Create app.py

app.py

@@ -0,0 +1,58 @@
+import fitz  # PyMuPDF
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+def extract_text_from_pdf(file_path):
+    doc = fitz.open(file_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+def analyze_document(file, prompt):
+    # Determine file type and extract text
+    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 type. Please upload a PDF or TXT file."
+
+    # Load the model and tokenizer
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        "THUDM/glm-4v-9b",
+        torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True
+    ).to(device).eval()
+
+    # Prepare inputs
+    inputs = tokenizer.apply_chat_template([{"role": "user", "content": text + "\n\n" + prompt}],
+                                           add_generation_prompt=True, tokenize=True, return_tensors="pt",
+                                           return_dict=True)
+    inputs = inputs.to(device)
+
+    # Generate response
+    gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
+    with torch.no_grad():
+        outputs = model.generate(**inputs, **gen_kwargs)
+        outputs = outputs[:, inputs['input_ids'].shape[1]:]
+        return tokenizer.decode(outputs[0])
+
+# Gradio interface
+iface = gr.Interface(
+    fn=analyze_document,
+    inputs=[
+        gr.inputs.File(label="Upload PDF or TXT Document"),
+        gr.inputs.Textbox(label="Enter your prompt")
+    ],
+    outputs="text",
+    title="Medical Report Analyzer",
+    description="Upload a medical report (PDF or TXT) and enter a prompt to analyze the report."
+)
+
+if __name__ == "__main__":
+    iface.launch()