Create app.py

app.py

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+import gradio as gr
+import google.generativeai as genai
+import spacy
+import yake
+
+# Initialize Google Gemini AI
+genai.configure(api_key="AIzaSyDnx_qUjGTFG1pv1otPUhNt_bGGv14aMDI")
+
+# Load NLP Model
+nlp = spacy.load("en_core_web_sm")
+
+def analyze_text(text):
+    """Perform AI-driven text analysis."""
+    if not 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."
+
+    # Sentiment Analysis
+    sentiment = "Positive" if "good" in text.lower() else "Negative"  # Basic example
+
+    # Keyword Extraction
+    kw_extractor = yake.KeywordExtractor()
+    keywords = [kw[0] for kw in kw_extractor.extract_keywords(text)[:5]]
+
+    # Named Entity Recognition (NER)
+    doc = nlp(text)
+    entities = {ent.text: ent.label_ for ent in doc.ents}
+
+    # AI-Generated Report
+    report = f"""
+    **Summary:** {summary}
+    **Sentiment:** {sentiment}
+    **Keywords:** {', '.join(keywords)}
+    **Entities:** {entities if entities else 'None'}
+    """
+
+    return report
+
+# 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")
+    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:
+                text = f.read()
+        return analyze_text(text)
+
+    analyze_button.click(process_input, inputs=[input_text, file_input], outputs=output)
+
+demo.launch()