Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load summarization and image captioning models
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+image_captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+
+def analyze_input(file, question=None):
+    if file is None:
+        return "Please upload a document or image."
+
+    filename = file.name.lower()
+
+    if filename.endswith((".png", ".jpg", ".jpeg")):
+        image = Image.open(file)
+        caption = image_captioner(image)[0]['generated_text']
+        return f"📷 Image Interpretation:\n{caption}"
+
+    elif filename.endswith((".pdf", ".docx", ".pptx", ".xlsx")):
+        from PyPDF2 import PdfReader
+        import docx
+        import pptx
+        import pandas as pd
+
+        try:
+            text = ""
+            if filename.endswith(".pdf"):
+                reader = PdfReader(file)
+                text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
+
+            elif filename.endswith(".docx"):
+                doc = docx.Document(file)
+                text = "\n".join([p.text for p in doc.paragraphs if p.text.strip()])
+
+            elif filename.endswith(".pptx"):
+                prs = pptx.Presentation(file)
+                for slide in prs.slides:
+                    for shape in slide.shapes:
+                        if hasattr(shape, "text"):
+                            text += shape.text + "\n"
+
+            elif filename.endswith(".xlsx"):
+                df = pd.read_excel(file, sheet_name=None)
+                text = "\n".join([df[sheet].to_string() for sheet in df])
+
+            if not text.strip():
+                return "Could not extract meaningful text from the document."
+
+            summary = summarizer(text[:3000], max_length=200, min_length=30, do_sample=False)
+            return f"📄 Document Summary:\n{summary[0]['summary_text']}"
+
+        except Exception as e:
+            return f"❌ Error processing document: {str(e)}"
+
+    else:
+        return "Unsupported file type. Please upload a valid image or document."
+
+iface = gr.Interface(
+    fn=analyze_input,
+    inputs=gr.File(label="Upload Document or Image"),
+    outputs=gr.Textbox(label="Result", lines=10),
+    title="Document & Image Analysis Web Service",
+    description="Upload a document (PDF, DOCX, PPTX, XLSX) to get a summary or an image to get a caption. Runs fully on CPU."
+)
+
+demo = gr.TabbedInterface(iface,"Docs and Images")
+app = gr.mount_gradio_app(app, demo, path="/")
+
+@app.get("/")
+def home():
+    return RedirectResponse(url="/")