Update app.py

app.py

@@ -1,36 +1,55 @@
 import os
 import requests
 import gradio as gr
+import pandas as pd
+import markdown
+import tempfile
+from fpdf import FPDF
+from io import BytesIO
+import nglview as nv
+import ipywidgets
 
-# Retrieve the API key from the environment variable
+# API setup
 groq_api_key = os.getenv("GROQ_API_KEY")
-
 if not groq_api_key:
-    raise ValueError("GROQ_API_KEY is missing! Set it in the Hugging Face Spaces 'Secrets'.")
+    raise ValueError("GROQ_API_KEY is missing!")
 
-# Define the API endpoint and headers
 url = "https://api.groq.com/openai/v1/chat/completions"
 headers = {"Authorization": f"Bearer {groq_api_key}"}
 
-# Function to interact with Groq API
+# Globals to hold table for export
+comparison_table = None
+
+# Function to extract markdown table and convert to dataframe
+def extract_table(md):
+    global comparison_table
+    lines = [line.strip() for line in md.splitlines() if "|" in line and "---" not in line]
+    headers = [x.strip() for x in lines[0].split("|")[1:-1]]
+    data = []
+    for row in lines[1:]:
+        values = [x.strip() for x in row.split("|")[1:-1]]
+        data.append(values)
+    df = pd.DataFrame(data, columns=headers)
+    comparison_table = df
+    return df
+
+# Main chatbot function
 def chat_with_groq(user_input):
-    # Check if question is related to materials science
-    keywords = [
-        "material", "materials", "alloy", "composite", "polymer", "ceramic",
-        "application", "mechanical properties", "thermal properties", "corrosion",
-        "creep", "fatigue", "strength", "tensile", "impact", "fracture", "modulus"
-    ]
-    
+    keywords = ["material", "materials", "alloy", "composite", "polymer", "ceramic",
+                "application", "mechanical", "thermal", "corrosion", "creep", "fatigue",
+                "strength", "tensile", "impact", "fracture", "modulus", "AI", "ML", "machine learning"]
+
     if not any(word in user_input.lower() for word in keywords):
-        return "⚠️ I am an expert in Materials Science, ask me anything about it and I will try my best to answer. Anything outside, feel free to use ChatGPT! 🙂"
+        return "⚠️ I am an expert in Materials Science. Ask me anything about it and I’ll try my best. For other topics, try ChatGPT! 🙂"
 
     system_prompt = (
-        "You are an expert materials scientist. When a user asks about the best materials for a specific application, "
-        "provide the top 3 material choices. First, list the key properties required for that application. Then show a clean, "
-        "side-by-side comparison in markdown table format of the three materials, with the properties as rows and materials as columns. "
-        "Include their relevant mechanical, thermal, and chemical properties. Conclude with a brief summary of which might be best depending on the scenario."
+        "You are a materials science expert. When a user asks about materials for an application, provide:\n"
+        "1. Required properties.\n"
+        "2. A markdown table comparing the top 3 materials (rows: properties, columns: materials).\n"
+        "3. A short summary of use cases.\n"
+        "Only reply with markdown content."
     )
-    
+
     body = {
         "model": "llama-3.1-8b-instant",
         "messages": [
@@ -38,16 +57,44 @@ def chat_with_groq(user_input):
             {"role": "user", "content": user_input}
         ]
     }
-    
+
     response = requests.post(url, headers=headers, json=body)
-    
+
     if response.status_code == 200:
-        return response.json()['choices'][0]['message']['content']
+        content = response.json()['choices'][0]['message']['content']
+        extract_table(content)
+        return content
     else:
         return f"Error: {response.json()}"
 
-# Build Gradio interface with better layout and custom styling
-with gr.Blocks(title="Materials Science Expert Chatbot", css="""
+# File export functions
+def download_csv():
+    if comparison_table is not None:
+        return comparison_table.to_csv(index=False).encode('utf-8')
+    return None
+
+def download_pdf():
+    if comparison_table is None:
+        return None
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=10)
+    col_width = pdf.w / (len(comparison_table.columns) + 1)
+    row_height = 8
+    for col in comparison_table.columns:
+        pdf.cell(col_width, row_height, col, border=1)
+    pdf.ln()
+    for i in range(len(comparison_table)):
+        for item in comparison_table.iloc[i]:
+            pdf.cell(col_width, row_height, str(item), border=1)
+        pdf.ln()
+    output = BytesIO()
+    pdf.output(output)
+    output.seek(0)
+    return output.read()
+
+# Build UI
+with gr.Blocks(title="Materials Science Chatbot", css="""
     #orange-btn {
         background-color: #f97316 !important;
         color: white !important;
@@ -55,32 +102,56 @@ with gr.Blocks(title="Materials Science Expert Chatbot", css="""
         font-weight: bold;
     }
 """) as demo:
-    
-    gr.Markdown("## 🧪 Materials Science Expert\nAsk about the best materials for any engineering or industrial application.")
+    gr.Markdown("## 🧪 Materials Science Expert\nAsk about materials for any application or property requirements.")
 
     with gr.Row():
         with gr.Column(scale=3):
             user_input = gr.Textbox(
+                label="Ask your question",
+                placeholder="e.g. Best materials for heat shields...",
                 lines=2,
-                placeholder="e.g. Best materials for high-temperature turbine blades...",
-                label="Ask your question"
+                elem_id="question_box"
             )
+            gr.Markdown("💡 *Hit Enter to submit your query*")
         with gr.Column(scale=1, min_width=100):
-            submit_btn = gr.Button("Submit", variant="primary", elem_id="orange-btn")
+            submit_btn = gr.Button("Submit", elem_id="orange-btn")
 
+    # Popular questions section
     gr.Markdown("#### 📌 Popular Materials Science related questions")
-    gr.Markdown("""
-- What are the best corrosion-resistant materials for marine environments (e.g., desalination)?
-- Which materials are ideal for solar panel coatings and desert heat management?
-- What materials are used for aerospace structures in extreme climates?
-- Best high-strength materials for construction in the Gulf region?
-- What advanced materials are used in electric vehicles and batteries in the UAE?
-    """)
+    popular_questions = [
+        "What are the best corrosion-resistant materials for marine environments (e.g., desalination)?",
+        "Which materials are ideal for solar panel coatings and desert heat management?",
+        "What materials are used for aerospace structures in extreme climates?",
+        "Best high-strength materials for construction in the Gulf region?",
+        "What advanced materials are used in electric vehicles and batteries in the UAE?",
+        "How can one leverage AI/ML techniques in Materials Science?",
+        "I’m a recent high school graduate interested in science. How can I explore Materials Science with AI/ML?"
+    ]
+
+    def autofill(question):
+        return gr.Textbox.update(value=question)
+
+    with gr.Row():
+        for q in popular_questions:
+            gr.Button(q, size="sm").click(autofill, inputs=[], outputs=user_input)
+
+    # Output
+    output_md = gr.Markdown()
+
+    with gr.Row():
+        with gr.Column():
+            csv_btn = gr.File(label="Download CSV", visible=False)
+            pdf_btn = gr.File(label="Download PDF", visible=False)
 
-    output = gr.Markdown()
+    def submit_and_prepare(user_input):
+        response = chat_with_groq(user_input)
+        csv_data = download_csv()
+        pdf_data = download_pdf()
+        return response, gr.File.update(value=("materials.csv", csv_data), visible=True), gr.File.update(value=("materials.pdf", pdf_data), visible=True)
 
-    submit_btn.click(chat_with_groq, inputs=user_input, outputs=output)
+    submit_btn.click(submit_and_prepare, inputs=user_input, outputs=[output_md, csv_btn, pdf_btn])
+    user_input.submit(submit_and_prepare, inputs=user_input, outputs=[output_md, csv_btn, pdf_btn])
 
-# Launch the app
+# Launch
 if __name__ == "__main__":
     demo.launch()