Create app.py

app.py

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+import os
+import gradio as gr
+import google.generativeai as genai
+import chardet  # Auto-detect file encoding
+
+# ✅ Load API Key securely from Hugging Face Secrets
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+if not GOOGLE_API_KEY:
+    raise ValueError("⚠ Error: Google API Key is missing. Set it in Hugging Face Secrets.")
+
+# ✅ Configure Google Generative AI (Gemini)
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# ✅ Load the Gemini Model
+model = genai.GenerativeModel(model_name="models/gemini-2.0-flash")
+
+# 🔹 Function to read a file with auto-detected encoding
+def read_file_with_encoding(file_path):
+    try:
+        with open(file_path, "rb") as f:
+            raw_data = f.read()
+
+        # Detect file encoding
+        encoding = chardet.detect(raw_data)["encoding"]
+        if encoding is None:
+            encoding = "utf-8"  # Default to UTF-8 if detection fails
+
+        # Read file with detected encoding
+        with open(file_path, "r", encoding=encoding, errors="replace") as f:
+            return f.read()
+    except Exception as e:
+        return f"⚠ Error reading file: {str(e)}"
+
+# 🔹 Function to analyze text or file content
+def analyze_input(text, file):
+    try:
+        if file is not None:
+            text = read_file_with_encoding(file)  # ✅ Auto-detect encoding
+        elif not text.strip():
+            return "⚠ Error: Please enter text or upload a file.", ""
+
+        text = text[:2000]  # Limit input text size
+        prompt = f"Analyze and summarize this document:\n\n{text}"
+        response = model.generate_content([prompt], stream=True)  # ✅ Fix applied
+
+        # Collect streamed response
+        result = "".join([chunk.text for chunk in response])
+        word_count = len(text.split())
+
+        return result, f"📊 Word Count: {word_count}"
+    except Exception as e:
+        return f"⚠ Error: {str(e)}", ""
+
+# 🔹 Function to clear inputs and outputs
+def clear_inputs():
+    return "", None, "", "", None  
+
+# 🔹 Function to generate a downloadable text file
+def generate_downloadable_file(text):
+    if text.strip():
+        file_path = "analysis_result.txt"
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(text)
+        return file_path
+    else:
+        return None  
+
+# ✅ Create Gradio UI
+with gr.Blocks(theme=gr.themes.Default()) as demo:
+    gr.Markdown("""
+    # 📄 *AI-Powered Text & File Analyzer*
+    🚀 Upload a .txt file or enter text manually to get an AI-generated analysis and summary.
+    """)
+
+    with gr.Row():
+        text_input = gr.Textbox(label="✍ Enter Text", placeholder="Type or paste your text here...", lines=6)
+        file_input = gr.File(label="📂 Upload Text File (.txt)", type="filepath")  
+
+    output_text = gr.Textbox(label="📝 Analysis Result", lines=10, interactive=False)
+    word_count_display = gr.Textbox(label="📊 Word Count", interactive=False)
+
+    with gr.Row():
+        analyze_button = gr.Button("🔍 Analyze", variant="primary")
+        clear_button = gr.Button("🗑 Clear", variant="secondary")
+
+    with gr.Column():
+        gr.Markdown("### 📥 Download Analysis Result")
+        with gr.Row():
+            download_button = gr.Button("⬇ Download Result", variant="success", size="sm")
+            download_file = gr.File(label="📄 Click to Download", interactive=False)
+
+    # ✅ Button functionalities
+    analyze_button.click(analyze_input, inputs=[text_input, file_input], outputs=[output_text, word_count_display])
+    clear_button.click(clear_inputs, inputs=[], outputs=[text_input, file_input, output_text, word_count_display, download_file])
+    download_button.click(generate_downloadable_file, inputs=output_text, outputs=download_file)
+
+# ✅ Launch the Gradio app
+demo.launch()