
         )
-        # Groq SDK returns message as dict
-        return response.choices[0].message['content']
+        return response.choices[0].message.content
 
-
-# -------------------------------
-# Globals
-# -------------------------------
+# Initialize global variables
 vectorstore = None
 qa_chain = None
 groq_llm = None
 
-
-# -------------------------------
-# PDF Upload + Processing
-# -------------------------------
 def upload_pdf(file):
     global vectorstore, qa_chain, groq_llm
-
+    
     try:
+        # Initialize Groq LLM wrapper
         groq_llm = GroqWrapper(client=Groq(api_key=GROQ_API_KEY))
-
-        reader = PdfReader(file)
-        text = ""
-        for page in reader.pages:
-            extracted = page.extract_text()
-            if extracted:
-                text += extracted + "\n"
-
+        
+        # PDF Text Extraction
+        text = "".join(
+            page.extract_text() or ""
+            for page in PdfReader(file).pages
+        )
         if not text.strip():
-            return "Error: PDF contains no readable text."
+            return "Error: No readable text found in PDF"
 
-        splitter = RecursiveCharacterTextSplitter(
+        # Text Chunking
+        texts = RecursiveCharacterTextSplitter(
             chunk_size=1000,
             chunk_overlap=200
-        )
-        chunks = splitter.split_text(text)
+        ).split_text(text)
 
+        # Using HuggingFace embeddings
         embeddings = HuggingFaceEmbeddings(
-            model_name="sentence-transformers/all-mpnet-base-v2",
-            model_kwargs={"device": "cpu"}
+            model_name="sentence-transformers/all-mpnet-base-v2"
         )
+        
+        vectorstore = FAISS.from_texts(texts, embeddings)
 
-        vectorstore = FAISS.from_texts(chunks, embeddings)
-
+        # QA System Initialization
         qa_chain = RetrievalQA.from_chain_type(
             llm=groq_llm,
             chain_type="stuff",
@@ -96,63 +88,68 @@ def upload_pdf(file):
             return_source_documents=True
         )
 
-        return "✅ PDF processed successfully!"
-
+        return "PDF processed successfully!"
     except Exception as e:
         return f"Error: {str(e)}"
 
-
-# -------------------------------
-# Ask a Question
-# -------------------------------
 def ask_question(query):
     global qa_chain
-
+    
     if qa_chain is None:
         return "Please upload a PDF first.", ""
 
     try:
-        prompt_template = """
-Use the following context to answer the question.
-If you are unsure, say you do not know.
-
-Context:
-{context}
-
-Question: {question}
-
-Answer:
-"""
+        # Create a custom prompt template for better answers
+        prompt_template = """Use the following pieces of context to answer the question at the end. 
+        If you don't know the answer, just say that you don't know, don't try to make up an answer.
+        Provide a detailed, accurate response with proper formatting.
+        
+        Context:
+        {context}
+        
+        Question: {question}
+        
+        Helpful Answer:"""
+        
         custom_prompt = PromptTemplate(
             template=prompt_template,
             input_variables=["context", "question"]
         )
-
+        
+        # Configure the QA chain with our custom prompt
         qa_chain.combine_documents_chain.llm_chain.prompt = custom_prompt
-
+        
+        # Execute the query
         result = qa_chain({"query": query}, return_only_outputs=False)
-
+        
+        # Extract answer and sources
         answer = result["result"]
         sources = result.get("source_documents", [])
-
+        
+        # Format the sources for display
         if sources:
-            citations = "\n\n---\n".join([
-                f"Source {i+1}:\n{doc.page_content[:400]}..."
+            source_text = "\n\n---\n".join([
+                f"Source {i+1}:\n{doc.page_content[:500]}{'...' if len(doc.page_content) > 500 else ''}"
                 for i, doc in enumerate(sources)
             ])
         else:
-            citations = "No sources available."
-
-        return answer, citations
-
+            source_text = "No sources cited"
+            
+        return answer, source_text
+    
     except Exception as e:
-        return f"Error: {str(e)}", ""
-
-
-# -------------------------------
-# Summarize PDF
-# -------------------------------
-def summarize_pdf(num_points: int = 6):
+        return f"Error processing your question: {str(e)}", ""
+
+def summarize_pdf(num_points: int = 6) -> str:
+    """
+    Summarizes the uploaded PDF using the Groq LLM with a creative prompt.
+    
+    Args:
+        num_points (int): Number of bullet points for the summary (default: 6).
+    
+    Returns:
+        str: The summary or an error message.
+    """
     global vectorstore, groq_llm
 
     if vectorstore is None:
@@ -160,90 +157,378 @@ def summarize_pdf(num_points: int = 6):
 
     try:
         docs = vectorstore.similarity_search("summary", k=5)
-        context = "\n\n".join([d.page_content for d in docs])
+        if not docs:
+            return "No content found to summarize."
 
-        prompt = f"""
-Summarize the research paper into {num_points} clear bullet points.
+        context = "\n\n".join([doc.page_content for doc in docs])
 
-Content:
-{context}
+        prompt = (
+            "Imagine you are a passionate science communicator tasked with revealing the essence of a groundbreaking research paper.\n"
+            f"Craft a captivating summary in {num_points} vivid bullet points that not only highlights the core discoveries but also paints a clear picture of their significance.\n"
+            "Make it engaging, insightful, and accessible to a curious reader eager to grasp the impact of this work.\n\n"
+            f"Here is the paper content:\n{context}\n\n"
+            "Your inspired summary:"
+        )
 
-Summary:
-"""
+        if groq_llm is None:
+            from groq import Groq
+            groq_llm = GroqWrapper(
+                client=Groq(
+                    api_key=os.getenv("GROQ_API_KEY"),
+                    model="llama-3.3-70b-versatile"
+                ),
+                model_name="llama-3.3-70b-versatile"
+            )
 
-        summary = groq_llm(prompt)  # use __call__
+        summary = groq_llm(prompt)
         return summary.strip()
-
+    
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error during summarization: {str(e)}"
 
-
-# -------------------------------
-# Find Similar Papers via arXiv
-# -------------------------------
+# *** Modified find_similar_papers function ONLY ***
 def find_similar_papers():
-    global vectorstore
-
     if vectorstore is None:
         return "Please upload a PDF first."
 
     try:
-        docs = vectorstore.similarity_search("abstract introduction", k=3)
-        combined = " ".join([d.page_content for d in docs])
+        docs = vectorstore.similarity_search("abstract or introduction", k=3)
 
-        query_text = " ".join(combined.split()[:150])  # more context
-        if len(query_text) < 20:
-            query_text = "transformer neural networks research"
+        # Combine chunks and take the first 40 words total
+        combined_text = " ".join([doc.page_content for doc in docs])
+        query_text = " ".join(combined_text.split()[:40])
 
-        encoded = urllib.parse.quote(query_text)
-        url = f"http://export.arxiv.org/api/query?search_query=all:{encoded}&start=0&max_results=3"
+        # Fallback if query_text is too short or citation-heavy
+        if len(query_text) < 30 or "arXiv" in query_text or "[" in query_text:
+            query_text = "transformer models for abstractive text summarization"
+
+        encoded_query = urllib.parse.quote(query_text)
+
+        # Build arXiv API query
+        url = f"http://export.arxiv.org/api/query?search_query=all:{encoded_query}&start=0&max_results=2"
+
+        print("Querying arXiv with:", query_text)
+        print("URL:", url)
 
         feed = feedparser.parse(url)
+        entries = feed.entries
 
-        if not feed.entries:
-            return "No similar papers found."
+        if not entries:
+            return f"No similar papers found for query: **{query_text}**"
 
         results = []
-        for entry in feed.entries:
-            results.append(
-                f"**{entry.title}**\n{entry.summary}\n🔗 {entry.link}"
-            )
+        for entry in entries:
+            title = entry.title
+            summary = entry.summary.replace('\n', ' ').strip()
+            link = entry.link
+            results.append(f"**{title}**\n{summary}\n🔗 {link}")
 
         return "\n\n".join(results)
 
     except Exception as e:
-        return f"Error: {str(e)}"
-
-
-# -------------------------------
-# Gradio UI
-# -------------------------------
-with gr.Blocks() as demo:
-    gr.Markdown("## 🔬 AI Research Assistant — PDF Q&A, Summaries & Similar Papers")
-
-    with gr.Tab("📄 Upload PDF"):
-        file_upload = gr.File(label="Upload Research PDF", file_types=[".pdf"])
-        btn = gr.Button("Process PDF")
-        status = gr.Textbox(label="Status")
-
-    with gr.Tab("❓ Ask Questions"):
-        question = gr.Textbox(label="Ask a Question")
-        ask_btn = gr.Button("Get Answer")
-        answer = gr.Textbox(label="Answer")
-        cites = gr.Textbox(label="Sources")
-
-    with gr.Tab("📝 Summarize"):
-        num_points_input = gr.Number(label="Number of Bullet Points", value=6, precision=0)
-        sm_btn = gr.Button("Summarize PDF")
-        sm_out = gr.Textbox(label="Summary")
-
-    with gr.Tab("🔍 Similar Papers"):
-        sim_btn = gr.Button("Find Similar Papers")
-        sim_out = gr.Textbox(label="Similar Papers")
+        return f"Error fetching similar papers: {str(e)}"
+
+
+
+
+
+
+
+css = '''
+:root {
+    --primary: #6e48aa;
+    --secondary: #9d50bb;
+    --accent: #4776e6;
+    --dark: #1a1a2e;
+    --darker: #16213e;
+    --light: #f8f9fa;
+    --success: #4caf50;
+    --warning: #ff9800;
+    --danger: #f44336;
+}
+
+body, .gradio-container {
+    margin: 0;
+    padding: 0;
+    font-family: 'Segoe UI', 'Roboto', sans-serif;
+    background: linear-gradient(135deg, var(--dark), var(--darker));
+    color: var(--light);
+    min-height: 100vh;
+}
+
+.header {
+    text-align: center;
+    padding: 1.5rem 0;
+    margin-bottom: 2rem;
+    color: white;                      /* Make text white */
+    font-size: 3rem;
+    font-weight: 800;
+    letter-spacing: 1px;
+    font-style: italic;               /* Make it italic */
+    text-shadow: 0 2px 10px rgba(0,0,0,0.2);
+}
+
+
+.nav-tabs {
+    display: flex;
+    justify-content: center;
+    margin-bottom: 2rem;
+    gap: 1rem;
+}
+
+.tab-button {
+    background: rgba(255,255,255,0.1);
+    border: none;
+    padding: 0.8rem 1.5rem;
+    border-radius: 50px;
+    color: white;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+}
+
+.tab-button:hover {
+    background: rgba(255,255,255,0.2);
+    transform: translateY(-2px);
+}
+
+.tab-button.active {
+    background: linear-gradient(45deg, var(--primary), var(--accent));
+    box-shadow: 0 4px 15px rgba(110, 72, 170, 0.4);
+}
+
+.tab-content {
+    display: none;
+    animation: fadeIn 0.5s ease-out;
+}
+
+.tab-content.active {
+    display: block;
+}
+
+.panel {
+    background: rgba(255,255,255,0.05);
+    border-radius: 16px;
+    padding: 2rem;
+    margin: 1rem auto;
+    max-width: 900px;
+    backdrop-filter: blur(10px);
+    border: 1px solid rgba(255,255,255,0.1);
+    box-shadow: 0 8px 32px rgba(0,0,0,0.2);
+}
+
+.panel-header {
+    font-size: 1.5rem;
+    font-weight: 700;
+    margin-bottom: 1.5rem;
+    color: white;
+    display: flex;
+    align-items: center;
+    gap: 0.8rem;
+}
+
+.panel-header svg {
+    width: 1.5rem;
+    height: 1.5rem;
+}
+
+button {
+    background: linear-gradient(45deg, var(--primary), var(--secondary));
+    color: white;
+    border: none;
+    padding: 0.8rem 1.5rem;
+    border-radius: 50px;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    box-shadow: 0 4px 15px rgba(110, 72, 170, 0.3);
+    margin: 0.5rem 0;
+}
+
+button:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 6px 20px rgba(110, 72, 170, 0.4);
+}
+
+button:active {
+    transform: translateY(0);
+}
+
+button.secondary {
+    background: rgba(255,255,255,0.1);
+}
+
+button.secondary:hover {
+    background: rgba(255,255,255,0.2);
+}
+
+textarea, input[type="text"] {
+    background: rgba(255,255,255,0.1);
+    border: 1px solid rgba(255,255,255,0.2);
+    color: white;
+    border-radius: 8px;
+    padding: 0.8rem;
+    width: 100%;
+    margin-bottom: 1rem;
+}
+
+textarea:focus, input[type="text"]:focus {
+    outline: none;
+    border-color: var(--accent);
+    box-shadow: 0 0 0 2px rgba(71, 118, 230, 0.3);
+}
+
+.output-box {
+    background: rgba(0,0,0,0.3);
+    border-radius: 8px;
+    padding: 1rem;
+    margin-top: 1rem;
+    border-left: 4px solid var(--accent);
+}
+
+.output-label {
+    font-weight: 600;
+    margin-bottom: 0.5rem;
+    display: block;
+    color: #ddd;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.slide-in {
+    animation: slideIn 0.5s ease-out forwards;
+}
+
+@keyframes slideIn {
+    from { transform: translateX(100%); opacity: 0; }
+    to { transform: translateX(0); opacity: 1; }
+}
+
+.file-upload {
+    border: 2px dashed rgba(255,255,255,0.3);
+    border-radius: 8px;
+    padding: 2rem;
+    text-align: center;
+    margin-bottom: 1rem;
+    transition: all 0.3s ease;
+}
+
+.file-upload:hover {
+    border-color: var(--accent);
+    background: rgba(71, 118, 230, 0.1);
+}
+
+.progress-bar {
+    height: 6px;
+    background: rgba(255,255,255,0.1);
+    border-radius: 3px;
+    margin-top: 1rem;
+    overflow: hidden;
+}
+
+.progress {
+    height: 100%;
+    background: linear-gradient(90deg, var(--primary), var(--accent));
+    width: 0%;
+    transition: width 0.3s ease;
+}
+'''
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("""
+    <div class='header'>
+        <span style="font-size:1.2em">🔬</span> AI Research Companion 
+        <span style="font-size:1.2em">🧠</span>
+    </div>
+    """)
+    
+    with gr.Tabs() as tabs:
+        with gr.TabItem("📄 Upload PDF", id="upload"):
+            with gr.Column(elem_classes=["panel"]):
+                gr.Markdown("""<div class="panel-header">
+                    <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M7 16a4 4 0 01-.88-7.903A5 5 0 1115.9 6L16 6a5 5 0 011 9.9M15 13l-3-3m0 0l-3 3m3-3v12" />
+                    </svg>
+                    Document Processing
+                </div>""")
+                
+                with gr.Column(elem_classes=["file-upload"]):
+                    file_upload = gr.File(
+                        file_types=['.pdf'], 
+                        label="Drag & Drop PDF or Click to Browse",
+                        elem_classes=["upload-box"]
+                    )
+                    upload_btn = gr.Button("Process Document", variant="primary")
+                    status = gr.Textbox(label="Processing Status", interactive=False)
+                    gr.Markdown("<div class='progress-bar'><div class='progress'></div></div>")
+
+        with gr.TabItem("❓ Ask Questions", id="qa"):
+            with gr.Column(elem_classes=["panel"]):
+                gr.Markdown("""<div class="panel-header">
+                    <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8.228 9c.549-1.165 2.03-2 3.772-2 2.21 0 4 1.343 4 3 0 1.4-1.278 2.575-3.006 2.907-.542.104-.994.54-.994 1.093m0 3h.01M21 12a9 9 0 11-18 0 9 9 0 0118 0z" />
+                    </svg>
+                    Research Q&A
+                </div>""")
+                
+                question = gr.Textbox(
+                    placeholder="Type your research question here...", 
+                    label="Your Question",
+                    lines=3
+                )
+                ask_btn = gr.Button("Get Answer", variant="primary")
+                
+                with gr.Column(elem_classes=["output-box"]):
+                    gr.Markdown("<div class='output-label'>Answer</div>")
+                    answer = gr.Textbox(show_label=False, lines=6, interactive=False)
+                
+                with gr.Column(elem_classes=["output-box"]):
+                    gr.Markdown("<div class='output-label'>Source References</div>")
+                    citations = gr.Textbox(show_label=False, lines=4, interactive=False)
+
+        with gr.TabItem("✍️ Summarize", id="summary"):
+            with gr.Column(elem_classes=["panel"]):
+                gr.Markdown("""<div class="panel-header">
+                    <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 6h16M4 12h16m-7 6h7" />
+                    </svg>
+                    Document Summary
+                </div>""")
+                
+                summary_btn = gr.Button("Generate Summary", variant="primary")
+                
+                with gr.Column(elem_classes=["output-box"]):
+                    gr.Markdown("<div class='output-label'>Key Insights</div>")
+                    summary_output = gr.Textbox(show_label=False, lines=8, interactive=False)
+
+        with gr.TabItem("🔍 Similar Papers", id="papers"):
+            with gr.Column(elem_classes=["panel"]):
+                gr.Markdown("""<div class="panel-header">
+                    <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M19 11H5m14 0a2 2 0 012 2v6a2 2 0 01-2 2H5a2 2 0 01-2-2v-6a2 2 0 012-2m14 0V9a2 2 0 00-2-2M5 11V9a2 2 0 012-2m0 0V5a2 2 0 012-2h6a2 2 0 012 2v2M7 7h10" />
+                    </svg>
+                    Related Research
+                </div>""")
+                
+                similar_btn = gr.Button("Find Similar Papers", variant="primary")
+                
+                with gr.Column(elem_classes=["output-box"]):
+                    gr.Markdown("<div class='output-label'>Recommended Papers</div>")
+                    similar_output = gr.Textbox(show_label=False, lines=8, interactive=False)
+
+    # Event handlers
+    upload_btn.click(upload_pdf, inputs=file_upload, outputs=status)
+    ask_btn.click(ask_question, inputs=question, outputs=[answer, citations])
+    summary_btn.click(summarize_pdf, outputs=summary_output)
+    similar_btn.click(find_similar_papers, outputs=similar_output)
+
+if __name__ == "__main__":
+    demo.launch()
+
+           
 
-    btn.click(upload_pdf, inputs=file_upload, outputs=status)
-    ask_btn.click(ask_question, inputs=question, outputs=[answer, cites])
-    sm_btn.click(summarize_pdf, inputs=num_points_input, outputs=sm_out)
-    sim_btn.click(find_similar_papers, outputs=sim_out)
 
-demo.launch(share=True)