Create app.py

app.py

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+import os
+import gradio as gr
+import wikipediaapi
+from groq import Groq
+import torch
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+from deep_translator import GoogleTranslator
+import yake
+from datetime import datetime
+
+# ✅ Set API Key
+# os.environ["GROQ_API_KEY"] = "your_api_key_here"
+# client = Groq(api_key=os.environ["GROQ_API_KEY"])
+
+# ✅ Set API Key
+os.environ["GROQ_API_KEY"] = "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"    # Replace with your API key
+client = Groq(api_key=os.environ["GROQ_API_KEY"])
+
+# ✅ Load M2M-100 Model
+model_name = "facebook/m2m100_418M"
+tokenizer = M2M100Tokenizer.from_pretrained(model_name)
+model = M2M100ForConditionalGeneration.from_pretrained(model_name)
+
+def fetch_wikipedia_summary(topic):
+    wiki_wiki = wikipediaapi.Wikipedia(
+        user_agent="EducationalScriptApp/1.0",
+        language="en"
+    )
+    page = wiki_wiki.page(topic)
+    return page.summary if page.exists() else "No Wikipedia summary available."
+
+def generate_script(topic, duration):
+    try:
+        factual_content = fetch_wikipedia_summary(topic)
+        words_per_minute = 130
+        target_words = duration * words_per_minute
+
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": f"Format the following factual content into a well-structured educational script in English with approximately {target_words} words: \n{factual_content}"}],
+            model="llama-3.3-70b-versatile"
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"❌ Error in script generation: {str(e)}"
+
+# # ✅ Function to Extract Keywords
+# def extract_keywords(script):
+#     words = word_tokenize(script.lower())
+#     stop_words = set(stopwords.words('english'))
+#     filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
+#     word_freq = Counter(filtered_words)
+#     keywords = [word for word, freq in word_freq.most_common(10)]
+#     return ", ".join(keywords)
+
+
+# ✅ Function to Extract Keywords Using YAKE
+def extract_keywords(script):
+    try:
+        kw_extractor = yake.KeywordExtractor(
+            lan="en",  # Language
+            n=3,  # Max number of words in a keyword phrase (trigrams)
+            dedupLim=0.9,  # Reduce redundant phrases
+            # top=10  # Extract top 10 keywords
+        )
+
+        keywords = kw_extractor.extract_keywords(script)
+        return ", ".join([kw[0] for kw in keywords])  # ✅ Extract only the keyword text
+    except Exception as e:
+        return f"❌ Error extracting keywords: {str(e)}"
+
+
+# # ✅ Function to Save Edited or Generated Keywords
+# def save_keywords_file(keywords):
+#     filename = "keywords.txt"
+#     with open(filename, "w", encoding="utf-8") as f:
+#         f.write(keywords)
+#     return filename
+
+# def save_keywords_file(keywords):
+#     if not keywords.strip():
+#         return "❌ No keywords available to save."
+    
+#     filename = "keywords.txt"
+#     with open(filename, "w", encoding="utf-8") as f:
+#         f.write(keywords)
+#     return filename
+
+def save_keywords_file(keywords, topic):
+    today = datetime.today().strftime('%Y_%b_%d')
+    filename = f"Keywords/{topic}_Keyword_{today}.txt"
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(keywords)
+    return filename
+
+
+# # ✅ Function to Translate English Script to Urdu
+# def translate_to_urdu(english_script):
+#     try:
+#         google_translation = GoogleTranslator(source='en', target='ur').translate(english_script)
+#         tokenizer.src_lang = "en"
+#         max_length = 500
+#         input_chunks = [google_translation[i:i+max_length] for i in range(0, len(google_translation), max_length)]
+#         refined_chunks = []
+#         for chunk in input_chunks:
+#             inputs = tokenizer(chunk, return_tensors="pt", truncation=True, max_length=1024).to(model.device)
+#             translated_tokens = model.generate(
+#                 **inputs,
+#                 max_length=1024,
+#                 no_repeat_ngram_size=2,
+#                 forced_bos_token_id=tokenizer.get_lang_id("ur"),
+#                 num_beams=2
+#             )
+#             refined_chunks.append(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0])
+#         return " ".join(refined_chunks)
+#     except Exception as e:
+#         return f"❌ Error in translation: {str(e)}"
+
+
+def translate_to_urdu(english_script):
+    try:
+        # ✅ Define a max chunk size (Google Translator has a limit)
+        max_chunk_size = 5000
+        chunks = [english_script[i:i + max_chunk_size] for i in range(0, len(english_script), max_chunk_size)]
+
+        # ✅ Translate each chunk separately
+        translated_chunks = [GoogleTranslator(source='en', target='ur').translate(chunk) for chunk in chunks]
+
+        return " ".join(translated_chunks)  # ✅ Join all translated chunks
+    except Exception as e:
+        return f"❌ Error in translation: {str(e)}"
+
+
+def save_english_file(content, topic):
+    today = datetime.today().strftime('%Y_%b_%d')  # Format: 2025_Feb_21
+    filename = f"English_Scripts/{topic}_Eng_{today}.txt"
+    os.makedirs(os.path.dirname(filename), exist_ok=True)  # Ensure directory exists
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(content)
+    return filename
+
+
+def save_urdu_file(content, topic):
+    today = datetime.today().strftime('%Y_%b_%d')
+    filename = f"Urdu_Scripts/{topic}_Urdu_{today}.txt"
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(content)
+    return filename
+
+
+def save_final_urdu_file(topic, content):
+    date_str = datetime.now().strftime("%Y_%b_%d")
+    filename = f"Urdu_Final/{topic}_Urdu_Final_{date_str}.txt"  # ✅ Corrected file path
+    os.makedirs(os.path.dirname(filename), exist_ok=True)  # ✅ Ensure the directory exists
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(content)
+    return filename
+
+
+def finalize_process():
+    return "✅ Script Generation Completed Successfully!"
+
+
+
+
+# ✅ Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("# 🎬 AI-Powered Educational Script Generator")
+
+    topic_input = gr.Textbox(label="Enter Topic")
+    duration_input = gr.Slider(minimum=1, maximum=30, step=1, label="Duration (minutes)")
+
+
+    generate_button = gr.Button("Generate English Script")
+    eng_output = gr.Textbox(label="Generated English Script", interactive=False)
+    download_english_button = gr.Button("Download English Script")
+    download_english_button.click(save_english_file, inputs=[eng_output, topic_input], outputs=[gr.File()])
+
+
+    # ✅ Keyword Extraction Section
+    extract_keywords_btn = gr.Button("🔑 Extract Keywords")
+    keyword_output = gr.Textbox(label="🔍 Extracted Keywords", interactive=True)
+    download_keywords_btn = gr.Button("⬇️ Download Keywords")
+    download_keywords_btn.click(save_keywords_file, inputs=[keyword_output], outputs=[gr.File()])
+
+    translate_button = gr.Button("Generate Urdu Script")
+    urdu_output = gr.Textbox(label="Translated Urdu Script", interactive=False, rtl=True)
+    download_urdu_button = gr.Button("Download Urdu Script")
+    download_urdu_button.click(save_urdu_file, inputs=[urdu_output, topic_input], outputs=[gr.File()])
+
+    
+    final_edited_urdu_output = gr.Textbox(label="Edited Urdu Script", interactive=True, rtl=True)
+    download_final_urdu_button = gr.Button("Download Final Urdu Script")
+    download_final_urdu_button.click(save_final_urdu_file, inputs=[topic_input, final_edited_urdu_output], outputs=[gr.File()])
+
+
+    # ✅ Button Actions
+    generate_button.click(generate_script, inputs=[topic_input, duration_input], outputs=[eng_output])
+    extract_keywords_btn.click(extract_keywords, inputs=[eng_output], outputs=[keyword_output])
+    translate_button.click(translate_to_urdu, inputs=[eng_output], outputs=[urdu_output])
+
+    status_output = gr.Textbox(label="Status", interactive=False)
+    finalize_button = gr.Button("Finalize Process")
+    finalize_button.click(finalize_process, outputs=[status_output])
+
+
+app.launch()