Update app.py

app.py

@@ -1,128 +1,60 @@
 import gradio as gr
-import requests
-import os
-from fpdf import FPDF
-import uuid
-import re
-import matplotlib.pyplot as plt
+import arxiv
+from transformers import pipeline
 
-# Load your Nebius API key and Folder ID from environment variables
-NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY") or "YOUR_NEBIUS_API_KEY"
-FOLDER_ID = os.getenv("FOLDER_ID") or "YOUR_FOLDER_ID"
+# Load summarization model
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 
-def generate_meal(preferences, ingredients, time):
-    prompt = f"""
-You're a smart kitchen agent.
-
-Given:
-- Dietary preferences: {preferences}
-- Ingredients available: {ingredients}
-- Time available: {time} minutes
-
-Tasks:
-1. Suggest one meal idea
-2. Provide step-by-step recipe instructions
-3. List missing ingredients (Shopping List)
-4. Estimate nutrition (calories, protein, carbs, fat)
-
-Output in this format:
-Meal Name: ...
-Steps:
-1. ...
-2. ...
-Shopping List:
-- ...
-Nutrition:
-- Calories: ... kcal
-- Protein: ... g
-- Carbs: ... g
-- Fat: ... g
-"""
-    headers = {
-        "Authorization": f"Api-Key {NEBIUS_API_KEY}",
-        "Content-Type": "application/json"
-    }
-    data = {
-        "modelUri": f"gpt://{FOLDER_ID}/yandexgpt-lite",
-        "completionOptions": {"stream": False, "temperature": 0.7, "maxTokens": 700},
-        "messages": [{"role": "user", "text": prompt.strip()}]
-    }
-    response = requests.post("https://llm.api.cloud.yandex.net/foundationModels/v1/completion",
-                             headers=headers, json=data)
+# Search and summarize papers
+def search_and_summarize(topic, sort_by_option):
     try:
-        text = response.json()["result"]["alternatives"][0]["message"]["text"]
-    except Exception as e:
-        return f"❌ Error: Could not fetch recipe. {e}"
-    return text
+        num_papers = 3  # fixed value
+        sort_mapping = {
+            "Relevance": arxiv.SortCriterion.Relevance,
+            "Most Recent": arxiv.SortCriterion.SubmittedDate
+        }
+
+        search = arxiv.Search(
+            query=topic,
+            max_results=num_papers,
+            sort_by=sort_mapping.get(sort_by_option, arxiv.SortCriterion.Relevance)
+        )
+
+        results = []
+        for result in search.results():
+            summary = summarizer(result.summary[:1000], max_length=120, min_length=30, do_sample=False)[0]['summary_text']
+            authors = ", ".join([author.name for author in result.authors])
+            published_date = result.published.date().strftime("%Y-%m-%d")
+            result_block = (
+                f"📘 *{result.title}*\n\n"
+                f"👩‍🔬 Authors: {authors}\n"
+                f"📅 Published: {published_date}\n\n"
+                f"📝 Summary: {summary}\n\n"
+                f"🔗 [Read More]({result.pdf_url})"
+            )
+            results.append(result_block)
+
+        return "\n\n---\n\n".join(results) if results else "No results found."
 
-def extract_nutrition(recipe_text):
-    pattern = r"Calories:\s*([\d\.]+)\s*kcal.*Protein:\s*([\d\.]+)g.*Carbs:\s*([\d\.]+)g.*Fat:\s*([\d\.]+)g"
-    match = re.search(pattern, recipe_text.replace("\n", " "))
-    if match:
-        calories, protein, carbs, fat = map(float, match.groups())
-        return {"Calories": calories, "Protein": protein, "Carbs": carbs, "Fat": fat}
-    return None
-
-def plot_nutrition_chart(nutrition):
-    fig, ax = plt.subplots()
-    nutrients = list(nutrition.keys())
-    values = list(nutrition.values())
-    colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
-    ax.pie(values, labels=nutrients, autopct='%1.1f%%', startangle=140, colors=colors)
-    ax.axis('equal')
-    plt.tight_layout()
-    filename = f"/tmp/nutrition_{uuid.uuid4().hex}.png"
-    plt.savefig(filename)
-    plt.close(fig)
-    return filename
-
-def handle_generate(preferences, ingredients, time):
-    recipe = generate_meal(preferences, ingredients, time)
-    nutrition = extract_nutrition(recipe)
-    chart_path = None
-    if nutrition:
-        chart_path = plot_nutrition_chart(nutrition)
-    return recipe, chart_path
-
-def save_pdf(recipe_text):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    for line in recipe_text.split('\n'):
-        pdf.multi_cell(0, 10, line)
-    filename = f"/tmp/AgentChef_Recipe_{uuid.uuid4().hex}.pdf"
-    pdf.output(filename)
-    return filename
+    except Exception as e:
+        return f"⚠️ An error occurred: {e}"
 
-with gr.Blocks(css="""
-    body { background-color: #111; color: #eee; font-family: 'Segoe UI', sans-serif; }
-    .gradio-container { max-width: 900px; margin: auto; }
-    .input-label { font-weight: 600; margin-bottom: 5px; }
-    .footer { font-size: 0.8rem; color: #666; padding: 10px; text-align: center; }
-    @media (max-width: 600px) {
-        .gradio-container { padding: 10px; }
-    }
-""") as demo:
-    gr.Markdown("# 👨‍🍳 AgentChef: AI Recipe Planner & Smart Kitchen Assistant")
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown("# 🤖 AI Research Assistant\nSummarize academic research papers using Hugging Face models + Arxiv!")
 
     with gr.Row():
-        with gr.Column(scale=1, min_width=200):
-            preferences = gr.Textbox(label="🥗 Dietary Preferences", placeholder="e.g. vegetarian, keto")
-            ingredients = gr.Textbox(label="🧂 Ingredients You Have", placeholder="e.g. rice, tomato, onion", lines=3)
-            mic = gr.Microphone(label="🎤 Speak Ingredients")
-            mic.stream(lambda audio: audio if audio else "", inputs=None, outputs=ingredients)
-            time = gr.Slider(5, 60, value=20, step=5, label="⏱️ Time Available (minutes)")
-            generate_btn = gr.Button("🍽️ Generate Recipe")
+        topic = gr.Textbox(label="🔍 Enter your research topic", placeholder="e.g. diffusion models in AI")
+        sort_by = gr.Dropdown(choices=["Relevance", "Most Recent"], value="Relevance", label="Sort by")
 
-        with gr.Column(scale=1, min_width=300):
-            recipe_output = gr.Textbox(label="📝 Recipe Output", lines=15)
-            nutrition_chart = gr.Image(label="📊 Nutrition Breakdown", interactive=False)
-            pdf_btn = gr.Button("📄 Export as PDF")
+    search_btn = gr.Button("Search 🔎")
+    output = gr.Markdown()
 
-    generate_btn.click(handle_generate,
-                       inputs=[preferences, ingredients, time],
-                       outputs=[recipe_output, nutrition_chart])
+    # Show loading message
+    def show_loading():
+        return "⏳ Loading, please wait..."
 
-    pdf_btn.click(save_pdf, inputs=[recipe_output], outputs=gr.File(label="📥 Download Recipe PDF"))
+    search_btn.click(fn=show_loading, inputs=[], outputs=output, queue=False)
+    search_btn.click(fn=search_and_summarize, inputs=[topic, sort_by], outputs=output)
 
-demo.launch(share = True)
+demo.launch()