Update app.py

app.py

@@ -1,137 +1,76 @@
-    
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from PyPDF2 import PdfReader
-import docx
-
-# Load multilingual model
-model_name = "csebuetnlp/mT5_multilingual_XLSum"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Languages supported by mT5_multilingual_XLSum
-supported_languages = [
-    "english", "hindi", "telugu", "french", "german", "spanish", "bengali", "tamil", "marathi", "urdu"
-    # (Add more as needed from the model's card)
-]
-
-def read_pdf(file):
-    reader = PdfReader(file)
-    return "".join([page.extract_text() for page in reader.pages])
-
-def read_docx(file):
-    doc = docx.Document(file)
-    return "\n".join([para.text for para in doc.paragraphs])
-
-def summarize_file(file, language):
-    if file.name.endswith(".pdf"):
-        text = read_pdf(file)
-    elif file.name.endswith(".docx"):
-        text = read_docx(file)
-    elif file.name.endswith(".txt"):
-        text = file.read().decode("utf-8")
-    else:
-        return "Unsupported file format."
-
-    if not text.strip():
-        return "The file is empty or unreadable."
-
-    text = text[:3000]
-
-    # Prepare input as per mT5 format
-    prefix = f"summarize {language}: "
-    inputs = tokenizer(prefix + text, return_tensors="pt", max_length=1024, truncation=True)
-    summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-    return summary
-
-iface = gr.Interface(
-    fn=summarize_file,
-    inputs=[
-        gr.File(file_types=[".pdf", ".docx", ".txt"]),
-        gr.Dropdown(supported_languages, label="Select Language", value="english")
-    ],
-    outputs="text",
-    title="Multilingual AI Document Summarizer",
-    description="Upload a document and get summaries in multiple languages using mT5."
-)
-
-if __name__ == "__main__":
-    iface.launch(share=True)'''
-
-
-import gradio as gr
-from PyPDF2 import PdfReader
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-# Load summarizer model (LaMini-Flan-T5)
-summarizer_tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
-summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
-
-# Load translators
-translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
-translator_te = pipeline("translation", model="Helsinki-NLP/opus-mt-en-mul")
-
-# Extract text from PDF
-def extract_text_from_pdf(file):
-    reader = PdfReader(file)
-    text = ""
-    for page in reader.pages:
-        text += page.extract_text()
-    return text
-
-# Summarize based on doc type
-def summarize_text(text, doc_type):
-    prompt = f"Summarize this {doc_type} document clearly:\n{text}\nSummary:"
-    inputs = summarizer_tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
-    outputs = summarizer_model.generate(**inputs, max_length=300, num_beams=4, early_stopping=True)
-    return summarizer_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Translate summary
-def translate_summary(summary, lang):
-    if lang == "hindi":
-        return translator_hi(summary)[0]["translation_text"]
-    elif lang == "telugu":
-        return translator_te(summary)[0]["translation_text"]
-    else:
-        return summary  # English or unsupported
-
-# Main processing logic
-def process(file, lang, doc_type):
-    text = extract_text_from_pdf(file)
-    if not text.strip():
-        return "Error: PDF has no extractable text."
-    
-    summary = summarize_text(text, doc_type)
-    return translate_summary(summary, lang)
-
-# Gradio UI
-with gr.Blocks() as app:
-    gr.Markdown("## Multilingual AI Document Summarizer")
-    gr.Markdown("Upload a document and get summaries in multiple languages using mT5.")
-
-    file_input = gr.File(label="Upload PDF")
-
-    with gr.Row():
-        language_input = gr.Dropdown(
-            label="Select Language",
-            choices=["english", "hindi", "telugu"],
-            value="english"
-        )
-        type_input = gr.Dropdown(
-            label="Select Document Type",
-            choices=["legal", "medical", "general"],
-            value="general"
-        )
-
-    output = gr.Textbox(label="Summary Output", lines=10)
-
-    with gr.Row():
-        clear = gr.Button("Clear")
-        submit = gr.Button("Submit")
-
-    submit.click(fn=process, inputs=[file_input, language_input, type_input], outputs=output)
-    clear.click(lambda: "", inputs=[], outputs=output)
-
-app.launch()
+    
+
+import gradio as gr
+from PyPDF2 import PdfReader
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Load summarizer model (LaMini-Flan-T5)
+summarizer_tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+
+# Load translators
+translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
+translator_te = pipeline("translation", model="Helsinki-NLP/opus-mt-en-mul")
+
+# Extract text from PDF
+def extract_text_from_pdf(file):
+    reader = PdfReader(file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Summarize based on doc type
+def summarize_text(text, doc_type):
+    prompt = f"Summarize this {doc_type} document clearly:\n{text}\nSummary:"
+    inputs = summarizer_tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
+    outputs = summarizer_model.generate(**inputs, max_length=300, num_beams=4, early_stopping=True)
+    return summarizer_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Translate summary
+def translate_summary(summary, lang):
+    if lang == "hindi":
+        return translator_hi(summary)[0]["translation_text"]
+    elif lang == "telugu":
+        return translator_te(summary)[0]["translation_text"]
+    else:
+        return summary  # English or unsupported
+
+# Main processing logic
+def process(file, lang, doc_type):
+    text = extract_text_from_pdf(file)
+    if not text.strip():
+        return "Error: PDF has no extractable text."
+    
+    summary = summarize_text(text, doc_type)
+    return translate_summary(summary, lang)
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("## Multilingual AI Document Summarizer")
+    gr.Markdown("Upload a document and get summaries in multiple languages using mT5.")
+
+    file_input = gr.File(label="Upload PDF")
+
+    with gr.Row():
+        language_input = gr.Dropdown(
+            label="Select Language",
+            choices=["english", "hindi", "telugu"],
+            value="english"
+        )
+        type_input = gr.Dropdown(
+            label="Select Document Type",
+            choices=["legal", "medical", "general"],
+            value="general"
+        )
+
+    output = gr.Textbox(label="Summary Output", lines=10)
+
+    with gr.Row():
+        clear = gr.Button("Clear")
+        submit = gr.Button("Submit")
+
+    submit.click(fn=process, inputs=[file_input, language_input, type_input], outputs=output)
+    clear.click(lambda: "", inputs=[], outputs=output)
+
+app.launch()