Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import fitz  # PyMuPDF for PDF handling
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Function to handle file upload and text input
+def analyze_document(file, prompt):
+    # Check file type and extract text accordingly
+    if file.name.endswith(".pdf"):
+        text = extract_text_from_pdf(file.name)
+    elif file.name.endswith(".txt"):
+        text = file.read().decode("utf-8")
+    else:
+        return "Unsupported file format. Please upload a PDF or TXT file."
+    
+    # Load model and tokenizer
+    model_name = "Alibaba-NLP/gte-Qwen1.5-7B-instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    
+    # Generate input for the model
+    input_text = f"Document content:\n{text}\n\nPrompt:\n{prompt}"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=analyze_document,
+    inputs=[
+        gr.inputs.File(label="Upload TXT or PDF Document"),
+        gr.inputs.Textbox(label="Prompt", placeholder="Enter your structured prompt here")
+    ],
+    outputs="text",
+    title="Document Analysis with GPT Model",
+    description="Upload a TXT or PDF document and enter a prompt to get an analysis."
+)
+
+# Launch the interface
+iface.launch()