Update app.py

app.py

@@ -1,13 +1,20 @@
+import os
 import gradio as gr
 import google.generativeai as genai
 import spacy
 import yake
+import subprocess
 
-# Initialize Google Gemini AI
-genai.configure(api_key="AIzaSyDnx_qUjGTFG1pv1otPUhNt_bGGv14aMDI")
+# Ensure spaCy model is downloaded dynamically
+MODEL_NAME = "en_core_web_sm"
+try:
+    nlp = spacy.load(MODEL_NAME)
+except OSError:
+    subprocess.run(["python", "-m", "spacy", "download", MODEL_NAME])
+    nlp = spacy.load(MODEL_NAME)
 
-# Load NLP Model
-nlp = spacy.load("en_core_web_sm")
+# Configure Google Gemini AI
+genai.configure(api_key=os.getenv("AIzaSyDnx_qUjGTFG1pv1otPUhNt_bGGv14aMDI"))  # Use environment variable for security
 
 def analyze_text(text):
     """Perform AI-driven text analysis."""
@@ -15,12 +22,15 @@ def analyze_text(text):
         return "Please enter some text."
 
     # Summarization using Gemini AI
-    prompt = f"Summarize this text:\n{text}"
-    response = genai.generate_text(prompt)
-    summary = response.text.strip() if response.text else "Error in summarization."
+    try:
+        prompt = f"Summarize this text:\n{text}"
+        response = genai.generate_text(prompt)
+        summary = response.text.strip() if response.text else "Error in summarization."
+    except Exception as e:
+        summary = f"Summarization failed: {str(e)}"
 
-    # Sentiment Analysis
-    sentiment = "Positive" if "good" in text.lower() else "Negative"  # Basic example
+    # Basic Sentiment Analysis (Improve with ML if needed)
+    sentiment = "Positive" if "good" in text.lower() else "Negative"
 
     # Keyword Extraction
     kw_extractor = yake.KeywordExtractor()
@@ -32,9 +42,9 @@ def analyze_text(text):
 
     # AI-Generated Report
     report = f"""
-    **Summary:** {summary}
-    **Sentiment:** {sentiment}
-    **Keywords:** {', '.join(keywords)}
+    **Summary:** {summary}  
+    **Sentiment:** {sentiment}  
+    **Keywords:** {', '.join(keywords)}  
     **Entities:** {entities if entities else 'None'}
     """
 
@@ -43,15 +53,16 @@ def analyze_text(text):
 # Gradio Interface
 with gr.Blocks() as demo:
     gr.Markdown("# AI-Powered Text & File Analyzer 🚀")
-    input_text = gr.Textbox(label="Enter Text or Upload .txt File")
+    
+    input_text = gr.Textbox(label="Enter Text")
     file_input = gr.File(label="Upload .txt File", file_types=[".txt"])
     analyze_button = gr.Button("Analyze")
     output = gr.Markdown()
-    
+
     def process_input(text, file):
         """Process text from input or file."""
         if file:
-            with open(file.name, "r") as f:
+            with open(file.name, "r", encoding="utf-8") as f:
                 text = f.read()
         return analyze_text(text)