Update app.py

app.py

@@ -1,54 +1,36 @@
 import os
 import requests
 import gradio as gr
-import pandas as pd
-import tempfile
-from fpdf import FPDF
-from io import BytesIO
-import nglview as nv
-import ipywidgets
 
-# API setup
+# Retrieve the API key from the environment variable
 groq_api_key = os.getenv("GROQ_API_KEY")
+
 if not groq_api_key:
-    raise ValueError("GROQ_API_KEY is missing!")
+    raise ValueError("GROQ_API_KEY is missing! Set it in the Hugging Face Spaces 'Secrets'.")
 
+# Define the API endpoint and headers
 url = "https://api.groq.com/openai/v1/chat/completions"
 headers = {"Authorization": f"Bearer {groq_api_key}"}
 
-# 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
+# Function to interact with Groq API
 def chat_with_groq(user_input):
-    keywords = ["material", "materials", "alloy", "composite", "polymer", "ceramic",
-                "application", "mechanical", "thermal", "corrosion", "creep", "fatigue",
-                "strength", "tensile", "impact", "fracture", "modulus", "AI", "ML", "machine learning"]
-
+    # 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"
+    ]
+    
     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’ll try my best. For other topics, try ChatGPT! 🙂"
+        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! 🙂"
 
     system_prompt = (
-        "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."
+        "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."
     )
-
+    
     body = {
         "model": "llama-3.1-8b-instant",
         "messages": [
@@ -56,44 +38,16 @@ def chat_with_groq(user_input):
             {"role": "user", "content": user_input}
         ]
     }
-
+    
     response = requests.post(url, headers=headers, json=body)
-
+    
     if response.status_code == 200:
-        content = response.json()['choices'][0]['message']['content']
-        extract_table(content)
-        return content
+        return response.json()['choices'][0]['message']['content']
     else:
         return f"Error: {response.json()}"
 
-# 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="""
+# Build Gradio interface with better layout and custom styling
+with gr.Blocks(title="Materials Science Expert Chatbot", css="""
     #orange-btn {
         background-color: #f97316 !important;
         color: white !important;
@@ -101,56 +55,32 @@ with gr.Blocks(title="Materials Science Chatbot", css="""
         font-weight: bold;
     }
 """) as demo:
-    gr.Markdown("## 🧪 Materials Science Expert\nAsk about materials for any application or property requirements.")
+    
+    gr.Markdown("## 🧪 Materials Science Expert\nAsk about the best materials for any engineering or industrial application.")
 
     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,
-                elem_id="question_box"
+                placeholder="e.g. Best materials for high-temperature turbine blades...",
+                label="Ask your question"
             )
-            gr.Markdown("💡 *Hit Enter to submit your query*")
         with gr.Column(scale=1, min_width=100):
-            submit_btn = gr.Button("Submit", elem_id="orange-btn")
+            submit_btn = gr.Button("Submit", variant="primary", elem_id="orange-btn")
 
-    # Popular questions section
     gr.Markdown("#### 📌 Popular Materials Science related questions")
-    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)
+    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?
+    """)
 
-    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)
+    output = gr.Markdown()
 
-    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])
+    submit_btn.click(chat_with_groq, inputs=user_input, outputs=output)
 
-# Launch
+# Launch the app
 if __name__ == "__main__":
     demo.launch()